bluuebunny/crossref_metadata_2025 huggingface dataset:
Friedel Oscillations and He-He Interactions in Mo
Authors: Shen Xuepeng , Liang Enzhi , Zhan Qian , Wang Wei , Geng Wen Tong
Published: September 2024

Helium ions implanted into metals can form ordered bubbles that are isomorphic to the host lattice. While long-range elastic interactions are generally believed to drive bubble superlattice formation, the interactions between individual helium solutes are not yet fully understood. Our first-principles calculations reveal that in molybdenum, Friedel oscillations induced by individual helium atoms generate potential barriers and wells that influence helium pairing and clustering at short He-He distances. These repulsive and attractive interactions at high concentrations provide thermodynamic driving forces that align randomly distributed helium atoms into Mo-He superlattices. Friedel oscillations may have broad impacts on solute–solute interactions in alloys.
Three-Dimensional and Two-Dimensional Green Tensors of Piezoelectric Quasicrystals
Authors: Lazar Markus , Agiasofitou Eleni
Published: September 2024

In this work, within the framework of the linear piezoelectricity theory of quasicrystals, the three-dimensional and two-dimensional Green tensors for arbitrary piezoelectric quasicrystals are derived. In the piezoelectricity of quasicrystals, where phonon, phason and electric fields exist, we introduce the corresponding multifields by developing a hyperspace notation for piezoelectric quasicrystals. Using Fourier transform and the multifield formalism, the three-dimensional Green tensor for piezoelectric quasicrystals as well as its spatial gradient necessary for applications, are derived. The solutions for the “displacement”, “distortion” and “stress” multifields in the presence of a “force” multifield in a piezoelectric quasicrystal as well as the solution of the generalised Kelvin problem, are given. In addition, the two-dimensional Green tensors of piezoelectric quasicrystals as well as of quasicrystals, are determined.
Assessment of Nurses’ Knowledge of the Glasgow Coma Scale in a Saudi Tertiary Care Hospital: A Cross-Sectional Study
Authors: Alsharif Roaa , Abo Al-Azayem Salsabil , Alsomali Nimah , Alsaeed Wjoud , Alshammari Nawal , Alwatban Abdulaziz , Alrabae Yaseen , Orfali Razan , Alqarni Faisal , Alrasheedi Ahmad
Published: September 2024

The Glasgow Coma Scale (GCS) is essential for assessing traumatic brain injury and predicting patient outcomes, yet studies indicate that nurses often have only a basic understanding of the GCS. In Saudi Arabia, research on this topic is limited, suggesting a need for improvement in nurses’ GCS knowledge. This study aimed to evaluate the knowledge and proficiency of 199 staff nurses at King Fahd Medical City in Riyadh, Saudi Arabia, regarding GCS usage and to identify the factors impacting their competence. A descriptive, cross-sectional survey was conducted, and the data were analyzed using SPSS version 23.0. The results showed that 81.4% of nurses had an average level of GCS knowledge, with a mean score of 8.8 ± 1.826. Only 13.6% demonstrated good knowledge, while 5% had poor knowledge. A significant correlation was found between GCS knowledge and nurses’ departments (χ2(2) = 19.184, p < 0.001). The study concludes that GCS knowledge among nurses in this Saudi Arabian center is moderate, highlighting the need for continuous education programs to enhance their competence in GCS assessment.
Spatial Identification and Conservation Gaps of Wilderness Areas in the State-Owned Forest Region of Daxing’anling
Authors: Wang Qingyi , Fu Aihua , Yan Cuicui , Hou Peng , Luan Xiaofeng
Published: September 2024

Wilderness areas are important in maintaining biodiversity, and “retaining existing intact and wilderness areas” is listed as the first of 21 action targets for 2030 in the first draft of the post-2020 Global Biodiversity Framework. However, the quantity and quality of regional-scale wilderness mapping and identification of priority areas for conservation are still insufficient. Here, an approach integrating Boolean and Multi-Criteria Evaluation is used to conduct wilderness mapping, and priority conservation areas are identified based on ecosystem service values and the current status of wilderness protection in protected areas in the state-owned forest region of Daxing’anling. It is found that the wilderness areas account for 96.72% of the total area of the study area, of which high-quality wilderness areas constitute for 50.34% of the total area of wilderness areas, and the quality of wilderness is greatly affected by the railroads. Secondly, wilderness areas have higher ecosystem service values per square kilometer than non-wilderness areas, except for food supply service. In addition, 72.26% of the wilderness areas are not protected, and 52.26% of these high-quality wilderness areas with high ecosystem service values still have conservation gaps. This study can provide data support and scientific reference for future wilderness conservation works in different regions.
Assessment of the Trabecular Bone Microstructure Surrounding Impacted Maxillary Canines Using Fractal Analysis on Cone-Beam Computed Tomography Images
Authors: Sunal Akturk Ezgi , Toktas Ahsen Irem , Can Erkay , Kosen Ezgi , Sarica Irfan
Published: September 2024

