bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating a University-Wide Digital Skills Programme: Understanding the Student Journey
Authors: Raji Nabila A. S. , Dommett Eleanor J.
Published: November 2024

Digital competencies are critical to success in higher education, and yet these skills are often not explicitly taught to students. We have previously designed and evaluated a university-wide digital skills programme using quantitative methods. In the current study, we aim to better understand the student experience of this programme by conducting semi-structured interviews with those completing the programme. Twelve students were interviewed, and data were thematically analysed to reveal five themes. Firstly, students defined digital competencies in line with tridimensional models but also noted that these competencies were deployed in a goal-directed fashion. Secondly, prior learning was explored, with some students noting they had received training as part of specific qualifications at school but many relying on self and peer-teaching. This fed into the third theme, which related to motivations for training in which students noted the appeal of a comprehensive programme with certification on completion but also a need to address their lack of skills or confidence and maximise their university experience. The fourth theme revealed that the student learning journey through the programme varied considerably. Online learning was perceived as having strengths and weaknesses and whilst the diversity of resources was welcomed, pacing was mixed. Finally, the data demonstrated training was impactful, both in terms of teaching and learning and the wider student experience, allowing students to be more digitally aware and proficient in all areas of digital competency. The findings of the current study indicate that there is value in offering university-wide digital skills training.
Performance Study of FSO/THz Dual-Hop System Based on Cognitive Radio and Energy Harvesting System
Authors: Lu Jingwei , Liu Rongpeng , Wang Yawei , Wang Ziyang , Liu Hongzhan
Published: November 2024

In order to address the problems of low spectrum efficiency in current communication systems and extend the lifetime of energy-constrained relay devices, this paper proposes a novel dual-hop free-space optical (FSO) system that integrates cognitive radio (CR) and energy harvesting (EH). In this system, the source node communicates with two users at the terminal via FSO and terahertz (THz) hard-switching links, as well as a multi-antenna relay for non-orthogonal multiple access (NOMA). There is another link whose relay acts as both the power beacon (PB) in the EH system and the primary network (PN) in the CR system, achieving the double function of auxiliary transmission. In addition, based on the three possible practical working scenarios of the system, three different transmit powers of the relay are distinguished, thus enabling three different working modes of the system. Closed-form expressions are derived for the interruption outage probability per user for these three operating scenarios, considering the Gamma–Gamma distribution for the FSO link, the α−μ distribution for the THz link, and the Rayleigh fading distribution for the radio frequency (RF) link. Finally, the numerical results show that this novel system can be adapted to various real-world scenarios and possesses unique advantages.
A Novel Analysis of the Influence of Zero-Axis Control on Neutral-Point Potential Self-Balancing of Three-Level Converters
Authors: Tang Haiguo , Kong Lingchao , Wang Yong
Published: November 2024

The neutral-point potential balance issues in three-level converters have obtained great attention. The popular view thinks that the neutral-point voltage deviation can be suppressed by regulating the injected zero-sequence component, whether via carrier modulation or space vector modulation techniques. However, this paper presents a novel finding: the efficacy of different frame controllers on the self-balancing of neutral-point potential in three-level converters differs when a comprehensive analysis of zero-sequence dynamics, including neutral-point current and PWM modulation, is conducted. That is, the proportional-resonant (PR) controller in the abc frame effectively introduces a zero-axis PR control of the zero-sequence component, which subsequently degrades the stability of neutral-point potential self-balancing. In contrast, the PI control in the dq frame does not incorporate any additional control of the zero-sequence component, thereby enhancing the self-balancing capability of the neutral-point potential. To substantiate this novel finding, a series of simulations and experimental validations were performed.
TS-GRU: A Stock Gated Recurrent Unit Model Driven via Neuro-Inspired Computation
Authors: Zhang Yuanfang , Fill Heinz D.
Published: November 2024

Existing risk measurement methods often fail to fully consider the impact of climatic conditions on stock market risk, making it difficult to capture dynamic patterns and long-term dependencies. To address these issues, we propose the TS-GRU method: this approach utilizes a temporal convolutional network (TCN) to extract underlying features from historical data, capturing key characteristics of time series data. Subsequently, a gated recurrent unit (GRU) model is employed to capture dynamic patterns and long-term dependencies within the stock market. Finally, the TS-GRU model is optimized using the Sparrow algorithm based on collective behavior, iteratively evaluating and refining model parameters to obtain improved solutions. Experimental results demonstrate the effectiveness of the TS-GRU method in providing accurate risk assessment and forecasting. This comprehensive approach takes into account carbon finance, climate change, and environmental factors, offering valuable insights to investors to help them to understand and manage investment risks in the ever-changing stock market.
Optimization of SM2 Algorithm Based on Polynomial Segmentation and Parallel Computing
Authors: Zhu Hongyu , Li Ding , Sun Yizhen , Chen Qian , Tian Zheng , Song Yubo
Published: November 2024

