bluuebunny/crossref_metadata_2025 huggingface dataset:
Use or Misuse of Albumin in Critical Ill Patients
Authors: Saner Fuat Hakan , Stueben Bjoern-Ole , Hoyer Dieter , Broering Dieter , Bezinover Dmitri
Published: April 2023

Since 1940 albumin has been used worldwide and is widely available commercially since this time. However, a meta-analysis in 1998 challenged the use of albumin and identified a trend toward higher mortality in critically ill patients who had received albumin. Since then, many studies including multicenter randomized controlled trials have been carried out investigating the safety and efficacy of albumin treatment in different patient cohorts. In this context, patient cohorts that benefit from albumin were identified. However, particularly in non-liver patients, the use of albumin remains controversial. In our comprehensive review, we would like to highlight the most important studies in the recent 20 years and therefore offer an evidence-based outlook for the use of albumin for patients treated in the ICU.
UGV Parking Planning Based on Swarm Optimization and Improved CBS in High-Density Scenarios for Innovative Urban Mobility
Authors: Zeng Dequan , Chen Haotian , Yu Yinquan , Hu Yiming , Deng Zhenwen , Leng Bo , Xiong Lu , Sun Zhipeng
Published: April 2023

The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility.
Uniform Error Estimates of the Finite Element Method for the Navier–Stokes Equations in R2 with L2 Initial Data
Authors: Ren Shuyan , Wang Kun , Feng Xinlong
Published: April 2023

In this paper, we study the finite element method of the Navier–Stokes equations with the initial data belonging to the L2 space for all time t>0. Due to the poor smoothness of the initial data, the solution of the problem is singular, although in the H1-norm, when t∈[0,1). Under the uniqueness condition, by applying the integral technique and the estimates in the negative norm, we deduce the uniform-in-time optimal error bounds for the velocity in H1-norm and the pressure in L2-norm.
Downward-Growing Neural Networks
Authors: Laveglia Vincenzo , Trentin Edmondo
Published: April 2023

A major issue in the application of deep learning is the definition of a proper architecture for the learning machine at hand, in such a way that the model is neither excessively large (which results in overfitting the training data) nor too small (which limits the learning and modeling capabilities of the automatic learner). Facing this issue boosted the development of algorithms for automatically growing and pruning the architectures as part of the learning process. The paper introduces a novel approach to growing the architecture of deep neural networks, called downward-growing neural network (DGNN). The approach can be applied to arbitrary feed-forward deep neural networks. Groups of neurons that negatively affect the performance of the network are selected and grown with the aim of improving the learning and generalization capabilities of the resulting machine. The growing process is realized via replacement of these groups of neurons with sub-networks that are trained relying on ad hoc target propagation techniques. In so doing, the growth process takes place simultaneously in both the depth and width of the DGNN architecture. We assess empirically the effectiveness of the DGNN on several UCI datasets, where the DGNN significantly improves the average accuracy over a range of established deep neural network approaches and over two popular growing algorithms, namely, the AdaNet and the cascade correlation neural network.
Mechanistic Exploration of Dendrite Growth and Inhibition for Lithium Metal Batteries
Authors: Chen Yuanliang , Yuan Xingping , He Cuiping , Gou Qingyi , Yang Ni , Xie Gang , Zhang Keyu , Yao Yaochun , Hou Yanqing
Published: April 2023

Li metal has been considered an ideal anode in lithium batteries due to its high theoretical capacity of 3860 mAh·g−1 and lowest negative reduction potential of −3.040 V among the standard hydrogen electrodes. However, lithium dendrites can easily grow on the surface of the negative electrode during charging, which results in a short circuit of the battery and reduces its efficiency. This paper investigated dendrite growth and inhibition mechanisms in lithium metal batteries to improve battery life. The impacts of the initial nucleation spacing, surface energy anisotropy strength, and interfacial electrochemical driving force on lithium dendrite growth were analyzed with electrochemical experiments and mathematical models. The results showed that the smaller nucleation spacing inhibits the growth of dendrite side branches and reduces the roughness of lithium metal deposition on the negative electrode. A lower interfacial energy anisotropy strength can slow down the growth of dendrite tips and improve the dendrite growth structure. The growth of the dendrites is influenced by the interfacial electrochemical driving force. Reducing the nucleation overpotential can effectively inhibit the growth of lithium dendrites.
A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables
Authors: Zhang Yuzhe , Liu Xiaodong , Li Haitao , Zhang Zhenbin
Published: April 2023