Objectives: To assess the impact of the presence or position (buccal/palatal) of impacted canines on trabecular bone density using fractal analysis (FA) on cone-beam computed tomography (CBCT) images, and to compare the results with a control group without impacted canines. Methods: This retrospective study included 41 patients with unilateral impacted canines (30 palatal, 11 buccal) and a control group of 39 patients who underwent surgically assisted rapid maxillary expansion. All patients had CBCT images recorded for diagnostic and treatment purposes. Cross-sectional CBCT images were obtained between the first and second premolars on both sides of the patients’ maxilla. From these images, fractal dimension (FD) was measured in a 20 × 20 pixel region of interest in the trabecular bone using the ImageJ software. Results: The FD values were significantly higher on the impacted side in the impacted canine group (p = 0.02). Within the impacted canine group, a significant increase in FD was observed on the impacted side in the buccal-impacted subgroup (p = 0.02), while no significant difference was observed in the palatal-impacted subgroup (p > 0.05). Conclusions: According to the results of our study, there is an association between the position of the impacted canine and trabecular bone density. An increased trabecular bone density may play a role in the etiology of buccally impacted canines. Clinicians should consider anchorage planning, and appropriate force level, during the forced eruption of buccally impacted canines with high surrounding bone density, to minimize undesirable movements and achieve optimal treatment outcomes.
Interception of a Single Intruding Unmanned Aerial Vehicle by Multiple Missiles Using the Novel EA-MADDPG Training Algorithm
Authors: Cai He , Li Xingsheng , Zhang Yibo , Gao Huanli
Published: September 2024

This paper proposes an improved multi-agent deep deterministic policy gradient algorithm called the equal-reward and action-enhanced multi-agent deep deterministic policy gradient (EA-MADDPG) algorithm to solve the guidance problem of multiple missiles cooperating to intercept a single intruding UAV in three-dimensional space. The key innovations of EA-MADDPG include the implementation of the action filter with additional reward functions, optimal replay buffer, and equal reward setting. The additional reward functions and the action filter are set to enhance the exploration performance of the missiles during training. The optimal replay buffer and the equal reward setting are implemented to improve the utilization efficiency of exploration experiences obtained through the action filter. In order to prevent over-learning from certain experiences, a special storage mechanism is established, where experiences obtained through the action filter are stored only in the optimal replay buffer, while normal experiences are stored in both the optimal replay buffer and normal replay buffer. Meanwhile, we gradually reduce the selection probability of the action filter and the sampling ratio of the optimal replay buffer. Finally, comparative experiments show that the algorithm enhances the agents’ exploration capabilities, allowing them to learn policies more quickly and stably, which enables multiple missiles to complete the interception task more rapidly and with a higher success rate.
Estimating the Effects of Credit Constraints on Productivity of Peruvian Agriculture
Authors: Woutersen Tiemen , Hauck Katherine , Khandker Shahidur R.
Published: September 2024

This paper proposes an estimator for the endogenous switching regression models with fixed effects. The decision to switch from one regime to the other may depend on unobserved factors, which would cause the state, such as being credit constrained, to be endogenous. Our estimator allows for this endogenous selection and for conditional heteroscedasticity in the outcome equation. Applying our estimator to a dataset on the productivity in agriculture substantially changes the conclusions compared to earlier analysis of the same dataset. Intuitively, the reason that our estimate of the impact of switching between states is smaller than previously estimated is that we captured the selection issue: switching between being credit constrained and credit unconstrained may be endogenous to farm production. In particular, we find that being credit constant has the substantial effect of reducing yield by 11%, but not the previously estimated very dramatic effect of reducing yield by 26%.
Validation of the University Student Engagement Questionnaire in the Field of Education
Authors: Cebrián Cifuentes Sara , Guerrero Valverde Empar
Published: September 2024