The SM2 public key cryptographic algorithm is widely utilized for secure communication and data protection due to its strong security and compact key size. However, the intensive large integer operations it requires pose significant computational challenges, which can limit the performance of Internet of Things (IoT) terminal devices. This paper introduces an optimized implementation of the SM2 algorithm specifically designed for IoT contexts. By segmenting large integers as polynomials within a modified Montgomery modular multiplication algorithm, the proposed method enables parallel modular multiplication and reduction, thus addressing storage constraints and reducing computational redundancy. For scalar multiplication, a Co-Z Montgomery ladder algorithm is employed alongside Single Instruction Multiple Data (SIMD) instructions to enhance parallelism, significantly improving efficiency. Experimental results demonstrate that the proposed scheme reduces the computation time for the SM2 algorithm’s digital signature by approximately 20% and enhances data encryption and decryption efficiency by about 15% over existing methods, marking a substantial performance gain for IoT applications.
Research on Non-Intrusive Load Disaggregation Technology Based on VMD–Nyströmformer–BiTCN
Authors: Xu Fengxia , Wang Han , Lu Zhongda , Qiao Jun , Zhang Yongqiang , Heng Hu
Published: November 2024

Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the individual usage of different electrical devices. To address issues in load disaggregation models such as long training times, feature interference caused by the activation of other loads, and accuracy deficiencies caused by behavioral interference from users’ electricity usage habits, this paper proposes a VMD–Nyströmformer–BiTCN network architecture. The variational mode decomposition (VMD) filters the raw power data, reducing errors caused by noise and enhancing the accuracy of decomposing the load. A deep learning network utilizes a modified attention model, Nyströmformer, to reduce feature entanglement and accuracy degradation caused by habitual behavior interference during load disaggregation, while ensuring precise accuracy and improving network operational speed. The training network uses a bidirectional temporal convolutional network (BiTCN) and incorporates a residual network to expand the receptive field, allowing it to receive longer load sequence data and acquire more effective load information, thereby improving the disaggregation effectiveness for target appliances.
An Interdisciplinary Double-Diamond Design Thinking Model for Urban Transport Product Innovation: A Design Framework for Innovation Combining Mixed Methods for Developing the Electric Microvehicle “Leonardo Project”
Authors: Viviani Sara , Gulino Michelangelo-Santo , Rinaldi Alessandra , Vangi Dario
Published: November 2024

The increase in greenhouse gas emissions prompts the transport sector towards new technological perspectives on personal mobility. Addressing sustainable mobility through electric micromobility requires interdisciplinary design research methods and approaches. In the context of the LEONARDO project, funded under the Horizon 2020 framework, this paper addresses a critical literature review on the design thinking, design research models, tools, and mixed methods to be undertaken for driving product mobility innovation in a cross-disciplinary context. Following the “research through design” research strategy, the authors applied the Double-Diamond design thinking model to frame the design research process in four phases, aligning with three overarching objectives, four specific research objectives, and 24 research tasks, supported by a total of 71 mixed methods and tools. As a result, the transdisciplinary process provides a co-designed energy-efficient stand-alone microvehicle and a scalable interdisciplinary design model for urban transport product innovation. In conclusion, this case study suggests the value of the Double-Diamond design thinking model as a design research instrument capable of addressing sustainable mobility and guiding interdisciplinary design research, design practice, and education in the industrial engineering and design disciplinary sectors.
State of Charge Prediction of Mine-Used LiFePO4 Battery Based on PSO-Catboost
Authors: Wang Dazhong , Chang Yinghui , Ji Pengfei , Suo Yanchun , Chen Ning
Published: November 2024

The accurate prediction of battery state of charge (SOC) is one of the critical technologies for the safe operation of a power battery. Aiming at the problem of mine power battery SOC prediction, based on the comparative experiments and analysis of particle swarm optimization (PSO) and Categorical Boosting (Catboost) characteristics, the PSO-Catboost model is proposed to predict the SOC of a power lithium iron phosphate battery. Firstly, the classification model based on Catboost is constructed, and then the particle swarm algorithm is used to optimize the Catboost hyperparameters to build the optimal model. The experiment and comparison show that the optimized model’s prediction accuracy and average precision are superior to other comparative models. Compared with the Catboost model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of the PSO-Catboost model decreased by 12.4% and 25.4% during charging and decreased by 5.5% and 12.2% during discharging. Finally, the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, both ensemble learning models, are selected and compared with PSO-Catboost after being optimized via PSO. The experimental results show that the proposed model has a better performance. In this paper, experiments show that the optimization model can select parameters more intelligently, reduce the error caused by artificial experience to adjust parameters, and have a better theoretical value and practical significance.
Emergency Power Supply Management Strategy for Low-Performance Distribution Networks Based on Mobile Multi-Port Interconnection Equipment
Authors: Ning Xin , Zhu Tianhao , Kang Tong , Zhu Jiran , Liu Keyan , Sun Lijing , You Jinliang , Wang Xin , Guo Qi
Published: November 2024

Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles.
Determination of Plastic Pollutants in Solid Biofuels
Authors: Muzyka Roksana , Werle Sebastian , Sajdak Marcin
Published: November 2024

Many countries widely use biomass for household heating and heat production in district heating systems. Unfortunately, the steady increase in annual plastic waste production has a negative impact on the quality of solid biofuels. This is due to the increasing contamination of these fuels with wastes from plastic and wastes from furniture production, such as laminates and medium-density fiberboard made from wood fibers, among others. The design of specialized biomass combustion systems does not allow for the burning of waste fuel, or the reduction in hazardous organic compounds emitted when burning contaminated biofuels. The study demonstrated the detection of polymeric impurities in solid biofuels through analytical pyrolysis (Py-GC-MS). The study was conducted on model samples that contained increasing proportions of plastic waste, ranging from 0.1 to 10.0% w/w to biomass. Markers were identified and described to indicate contaminated fuel, and the interactions between the sample matrix and plastic were studied. Unique markers were detected that indicate the presence of contamination, even at low concentrations like 0.1% w/w of plastic waste in solid biofuel. These results suggest that direct analytical pyrolysis of solid biofuels, which are already on the market but not covered by the relevant regulatory system and are contaminated with polymeric ingredients, is a method that is not only possible but also gives quick confirmation.
Analysis of Severe Scarcity Situations in Finland’s Low Carbon Electricity System Until 2030
Authors: Koivunen Tero , Syri Sanna
Published: November 2024

This paper presents PLEXOS modelling of the Nordic and Baltic low-carbon electricity market until 2030, using a total of 35 different weather years’ (1982–2016) ERAA profiles as inputs for the modelling and focusing on the occurrence of severe electricity scarcity situations in Finland, analyzing their duration and depth. The expected development of generation and demand is modelled based on available authoritative sources, such as ENTSO-E TYNDP and national projections. The present amount of nuclear power in Finland and growing amounts of wind and solar generation across the Nordic electricity system are modelled. This study analyzes scarcity situations by calculating residual loads and the expected electricity spot market prices assuming the different weather years with the generation fleet and demand in 2024 and 2030 scenarios. This study finds that, despite the very significantly growing amount of variable renewable generation (42.5 TWh/a increase in wind generation from 2024 to 2030 in Finland only), the frequency and severity of scarcity situations will increase from 2024 to 2030. The main reasons are the retirement of Combined Heat and Power plants and the transition to more electrified district heating in Finland and the expected demand growth. The findings indicate that without further measures Finland is not sufficiently prepared for cold winter periods with high heating and electricity demand and events of serious scarcity can occur.
Dynamic Connectedness Among Alternative and Conventional Energy ETFs Based on the TVP-VAR Approach
Authors: Górka Joanna , Kuziak Katarzyna
Published: November 2024

This study investigates risk transmission in the US energy instrument market to determine if certain factors, such as crude oil and natural gas, influence this market and whether stock or energy investment portfolios track their behavior. To investigate volatility spillover, the VAR-based connectedness approach is applied. This approach facilitates the measurement of interdependence across a network of variables, providing insights into aggregate, directional, and net interdependence. The use of the time-varying parameter vector autoregression (TVP-VAR) approach, as developed by Antonakakis and Gabauer, avoids the problems associated with selecting rolling window sizes and the resultant loss of observations during estimations. The analysis revealed a distinction between alternative and traditional ETFs, with lower interdependence observed among the volatility of alternative energy ETFs. While most energy ETFs transmit risk within the systems analyzed, some act as risk receivers, though their net receiving/transmitting character fluctuates. The results of this study are significant for investment portfolio managers.
Electrochemical Mechanism Underlying Lithium Plating in Batteries: Non-Invasive Detection and Mitigation
Authors: Das Sourav , Shrotriya Pranav
Published: November 2024