In this study, the Student Engagement Questionnaire (SEQ) developed by Kember and Leung (2009), was validated using a Spanish sample. This instrument is intended to provide a comprehensive evaluation of teaching and learning processes within university contexts, offering feedback to educators and institutions to support the improvement of these processes. To achieve this, the SEQ was administered to a sample of 561 university students in the field of education, drawn from two universities in Valencia: the Universidad de Valencia (32%), a public institution, and the Universidad Católica de Valencia (68%), a private one, during the start of the first quarter. The questionnaire is designed to assess both students’ competencies and teachers’ abilities to foster a conducive learning environment. Across its various versions, the SEQ has demonstrated considerable stability in its dimensions and the relationships between the variables it assesses. Confirmatory factor analysis has validated the proposed structure in the Spanish population. As a result, the SEQ serves as a suitable instrument for diagnosing and evaluating the development of university students’ competencies, as well as teachers’ capacity to structure teaching and assessment in a way that promotes a constructive and enriching learning environment. Furthermore, it assists in identifying areas for improvement in the methodological strategies employed, enabling educators to refine their pedagogical approaches to maximise the impact on student learning and development.
Comparison of Procedural Content Item Generator versus Interactive Tool for Clinical Skills Acquisition in Physiotherapy Students
Authors: Barranco-i-Reixachs David , Bravo Cristina , Fernández-Lago Helena , Martínez-Soldevila Jordi , Martínez-Navarro Oriol , Masbernat-Almenara Maria , Rubí-Carnacea Francesc
Published: September 2024

Case-based learning (CBL) is an educational method widely used in health professional training, emphasizing theoretical knowledge’s practical application. However, traditional CBL has many challenges related to the complexity and accessibility of case scenarios and its demands on educators to effectively facilitate and evaluate student discussions. Despite its popularity and proven benefits, the comparative effectiveness and efficiency of CBL against newer educational technologies still need to be explored. In this quasi-experimental study, third-year physiotherapy students at the University of Lleida used a computer program for procedural content generation (PCG) and interactive clinical cases (ICs), and assessed them using the Spanish physiotherapy self-efficacy scale and the System Usability Scale, and a satisfaction scale. The study showed a significant improvement in self-efficacy among participants after using PCG and ICs. The usability of the PCG was moderate, while the ICs scored higher and had greater user satisfaction. Satisfaction metrics revealed a strong preference among students for incorporating clinical cases into other subjects, with higher satisfaction levels observed in the IC group compared to the PCG group. The study demonstrated that PCG and ICs significantly enhance clinical skills acquisition in physiotherapy education by improving student performance and engagement.
Active Learning in the Extraction of Organic Compounds: A Study of Undergraduate Chemistry Students
Authors: Jakubčinová Jana , Feszterová Melánia , Silliková Veronika
Published: September 2024

This study investigates the impact of active learning on the acquisition of competencies and learning outcomes in the context of organic chemistry education. Specifically, this study focuses on the implementation of active learning in the extraction of an unknown mixture of organic compounds using acidic and basic solutions. This research is based on an “ex post facto” study involving 40 first-year undergraduate students who are pre-service chemistry teachers at a Slovak public university. This study aims to analyse students’ performance, identify common problems encountered, and assess the advantages and disadvantages of the active learning approach. The data collection instruments included a structured report on best practices in university education and a questionnaire to evaluate the experiences and assessment systems used. This study compares the effectiveness of online and face-to-face teaching methods for practical chemistry coursework. The key findings from the comparison of these methods are the differences in learning outcomes achieved, e.g., answers to tasks 2–6 from the questionnaire. Group B respondents had a higher number of correct responses and lower variability compared to Group A respondents. This difference may indicate an improvement in comprehension and effectiveness of instruction over the period. Differences in scores between the groups may be due to random variability in the composition of the groups, which we found through statistical analysis. Full-time students felt more engaged and more satisfied. More than half of the students said that they preferred face-to-face interactions to help them better understand the material. While online instruction provided greater flexibility and accessibility, students felt that they lacked hands-on interaction, which negatively impacted their acquisition of practical skills. The results indicate that active learning, particularly hands-on laboratory exercises, had a positive impact on the acquisition of professional competencies and students’ learning outcomes. This study also highlights the advantages of active learning in practical chemistry education.
Stability of Local Trajectory Planning for Level-2+ Semi-Autonomous Driving without Absolute Localization
Authors: Zhu Sheng , Wang Jiawei , Yang Yu , Aksun-Guvenc Bilin
Published: September 2024

Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for Level-2+ (L2+) semi-autonomous vehicles without the dependence on accurate absolute localization. Instead, emphasis is placed on estimating the pose change between consecutive planning timesteps from motion sensors and on integrating the relative locations of traffic objects into the local planning problem within the ego vehicle’s local coordinate system, thereby eliminating the need for absolute localization. Without the availability of absolute localization for correction, the measurement errors of speed and yaw rate greatly affect the estimation accuracy of the relative pose change between timesteps. This paper proved that the stability of the continuous planning problem under such motion sensor errors can be guaranteed at certain defined conditions. This was achieved by formulating it as a Lyapunov-stability analysis problem. Moreover, a simulation pipeline was developed to further validate the proposed local planning method, which features adjustable driving environment with multiple lanes and dynamic traffic objects to replicate real-world conditions. Simulations were conducted at two traffic scenes with different sensor error settings for speed and yaw rate measurements. The results substantiate the proposed framework’s functionality even under relatively inferior sensor errors distributions, i.e., speed error verr∼N(−0.1,0.1) m/s and yaw rate error θ˙err∼N(0.57,1.72) deg/s. Experiments were also conducted to evaluate the stability limits of the planned results under abnormally larger motion sensor errors. The results provide a good match to the previous theoretical analysis. Our findings suggested that precise absolute localization may not be the sole path to achieving reliable trajectory planning, eliminating the necessity for high-accuracy dual-antenna Global Positioning System (GPS) as well as the pre-built high-fidelity (HD) maps for map-based localization.
Electric Vehicle Charging Load Prediction Based on Weight Fusion Spatial–Temporal Graph Convolutional Network
Authors: Zhang Jun , Cong Huiluan , Zhou Hui , Wang Zhiqiang , Wen Ziyi , Zhang Xian
Published: September 2024

The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel deep learning-based EV charging load prediction framework to assess the impact of EVs on the MES. First, to model the EV traffic flow, a modified weight fusion spatial–temporal graph convolutional network (WSTGCN) is proposed to capture the inherent spatial–temporal characteristics of traffic flow. Specifically, to enhance the WSTGCN performance, the modified residual modules and weight fusion mechanism are integrated into the WSTGCN. Then, based on the predicted traffic flow, an improved queuing theory model is introduced to predict the charging load. In this improved queuing theory model, special consideration is given to subjective EV user behaviors, such as refusing to join queues and leaving impatiently, making the queuing model more realistic. Additionally, it should be noted that the proposed charging load predicting method relies on traffic flow data rather than historical charging data, which successfully addresses the data insufficiency problem of newly established charging stations, thereby offering significant practical value. Experimental results demonstrate that the proposed WSTGCN model exhibits superior accuracy in predicting traffic flow compared to other benchmark models, and the improved queuing theory model further enhances the accuracy of the results.
Predictive Models for Biodiesel Performance and Emission Characteristics in Diesel Engines: A Review
Authors: Ai Wenbo , Cho Haeng Muk
Published: September 2024

With the increasing global demand for renewable energy, biodiesel has become a promising alternative to fossil fuels with significant environmental benefits. This article systematically reviews the latest advances in predictive modeling techniques for estimating the characteristics of biodiesel and its impact on diesel engine performance. Various methods for predicting the key performance of biodiesel and the performance and emissions of diesel engines have been summarized. According to the categories of parameters, research cases in recent years have been listed and discussed separately. This review provides a comprehensive overview and serves as a reference for future research and development of biodiesel.
Cost–Benefit Analysis for Flexibility in Hydrothermal Power Systems
Authors: Cavados Gabriel de Azevedo , Pereira Amaro Olimpio
Published: September 2024

The world is experiencing an energy transition, migrating from fossil fuels to renewables, which are usually intermittent and, therefore, require flexibility to keep the power system reliable. Although system flexibility is a well-known theme of research, the question “What is a fair cost for flexibility?” remains to be answered. The present paper proposes a metric to estimate the value of a flexible resource to a power system. This metric is especially important in hydroelectric systems, where the flexibility of the hydro portfolio is uncertain and subject to seasonal changes and other considerations such as agricultural use. The valuation of a flexible resource by its cost–benefit is necessary since its installation generates operational savings to the entire system. A combined project cost and system overall savings is proposed to assess the net cost of a flexible resource. The net cost of flexibility can be used as a metric to rank flexibility candidates in systems with large amounts of renewable energy. A simplified case study of the Brazilian energy system is presented, and the flexibility solutions are evaluated according to the new metric. Results show how different technologies impact the system overall costs and the importance of analyzing system needs (both short and medium terms) when planning the expansion of flexibility in hydro systems.
Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction
Authors: Mao Zhenghui , Zhou Bijun , Huang Jiaxuan , Liu Dandan , Yang Qiangqiang
Published: September 2024

The power consumption data in buildings can be viewed as a time series, where outliers indicate unreasonable energy usage patterns. Accurately detecting these outliers and improving energy management methods based on the findings can lead to energy savings. To detect outliers, an anomaly detection model based on time-series reconstruction, AF-GS-RandomForest, is proposed. This model comprises two modules: prediction and detection. The prediction module uses the Autoformer algorithm to build an accurate and robust predictive model for unstable nonlinear sequences, and calculates the model residuals based on the prediction results. Points with large residuals are considered outliers, as they significantly differ from the normal pattern. The detection module employs a random forest algorithm optimized by grid search to detect residuals and ultimately identify outliers. The algorithm’s accuracy and robustness were tested on public datasets, and it was applied to a power consumption dataset of an office building. Compared with commonly used algorithms, the proposed algorithm improved precision by 2.2%, recall by 12.1%, and F1 score by 7.7%, outperforming conventional anomaly detection algorithms.
Harnessing Blockchain and IoT for Carbon Credit Exchange to Achieve Pollution Reduction Goals
Authors: Boumaiza Ameni , Maher Kenza
Published: September 2024