Efficient, sustainable, safe, and portable energy storage technologies are required to reduce global dependence on fossil fuels. Lithium-ion batteries satisfy the need for reliability, high energy density, and power density in electrical transportation. Despite these advantages, lithium plating, i.e., the accumulation of metallic lithium on the graphite anode surface during rapid charging or at low temperatures, is an insidious failure mechanism that limits battery performance. Lithium plating significantly shortens the battery’s life and rapidly reduces capacity, limiting the widespread adoption of electrical vehicles. When lithium plating is extreme, it can develop lithium dendrites, which may pass through the separator and lead to an internal short circuit and the subsequent thermal runaway damage of the cell. Over the last two decades, a large number of published studies have focused on understanding the mechanisms underlying lithium plating and on approaches to mitigate its harmful effects. Nevertheless, the physics underlying lithium plating still needs to be clarified. There is a lack of real-time techniques to accurately detect and quantify lithium plating. Real-time detection is essential for alleviating lithium plating-induced failure modes. Several strategies have been explored to minimize plating and its effect on battery life and safety, such as electrolyte design, anode structure design, and hybridized charging protocol design. We summarize the current developments and the different reported hypotheses regarding plating mechanisms, the influence of environmental and electrochemical conditions on plating, recent developments in electrochemical detection methods and their potential for real-time detection, and plating mitigation techniques. The advantages and concerns associated with different electrochemical detection and mitigation techniques are also highlighted. Lastly, we discuss outstanding technical issues and possible future research directions to encourage the development of novel ideas and methods to prevent lithium plating.
Mobile Charging Stations: A Comprehensive Review of Converter Topologies and Market Solutions
Authors: Neto Rafael C. , Bandeira Camila M. , Azevedo Gustavo M. S. , Limongi Leonardo R. , de Carvalho Márcio R. S. , Castro José F. C. , Rosas Pedro A. C. , Venerando Augusto C. , Spader Newmar , Bueno Emilio
Published: November 2024

The adoption of electric vehicles (EVs) has shown exponential growth in recent years, with expectations for further development in the years to come. With such significant expansion, efforts and incentives are shifting from EV sales to projects aimed at expanding charging station infrastructure. In order to sustain this growing trend, a reliable and robust charging infrastructure is needed. However, the entire process of planning, designing, and constructing fixed charging stations (FCSs) is time-consuming and expensive. In this scenario, mobile charging stations (MCSs) offer a complementary solution to ensure the necessary reliability for the improvement of EV owners’ experiences in the electrified transportation sector, as they help reduce range anxiety, peak-hour costs, and waiting times. In this sense, this paper aims to disseminate the state-of-the-art research and studies on MCSs, covering topics such as architectures, standards, converter topologies, and market solutions.
Is the Spatiotemporal Evolution of Manufacturing Carbon Emissions in China Toward Convergence?
Authors: You Jianmin , Zhang Wei
Published: November 2024

Understanding the convergence characteristics of manufacturing carbon emissions (MCEs) in China is essential for aligning regional carbon reduction efforts and achieving national climate goals. This study investigates the spatiotemporal evolution and convergence of MCEs across China and its eastern, central, and western regions, using panel data from 30 provinces spanning 2001 to 2020. A spatial panel model is applied to analyze convergence trends and influencing factors. The findings reveal three key insights: (1) Nationwide, the disparity in MCEs is expanding, with significant spatial imbalances; intra-regionally, emission disparities are highest in the eastern region and lowest in the western region. (2) Both nationally and regionally, MCEs lacks a converging trend, complicating coordinated carbon reduction efforts. Less economically developed regions exhibit higher degrees and rates of spatial divergence. (3) Technological advancement and energy structure optimization accelerate spatial divergence, while reduced disparities in manufacturing output and urbanization levels help mitigate it. These results underscore the need for a gradient-based, region-specific approach to achieve carbon peaking and neutrality in China.
A Combined Investment and Operational Optimization Approach for Power-to-Methanol Plants
Authors: Akram Nouman , Kienberger Thomas
Published: November 2024

In the global effort for industrial decarbonization, repurposing closed coal-fired power plants into power-to-methanol (PtM) plants offers a promising pathway to reduce CO2 emissions while leveraging existing infrastructure. This study introduces a novel combined optimization approach using mixed-integer linear programming (MILP) to simultaneously optimize the investment and operation of a PtM plant, assessing its economic viability. The model incorporates the operational flexibility of proton exchange membrane (PEM) electrolyzers in response to fluctuating electricity prices through a piecewise linear representation of its load–efficiency characteristic curve. A case study of a repurposed coal plant in Austria demonstrates the model’s applicability and practical relevance. The results show that larger electrolyzer capacities, i.e., 434 MW, with flexible part-load operation can significantly reduce methanol production costs, i.e., EUR 0.8/kg, achieving competitiveness under high CO2 pricing scenarios, i.e., EUR 500/ton. A sensitivity analysis is performed to identify the critical factors influencing production costs. This study concludes that the combined investment and operational optimization approach effectively captures the essential elements of PtM systems, enabling faster, better, and operation-informed investment decisions for innovative technologies to support the ongoing energy transition. These findings indicate that PtM technologies can be a viable solution for asset repurposing, grid stabilization, and decarbonizing hard-to-abate sectors.
Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads
Authors: Tian Chuan , Jiang Shunli , Li Shuai , Feng Guohui , Yu Bin
Published: November 2024

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.
Grey-Box Energy Modelling of Energy-Efficient House Using Hybrid Optimization Technique of Genetic Algorithms (GA) and Quasi-Newton Algorithms with Markov Chain Monte Carlo Uncertainty Distribution
Authors: Demirezen Gulsun , Fung Alan S. , Brookson Aidan
Published: November 2024

Understanding energy demands and costs is important for policy makers and the energy sector, especially in the context of residential heating and cooling systems. To estimate the thermal demand of a residential house, a grey-box modelling method with a resistance–capacitance (RC) analogy was implemented. The architectural properties used to parameterize the grey-box model were derived from a house used for research purposes in Vaughan, Ontario, Canada (TRCA-House A). The house model accounts for solar irradiance on exterior building surfaces, thermal conductivity through all surfaces, solar heat gains through windows, and thermal gains from ventilation. Two parallel short- and long-term calibrations were performed such that model outputs reflected the real-world operation of the house as best as possible. To define the unknown model parameters (such as the conductivity of building materials and some constant parameters), a hybrid optimization scheme including a genetic algorithm (GA) and the Quasi-Newton algorithm was introduced and implemented using Bayesian approximation and Markov Chain Monte Carlo (MCMC) methods. The temperature outputs from the model were compared to the data retrieved from TRCA-House A. The final iteration of the model had an RMSE for interior zone temperature estimation of 0.22 °C when compared to the retrieved interior zone temperature data from TRCA-House A. Furthermore, the annual heating and cooling energy consumption values are within 1.50% and 0.08% of target values, respectively. According to these preliminary results, the introduced model and optimization techniques could be adjusted for different types of housing, as well as for smart control applications on both a short- and long-term basis.
Interleukin-33 and Obesity-Related Inflammation and Cancer
Authors: Kowitt Cameron , Zhang Qiuyang
Published: November 2024

Interleukin-33 (IL-33) is a cytokine belonging to the IL-1 family. It is primarily associated with type 2 immune responses. It interacts with a receptor complex on immune cells in reaction to tissue damage or cellular injury. IL-33 is crucial in immune responses and is involved in various autoimmune and inflammatory diseases. Obesity is marked by chronic inflammation and is a known risk factor for several types of cancer. Recent studies have shown that IL-33 and its receptor complex are expressed in adipose (fat) tissue, suggesting they may play a role in obesity. While inflammation connects obesity and cancer, it is not yet clear whether IL-33 contributes to cancer associated with obesity. Depending on the cellular context, inflammatory environment, expression levels, and bioactivity, IL-33 can exhibit both protumorigenic and antitumorigenic effects. This review will explore the various functions of IL-33 in the inflammation linked to obesity and its relationship with cancer.
Effect of Incorporating Natural Zeolitic Tuffs in Concrete Mixed and Cured Using Seawater
Authors: Qsymah Ansam , Alqawasmeh Hasan , Ayasrah Mo’men , Al-kharabsheh Buthainah
Published: November 2024