The trinity of global warming, climate change, and air pollution casts an ominous shadow over society and the environment. At the heart of these threats lie carbon emissions, whose reduction has become paramount. Blockchain technology and the internet of things (IoT) emerge as innovative tools for establishing an efficient carbon credit exchange. This paper presents a blockchain and IoT-centric platform for carbon credit exchange, paving the way for transparent, secure, and effective trading. IoT devices play a pivotal role in monitoring and verifying carbon emissions, safeguarding the integrity and accountability of the trading process. Blockchain technology, with its decentralized and immutable nature, empowers the platform with transparency, reduced fraud, and enhanced accountability. This platform aims to arm organizations and individuals with the ability to actively curb carbon emissions, fostering collective efforts towards global pollution reduction goals.
Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data
Authors: Rosero Fredy , Rosero Carlos Xavier , Segovia Carlos
Published: September 2024

Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in regions with technical and economic constraints, such as Latin America, the use of PEMS is often limited, highlighting the need for low-cost methodologies to assess vehicle performance. OBD interfaces provide extensive vehicle and engine operational data in this context, offering a valuable alternative for analyzing vehicle performance in real-world conditions. This study proposes a straightforward methodology for assessing vehicle fuel efficiency and carbon dioxide (CO2) emissions under real-world traffic conditions using OBD data. An experimental campaign was conducted with three gasoline-powered passenger vehicles representative of the Ecuadorian fleet, operating as urban taxis in Ibarra, Ecuador. This methodology employs an OBD interface paired with a mobile phone data logging application to capture vehicle kinematics, engine parameters, and fuel consumption. These data were used to develop engine maps and assess vehicle performance using the vehicle-specific power (VSP) approach based on the energy required for vehicle propulsion. Additionally, VSP analysis combined with OBD data facilitated the development of an energy-emission model to characterize fuel consumption and CO2 emissions for the tested vehicles. The results demonstrate that OBD systems effectively monitor vehicle performance in real-world conditions, offering crucial insights for improving urban transportation sustainability. Consequently, OBD data serve as a critical resource for research supporting decarbonization efforts in Latin America.
ADPA Optimization for Real-Time Energy Management Using Deep Learning
Authors: Wan Zhengdong , Huang Yan , Wu Liangzheng , Liu Chengwei
Published: September 2024

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.
Hydrogen: Prospects and Criticalities for Future Development and Analysis of Present EU and National Regulation
Authors: Migliavacca Gianluigi , Carlini Claudio , Domenighini Piergiovanni , Zagano Claudio
Published: September 2024

Hydrogen is in the spotlight in the energy world, and it will remain so. In Europe, the necessity to integrate ever-growing amounts of Renewable Energy Sources (RES) in order to implement the ambitious European decarbonization policy (package Fit-for-55) and to preserve the security of energy supply (package Repower-EU) are feeding the interest in hydrogen. This paper will provide a thorough analysis of prospects and criticalities for the development of hydrogen both as a carrier and as a feedstock and, definitively, as a key element for the implementation of the European decarbonization policies. First, the present regulatory framework will be highlighted, taking the European Union as a main reference, since it presently has one of the most advanced hydrogen legislations in the world. Then, both hydrogen offer (technologies) and demand (both as a feedstock and as an energy carrier) will be dealt with in detail. Two additional sections will take care to illustrate, respectively, the interactions between hydrogen and the electricity grid and the issues related to the creation of a liquid hydrogen market. Finally, a conclusion section will wrap up and summarize the most urgent issues to be tackled to create a well-functioning hydrogen economy.
Genome-Wide Identification, Expression Analysis, and Transcriptome Analysis of the NPF Gene Family under Various Nitrogen Conditions in Eucalyptus grandis
Authors: Li Guangyou , Yang Deming , Hu Yang , Xu Jianmin , Li Juan , Lu Zhaohua
Published: September 2024

The NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER (NRT1/PTR) family (NPF) plays an important role in plant nitrate absorption, distribution, and nitrogen use efficiency. Nevertheless, few reports are available regarding Eucalyptus grandis NPF genes and their expression profiles. This study aims to identify and analyze NPF genes and their expression under various nitrogen (N) conditions. In this study, we successfully screened 64 NPF genes within the E. grandis genome. Subsequently, we conducted an extensive analysis, encompassing investigations into chromosome location, gene structure, phylogenetic relationship, promoter region, conserved motif, and gene expression profile. RNA-seq was conducted to analyze the expression profiles of EgNPF genes under different N conditions. The 64 NPF genes were categorized into eight distinct groups, exhibiting an uneven distribution among the 10 chromosomes of E. grandis, and no member was mapped on chromosome (Chr) 9. The examination of cis-regulatory elements revealed that NPF promoters were closely related to light responsive element, MeJA responsiveness, anaerobic induction, gibberellin responsiveness, low-temperature responsiveness, and auxin responsiveness. We used the comparative transcriptome method to identify the 10 differently expressed EgNPF genes of E. grandis under high-nitrogen (N: 119 mg/L) and low-nitrogen (N: 29.25 mg/L) conditions. Expression pattern analyses revealed that EUGRSUZ_G03119 showed an elevated expression in both leaves and roots under high-nitrogen conditions compared to low-nitrogen conditions, suggesting that EUGRSUZ_G03119 might affect nitrogen transport and redistribution, potentially boosting the stress tolerance of E. grandis in response to nitrogen deficiency. These findings may provide valuable insights into the evolutionary development of the NPF gene family in E. grandis and facilitate the clarification of the molecular mechanism underlying EgNPF-mediated N absorption and distribution in E. grandis.
Coordinated Development of Forests and Society: Insights and Lessons from Natural Forest Restoration and Regional Development in China
Authors: Chen Hui , Tian Gang , Wu Jiaxin , Sun Lilong , Yang Jingyao
Published: September 2024

Mitigating and avoiding social unrest caused by ecological forest restoration is a key factor in the coordinated development of forests and society. Forests, which are intricately linked with society, serve as a vital source of timber, non-timber forest products, and ecosystem services. Ecological forest restoration projects must carefully consider the relationship between forests and society to promote their coordinated development. As a key implementation area for the Natural Forest Resource Protection Project, the state-owned forest regions in Northeast China have experienced a crisis regarding harvestable resources and social unrest caused by protection policies and are currently innovating in policies and practices to mitigate social unrest. This study focuses on the history of natural forest resource conservation projects in China’s state-owned forest areas as a case study for empirical research, aiming to provide insights into ecological restoration models that foster harmony between forests and society. The empirical analyses reveal the following findings: (1) As a result of strict protection, natural forest resources on state-owned land have transitioned from providing timber to ecosystem services and non-timber products. (2) The strict logging ban policy has led to severe resource shortages; from 2000 to 2020, for every 10,000 cubic meter decrease in timber harvest, the per capita output in state-owned forest areas has dropped by more than CNY 500 (approximately USD 70). (3) Proactive ecological restoration can effectively alleviate social unrest; from 2000 to 2020, for every additional 10,000 hectares of forest tending, the average wage increased by more than CNY 900 (approximately USD 127). (4) Regional transformation can effectively generate a buffer effect to mitigate social unrest caused by strict forest protection policies and leverage the beneficial resources produced by ecological forest restoration to develop new drivers of economic growth. By systematically reviewing the comprehensive implementation of the NFPP, this paper’s findings provide insights into ecological restoration strategies that promote the harmonious development of forests and society.
Multifunctional Evaluation of Spruce–Fir Forest Based on Different Thinning Intensities
Authors: Huang Wenjin , Song Boyao , Liu Yang , Liu Jiarong , Wang Xinjie
Published: September 2024

Evaluating the performance of multifunctionality is a necessary foundation for forest multifunctionality management. This study aims to comprehensively adopt multiple methods to construct a multifunctional evaluation system for natural spruce–fir forests and explore the impact of thinning intensity on the multifunctional management effect of spruce–fir. This article combines subjective and objective evaluation methods and selects three methods to construct an evaluation system: the Analytic Hierarchy Process, the combined entropy weight method, and the CRITIC method. The results showed that the consistency of the three evaluation methods is good, and according to the score based on the evaluation results, the multifunctional performance of the sample plot with a thinning intensity of 20% (average score of AHP method is 75.5; EWM is 91; CRITIC is 96.5) is significantly better than that of the sample plot with a thinning intensity of 40% (AHP is 65.3; EWM is 51; CRITIC is 48), both of which were significantly better than those of the untreated sample plot (AHP is 12.7; EWM is 18.7; CRITIC is 17.3). A coupling relationship model between multifunctional values and different functions, as well as a coupling relationship model between different functions and various indicators, were constructed based on the evaluation system. Finally, the forest stand with the highest multifunctional comprehensive value was selected as the reference for the target structure to construct the target structure system, which is convenient for actual management. This study found that there is a nurturing intensity (20%) that can best utilize the multiple functions of forests, which has practical significance for promoting forest multifunctionality in forest management. In addition, this study scientifically constructed and compared several evaluation systems for the multifunctional performance of forests, laying a certain foundation for forest multifunctional evaluation and future forest multifunctional management.
Comparison of Deep Learning Models and Feature Schemes for Detecting Pine Wilt Diseased Trees
Authors: Zhi Junjun , Li Lin , Zhu Hong , Li Zipeng , Wu Mian , Dong Rui , Cao Xinyue , Liu Wangbing , Qu Le’an , Song Xiaoqing , Shi Lei
Published: September 2024