Concrete production has increasingly used seawater to overcome the challenge of freshwater scarcity. Although the use of seawater in concrete still has a controversial reputation, it is a promising application, particularly when combined with mineral admixtures such as natural zeolitic tuffs (ZT). This paper aims to investigate the effect of using locally quarried ZT on the strength of unreinforced concrete mixed and/or cured using seawater. The mix proportions of the concrete were selected to obtain the optimum combination for the M20 grade of concrete with a water-to-cement ratio of 0.69. Moreover, 150mm-cubes and cylinders of 100 mm diameter by 200mm height were cast from the concrete mixtures, which contain 0%, 5%, 7.5%, 10%, and 25% of ZT as a partial replacement of silica sand. Splitting tensile tests and compressive strength tests were conducted on these specimens at 7, 28, and 90 days. The results show the harmful effect of seawater on the strength of plain concrete (without ZT) at 7, 28, and 90 days of curing, especially when seawater is used in both mixing and curing of the concrete. However, adding ZT in seawater-based concrete improved its strength apparently, especially at early curing ages. For example, using 10% of ZT as a partial replacement of silica sand increased the compressive strength of seawater based-concrete by 105.4%, 28.3%, and 34.6% after 7, 28, and 90 days of curing, compared with concrete without ZT and produced using seawater. These results contribute to the enhancement of the sustainability of both freshwater and concrete material through the use of ZT in producing concrete, particularly in areas where freshwater is scarce or expensive.
Advancing Forest Plot Surveys: A Comparative Study of Visual vs. LiDAR SLAM Technologies
Authors: Guan Tianshuo , Shen Yuchen , Wang Yuankai , Zhang Peidong , Wang Rui , Yan Fei
Published: November 2024

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts.
Evaluating Alternative Transformation Pathways of Wood-Based Bioeconomy: Application of an Input–Output Model
Authors: Arnič Domen , Loizou Efstratios , Ščap Špela , Prislan Peter , Juvančič Luka
Published: November 2024

The wood-based bioeconomy is one of the main pillars of sustainable transformation and decarbonisation of the economy, as it promotes the use of renewable resources, reduces reliance on fossil fuels, and supports the development of eco-friendly industries. The paper provides quantitative insight into the potential impacts of transformation of the wood-based bioeconomy in Slovenia, a small, export-oriented economy with ample, but sub-optimally used, forest resources. The analysis uses an input–output (I-O) model to estimate I-O multipliers of sectors representing the wood-based bioeconomy and to capture their direct and indirect impacts on the Slovenian economy. The baseline performance of the wood bioeconomy sectors and their potential to induce economic activity (Scenario A) are estimated with an adjusted I-O model (based on the national I-O table for 2015), in which hybrid sectors are divided into conventional and bio-based segments. Alternative transformation pathways are translated into four additional scenarios. While Scenario B foresees progress in efficiency and integration within the existing structure of transactions, Scenario C (increased energy use of wood biomass) and Scenarios D/D+ (technology- and capital-intensive restructuring) represent two extreme restructuring pathways of the sector. The results of scenario analysis with the I-O model show that the changes in the input requirement structure of the Slovenian wood-based bioeconomy could result in up to a 17% increase in total output, up to a 20% increase in the number of employees, and up to a 16% increase in incomes, all of them attributed to the most optimistic scenario (D+). The results of the study provide quantified assessments underpinning strategic planning for the wood-based bioeconomy, both in the industry and public policy spheres.
Emission of Fire-Promoting Volatiles from Picea omorika (Pančić) Purk Needles in Different Forest Communities
Authors: Vidaković Vera , Popović Zorica
Published: November 2024

The importance of studying the role of volatiles in flammability is well recognized, but the relationship between specific compounds and components of flammability is underestimated. In this study, volatiles emitted from Picea omorika (Pančić) Purk. needles were identified and quantified, and the relationship between volatile emission and moisture content, flammability characteristics and bioclimatic coefficients was investigated. Fresh needles from four different forest communities were analyzed for specific volatile organic compounds (using a gas chromatography-surface acoustic wave analyzer, GC-SAW), moisture content (based on fresh and oven-dried mass), and flammability components (using an epiradiator). Five monoterpenes, one sesquiterpene, and one alcohol were identified and their amounts, moisture content, and flammability components differed between the populations. The amounts of myrcene and β-pinene correlated significantly with moisture content and time to ignition. Myrcene content also correlated with flame duration, site altitude, mean annual temperature, variation in mean annual temperature, and the Ellenberg quotient. The emission of myrcene correlates with ecophysiological, flammability, topographic, and climatic variables. This suggests that myrcene-rich plant species may be of particular interest for research into flammability, especially from the point of view of rapid ignition.
Optimization of Molding Process Parameters of Caragana korshinskii Kom. Based on Box-Behnken Design
Authors: Xu Yuyao , Huang Junyan , Wang Jue , Yu Guosheng , Xu Xiaofeng
Published: November 2024