Pine wilt disease (PWD) is a severe forest disease caused by the invasion of pine wood nematode (Bursaphelenchus xylophilus), which has caused significant damage to China’s forestry resources due to its short disease cycle and strong infectious ability. Benefiting from the development of unmanned aerial vehicle (UAV)-based remote sensing technology, the use of UAV images for the detection of PWD-infected trees has become one of the mainstream methods. However, current UAV-based detection studies mostly focus on multispectral and hyperspectral images, and few studies have focused on using red–green–blue (RGB) images for detection. This study used UAV-based RGB images to extract feature information using different color space models and then utilized semantic segmentation techniques in deep learning to detect individual PWD-infected trees. The results showed that: (1) The U-Net model realized the optimal image segmentation and achieved the highest classification accuracy with F1-score, recall, and Intersection over Union (IoU) of 0.9586, 0.9553, and 0.9221, followed by the DeepLabv3+ model and the feature pyramid networks (FPN) model. (2) The RGBHSV feature scheme outperformed both the RGB feature scheme and the hue saturation value (HSV) feature scheme, which were unrelated to the choice of the semantic segmentation techniques. (3) The semantic segmentation techniques in deep-learning models achieved superior model performance compared with traditional machine-learning methods, with the U-Net model obtaining 4.81% higher classification accuracy compared with the random forest model. (4) Compared to traditional semantic segmentation models, the newly proposed segment anything model (SAM) performed poorly in identifying pine wood nematode disease. Its success rate is 0.1533 lower than that of the U-Net model when using the RGB feature scheme and 0.2373 lower when using the HSV feature scheme. The results showed that the U-Net model using the RGBHSV feature scheme performed best in detecting individual PWD-infected trees, indicating that the proposed method using semantic segmentation technique and UAV-based RGB images to detect individual PWD-infected trees is feasible. The proposed method not only provides a cost-effective solution for timely monitoring forest health but also provides a precise means to conduct remote sensing image classification tasks.
Designing the Properties of Probiotic Kefir with Increased Whey Protein Content
Authors: Yiğit Ziolkowski Aslı , Bielska Paulina , Cais-Sokolińska Dorota , Samur Gülhan
Published: September 2024

This research unveiled new insights on the impact of incorporating whey proteins into kefir produced using three different methods. This aims to improve its quality and health benefits, primarily as a result of optimal proliferation of probiotic bacteria. In the initial part of the experiment, samples were prepared using three different methods (methods 1, 2, and 3) to examine the impact of introducing whey protein on bacterial count, the content of L(+)-lactic acid, lactase activity, and the lactic acid and ethanol levels. The methods differed primarily in the sequence of the inoculation milk with probiotic bacteria stage in the production cycle, as well as incubation time and temperature. No significant differences were found in the number of yeasts and bacteria between samples with and without whey proteins. However, it was revealed that the 5% addition of whey proteins enhanced the number of probiotic bacteria in kefir produced with method 2 (from 4.86 to 5.52 log cfu/mL) and method 3 (from 3.68 to 4.01 log cfu/mL). The second part of the research investigated the impact of whey proteins on firmness, consistency, cohesiveness, viscosity, color, and water activity of kefir. This part focused on testing samples with lower whey protein contents (1 and 3%, w/v). We found that the addition of 1% and 3% whey proteins resulted in decreased firmness, consistency, cohesiveness, and viscosity compared to the control kefir. On the other hand, the addition of 5% whey proteins resulted in increased firmness and consistency compared to the addition of 1% and 3% whey proteins. The addition of whey protein decreased the white index WI of the kefir samples. Overall, our results revealed that incorporating whey protein concentrate (WPC) in the production of probiotic kefir can enhance its health benefits while maintaining its rheological properties and overall quality.
Evaluation of Peripheral Milling and Abrasive Water Jet Cutting in CFRP Manufacturing: Analysis of Defects and Surface Quality
Authors: Sambruno Alejandro , Gómez-Parra Álvaro , Márquez Pablo , Tellaeche-Herrera Iñaki , Batista Moisés
Published: September 2024