In this study, using the Box-Behnken design (BBD) experimental method, a plunger-type three-roller pelletizer was employed to explore the optimal pelletizing parameters for biomass fuel pellets with Caragana korshinskii Kom. strip as the raw material. The moisture content of the raw material, the length-to-diameter ratio of the forming die, and the rotational speed of the ring mold were identified as the experimental factors. The relaxation density of the biomass fuel (BMF) pellets and the productivity of the pelletizer were set as the experimental indicators. The study aimed to uncover the influence patterns of these factors on the pelletizing outcomes and establish regression equations between various factors and indicators. The results revealed that when Caragana korshinskii Kom. strip was used as the raw material in this pelletizer, the optimal pelletizing parameters were as follows: a moisture content of 15.5%, a forming die length-to-diameter ratio of 5.3, and a ring mold rotational speed of 30 rpm. Under these conditions, the relaxation density, mechanical durability, and productivity reached 1.139 g/cm3, 96.21%, and 6.278 t/h, respectively. The energy consumption per ton of pellets did not exceed 41.3 kWh. The significance of this study is its potential to expand the utilization range of Caragana korshinskii Kom., reduce environmental pollution at the same time, and make a certain contribution to carbon peak and carbon neutrality.
Forest Management Is Key for Conserving Biodiversity and Providing Ecosystem Services in the United States
Authors: Loehle Craig , Miller Darren A. , Kovach Adrienne I. , Larsen-Gray Angela L. , Akresh Michael E. , McDonald John E. , Cheeseman Amanda E. , King David , Petzinger Sharon M. , Kanter John
Published: November 2024

Forests are valuable for a wide variety of reasons, including biodiversity and carbon sequestration and storage. As such, in the U.S., various parties have proposed large-scale forest management efforts to enhance biodiversity conservation and carbon sequestration. Others, in contrast, argue that forests should not be harvested and have used legal action to prevent timber harvest on public lands. However, given that modern forests in the U.S. are reduced in extent compared to pre-settlement times, are subject to a reduced rate of natural disturbances but experience novel disturbances such as invasive pests and elevated fire risk, and are out of ecological balance due to past human activities, we suggest that active management is not only aligned with forest sustainability but necessary to conserve the maximum feasible range of forest biodiversity. In many areas of the U.S., species most in need of conservation depend on open canopy or early seral forest conditions, both of which can be created or maintained by forest harvest. We suggest that forest management for wood products simultaneously produces these needed conditions, whereas setting aside forests from management only benefits a subset of biodiversity. Although areas not subjected to forest harvest are important landscape components, active management is also needed to restore once-common forest types such as oak (Quercus spp.) woodland, mitigate invasive pests, reduce fire risk, and manage for species that need early seral or disturbed conditions, which are declining on the landscape. We document the current unbalanced conditions and the need for management with a focus on the eastern U.S. to demonstrate the issues.
Enhancing Bird Diversity in Urban Parks: Insights from the Futian Mangrove Ecological Park, Shenzhen
Authors: Li Xiangyi , Li Zhaojun , Chen Yu , Wang Qing
Published: November 2024

Small urban parks and green spaces, serving as essential recreational venues for city residents, also play a vital and irreplaceable role in maintaining urban biodiversity. It is of great importance to design and plan these areas in a way that integrates multiple habitats for various species while accommodating residents’ usage. This study, carried out at the Futian Mangrove Ecological Park located in Shenzhen, Guangdong Province, China, chose birds as indicator species to assess biodiversity within the park. Site inventory was undertaken from May to September 2022 and from October 2022 to April 2023. We quantitatively described the park’s habitats by examining primary environmental factors, along with 3 primary environmental factors and 11 secondary factors. A correlation analysis was then performed between these factors and bird diversity to gain insights into birds’ habitat preferences across different habitat types and at a finer scale of plant communities. Furthermore, bird clusters in the study case were categorized by foraging guilds and foraging patterns, and their distributions were studied at both the habitat patch scale and the plant community scale. Our findings reveal that, at the habitat patch scale, water surface area and grass coverage significantly positively impact bird diversity. At the plant community scale, plant communities with different structural characteristics vary in their importance to bird clusters with distinct characteristics. In areas with high human disturbance, shrub coverage is crucial for bird habitat protection. Additionally, we discovered that the impact of anthropogenic sound differs among bird species, highlighting the complexity of human disturbance factors on bird habitat preferences. Accordingly, we proposed several design recommendations aimed at enhancing bird diversity in parks, including increasing water body areas, reducing the distance between habitats and water surfaces, enhancing herbaceous plant coverage, and controlling anthropogenic sound.
Recent Advances and Challenges in the Production of Hydroxylated Natural Products Using Microorganisms
Authors: Sun Chang , Zeng Rumei , Chen Tianpeng , Yang Yibing , Song Yi , Li Qiang , Cheng Jie , Liu Bingliang
Published: November 2024