The use of carbon fiber reinforced polymers (CFRP) is crucial in industries, such as aerospace, automotive, and marine, due to their excellent strength-to-weight ratio and corrosion resistance. However, machining CFRP is challenging due to its abrasive nature, which can cause premature tool wear. Some of the commonly used processes for machining these materials are dry milling and abrasive water jet machining (AWJM), which offer the best alternatives from an environmental point of view. This article presents an analysis of the defects and surface quality obtained in CFRP after machining by AWJM and milling. For this purpose, combinations of relevant parameters have been chosen for each process: cutting speed and tool wear in milling and traverse feed rate and hydraulic pressure in AWJM. The results obtained have been evaluated from two points of view: macroscopically, through the evaluation of delamination, and microscopically, through the study of the roughness in terms of Ra. Furthermore, a discussion on functional, environmental, economic, and social terms has been made between both processes. In summary, each machining process generates a specific type of delamination: Type II in milling and Type I in AWJM. In addition, the best Ra results are obtained for pressures of 1200 bar in AWJM.
Structural Response of Post-Tensioned Slabs Reinforced with Forta-Ferro and Conventional Shear Reinforcement under Impact Load
Authors: Chaaban Sandy , Temsah Yehya , Jahami Ali , Darwiche Mohamad
Published: September 2024

Several researchers have studied how impact loads from impact hazards affect reinforced concrete (RC) slabs. There is relatively little research on impact loading effects on pre-stressed structures. The usage of fibers in structural elements intrigued researchers. In this paper, impact-loaded post-tensioned (PT) slabs with and without Forta-Ferro fibers were compared to post-tensioned slabs with plain concrete and conventional shear reinforcement. Forta-Ferro is a lightweight, low-cost fiber, and hence its effects on slab structural response under impact load deserve to be explored. Post-tensioned slabs’ impact resistance and energy absorption were tested using real-world situations of rapid and severe loads. Four identical 3.3 by 1.5 m concrete slabs were utilized in the experiment. The experiment involved dropping a 600 kg iron ball from 8 m onto each slab’s center of gravity. The slabs’ responses were investigated. The four slab configurations were tested for displacement, energy absorption, and cracking. Forta-Ferro fiber reinforcement is understudied, making this study significant. The study’s findings may help us comprehend fiber-reinforced concrete PT slabs’ impact resistance and structural performance. Engineers and designers of impact-prone buildings like slabs and bridges will benefit from the findings. The study also suggests adding Forta-Ferro fibers to post-tensioned slabs to improve durability and resilience against unanticipated impact hazards.
Co-Encapsulation of Coffee and Coffee By-Product Extracts with Probiotic Kluyveromyces lactis
Authors: Tavares Dérica Gonçalves , Souza Mayara Andrade Martins de , Santos Tamara Leite dos , Silva Adriele do Amor Divino , Abreu Danilo José Machado de , Duarte Whasley Ferreira
Published: September 2024

Coffee and coffee by-products contain several chemical compounds of great relevance, such as chlorogenic acid (CGA), trigonelline, and caffeine. Furthermore, yeasts have been the target of studies for their use as probiotics because of their interesting biochemical characteristics. The combined administration of probiotic microorganisms with components that provide health benefits mediated by alginate encapsulation is an alternative that ensures the stability of cells and chemical compounds. In this context, the aim of this work was to co-encapsulate the probiotic yeast Kluyveromyces lactis B10 and extracts of green coffee beans, coffee silverskin, and PVA (black, green or immature, and sour coffee beans). The bioactive composition, antioxidant and antimicrobial activities of the extracts, microcapsule morphological characteristics and encapsulation efficiency, ability of the encapsulation to protect the yeast cells subjected to gastrointestinal conditions, and antioxidant activity of the microcapsules were evaluated. All the evaluated extracts showed antioxidant activity, of which PVA showed 75.7% and 77.0%, green coffee bean showed 66.4% and 45.7%, and coffee silverskin showed 67.7% and 37.4% inhibition of DPPH and ABTS•+ radicals, respectively, and antimicrobial activity against the pathogenic bacteria E. coli, Salmonella, and S. aureus, with high activity for the PVA extract. The microcapsules presented diameters of between 1451.46 and 1581.12 μm. The encapsulation efficiencies referring to the yeast retention in the microcapsules were 98.05%, 96.51%, and 96.32% for green coffee bean, coffee silverskin, and PVA, respectively. Scanning electron microscopy (SEM) showed that the microcapsules of the three extracts presented small deformations and irregularities on the surface. The K. lactis cells encapsulated in all treatments with the extracts showed viability higher than 8.59 log CFU/mL, as recommended for probiotic food products. The addition of green coffee bean, coffee silverskin, and PVA extracts did not reduce the encapsulation efficiency of the alginate microcapsules, enabling a safe interaction between the extracts and the K. lactis cells.