Hydroxylation reaction is a significant source of structural diversity in natural products (NPs), playing a crucial role in improving the bioactivity, solubility, and stability of natural product molecules. This review summarizes the latest research progress in the field of natural product hydroxylation, focusing on several key hydroxylases involved in the biosynthesis of NPs, including cytochrome P450 monooxygenases, α-ketoglutarate-dependent hydroxylases, and flavin-dependent monooxygenases. These enzymes achieve selective hydroxylation modification of various NPs, such as terpenoids, flavonoids, and steroids, through different catalytic mechanisms. This review systematically summarizes the recent advances on the hydroxylation of NPs, such as amino acids, steroids, terpenoids, lipids, and phenylpropanoids, demonstrating the potential of synthetic biology strategies in constructing artificial biosynthetic pathways and producing hydroxylated natural product derivatives. Through metabolic engineering, enzyme engineering, genetic engineering, and synthetic biology combined with artificial intelligence-assisted technologies, a series of engineered strains have been successfully constructed for the efficient production of hydroxylated NPs and their derivatives, achieving efficient synthesis of hydroxylated NPs. This has provided new avenues for drug development, functional food, and biomaterial production and has also offered new ideas for the industrial production of these compounds. In the future, integrating artificial synthetic pathway design, enzyme directed evolution, dynamic regulation, and artificial intelligence technology is expected to further expand the application of enzyme-catalyzed hydroxylation reactions in the green synthesis of complex NPs, promoting research on natural product hydroxylation to new heights.
Ease and Limitations in Using Environmental DNA to Track the Spread of Invasive Host–Parasite Complexes: A Case Study of the Freshwater Fish Pseudorasbora parva and the Cryptic Fungal Parasite Sphaerothecum destruens
Authors: Deremarque Théo , Gozlan Rodolphe Elie , Ravaozafindrasoa Ravo , Mucci Giuliano , Delalex Lucie , Foissy Jean-Michel , Cagnant Michaël , Clair Mathieu , Givens Justina , Justy Fabienne , Valentini Alice , Nicolas Delphine , Contournet Pascal , Tetrel Claire , Thibault Marc , Combe Marine
Published: November 2024

The spread of non-native species threatens biodiversity and exacerbates societal challenges like food security. To address this, effective conservation programs require detection methods that are easy to implement, accurate, and non-invasive. Over the past 15 years, environmental DNA (eDNA) techniques have gained popularity, surpassing traditional sampling methods. In this context, our study focused on tracking the invasive host–pathogen complex Pseudorasbora parva and Sphaerothecum destruens using eDNA metabarcoding. We collected water samples from freshwater canals over five months in the Camargue region, and once in Corsica Island, both in southern France. Total DNA was extracted from filtered water samples, and PCR-amplicons were sequenced using Illumina or Nanopore technologies. Our results revealed a high detection rate of P. parva in lentic ecosystems, aligning with habitat preferences of this small freshwater fish. Additionally, the detection rate in Camargue increased in May and June, likely due to the peak of the spawning season, which leads to more DNA being released into the environment (i.e., concentration and interaction of individuals). While eDNA successfully detected this invasive fish, we were unable to detect its cryptic fungal parasite, S. destruens, highlighting the challenges of identifying intracellular and cryptic fungal pathogens through eDNA methods.
Changes in Biochemical Composition and Nutrient Materials in Apocynum pictum Honey During Storage
Authors: Jiang Li , Gong Yanning , Zhao Yu , Dong Wanqing , Guo Leyan , Ju Jiaqi , Su Nana
Published: November 2024

Apocynum pictum (A. pictum) honey is rich in effective ingredients including flavonoids, terpenes, and alkaloids that are beneficial to human health. In this study, widely targeted metabolomics were used to detect the plant-derived secondary metabolites of the same batch of A. pictum honey from 2022 to 2024, in order to explore whether storage time changes the quality of A. pictum honey, especially the content of plant-derived secondary metabolites with important health benefits. The results showed that storage time had no significant effect on the content of sugars, proteins, and other major components in A. pictum honey. At the same time, we also found that although storage time had an impact on the content of some secondary metabolites such as flavonoids in A. pictum honey, the changes in the content of the characteristic active ingredient, hyperoside, in A. pictum honey were not significant. These findings suggest that storage time has a minimal impact on the quality of A. pictum honey. This study provides a theoretical basis for the rational storage of A. pictum honey.