Permanent magnet synchronous generator (PMSG) wind power system with full power rating converter configuration is especially suitable for wind energy applications. Direct model predictive control (DMPC) has led to more possibilities in terms of choice because of its straightforward concept for PMSG wind turbine systems in high-power off-shore wind farms. However, due to complete dependence on the model knowledge, parameter mismatches will seriously deteriorate the system control performances. This work presents a model/parameter-independent predictive control method with a novel mechanism to update current/power variations online. The proposed method makes use of only two measurements from the former intervals and the selected control vectors to estimate all variations of the candidate vectors in the present interval. Benefiting from this updating mechanism, the proposed method is completely independent of the model parameters in the state prediction. However, it still has a very low calculating requirement and smooth current/power variation waveforms. The proposed method is compared with classical DMPC. The results validate that the proposed solution outperforms the classical DMPC with model deviations, with considerably improved robustness.
Economic and Environmental Assessment of Hydrogen Production from Brazilian Energy Grid
Authors: Santana José Carlos Curvelo , Machado Pedro Gerber , Nascimento Cláudio Augusto Oller do , Ribeiro Celma de Oliveira
Published: April 2023

The Brazilian energy grid is considered as one of the cleanest in the world, because it is composed of more than 80% of renewable energy sources. This work aimed to apply the levelized costs (LCOH) and environmental cost accounting techniques to demonstrate the feasibility of producing hydrogen (H2) by alkaline electrolysis powered by the Brazilian energy grid. A project of hydrogen production, with a lifetime of 20 years, had been evaluated by economical and sensitivity analysis. The production capacity (8.89 to 46.67 kg H2/h), production volume (25 to 100%), hydrogen sale price (1 to 5 USD/kg H2) and the MAR rate were varied. Results showed that at 2 USD/kg H2, all H2 production plant sizes are economically viable. On this condition, a payback of fewer than 4 years, an IRR greater than 31, a break-even point between 56 and 68% of the production volume and a ROI above 400% were found. The sensitivity analysis showed that the best economic condition was found at 35.56 kg H2/h of the plant size, which generated a net present value of USD 10.4 million. The cost of hydrogen varied between 1.26 and 1.64 USD/kg and a LCOH of 37.76 to 48.71 USD/MWh. LCA analysis showed that the hydrogen production project mitigated from 26 to 131 thousand tons of CO2, under the conditions studied.
Tackling Climate Change through Multi-Stakeholder Partnerships: Promoting SDG 17 to Combat Climate Change
Authors: Bulmer Elena , Yáñez-Araque Benito
Published: April 2023

The seventeen Sustainable Development Goals (SDGs) aim to address environmental, social, global, and economic challenges. The SDGs were a continuation of the Millennium Development Goals and assumed a common vision for the year 2030. Efforts to achieve the SDGs must be carried out in an integrated manner, respecting the three pillars of sustainable development, which are economic, social, and environmental. This review analyses the viability of Sustainable Development Goal 17 (SDG 17), which aims to build global partnerships for development. It makes specific reference to multi-stakeholder collaboration between all sectors of society. While the first sixteen SDGs are dedicated to concrete actions, SDG 17 Partnerships for Development coordinates and facilitates the implementation of the other goals. SDG 17 promotes the “right way” of collaboration between different actors through the formation of multi-stakeholder partnerships, which are essential to foster sustainable development. Although SDG 17 has its multiple advantages, it also does have its limitations, such as the present absence of a lessons-learned repository to share and understand how multi-stakeholder partnerships can prove more effective in promoting the successful implementation of the rest of the SDGs, as well as that SDG 17 seems to be regarded as more appropriate to the achievement of the economic pillar of sustainability at the moment, rather than being applied more widely. In this review, we analyze two case studies located in the south-western part of France, from which one can observe the great number of stakeholders, some non-human, even inanimate, present even in relatively minor projects and how due consideration of the interests of all of them in a manner following due process (albeit lengthy) enables decisions to be reached correctly and approved projects implemented soundly. One of the projects was rejected and did not proceed, while the other was approved and is going ahead.
Investigation of Partial Discharges within Power Oil Transformers by Acoustic Emission
Authors: Witos Franciszek , Olszewska Aneta
Published: April 2023

This paper presents the authors’ multi-channel measurement systems designed and built to conduct research on partial discharge phenomena using the acoustic emission method. The systems provide real-time monitoring, recording of signals and analysis of recorded signals. The analysis is carried out in time, frequency, time-frequency and discrimination threshold domains. In particular, a descriptor with the ADC acronym is defined, which ranks the signals according to the so-called degree of advancement. Studies have been carried out, showing that for a single partial discharge source, when tested in parallel, using the electrical and acoustic emission methods, the ranking of the signals using this descriptor is identical to the ranking according to the value of the apparent charge introduced by sources. This paper presents the authors’ patented method of partial discharge location and identification in power oil transformers. The results of tests of power oil transformer at the test station, conducted in parallel with the electric method and the authors’ method, and the results of tests in three selected transformers carried out during ongoing in-situ operation using the authors’ method are presented. Based on these results, the authors make diagnoses of the condition of the insulation systems in the tested transformers. The inspections of these transformers confirm the diagnoses.
Energy Performance Comparison of a Chiller Plant Using the Conventional Staging and the Co-Design Approach in the Early Design Phase of Hotel Buildings
Authors: Díaz Torres Yamile , Gullo Paride , Hernández Herrera Hernán , Torres del Toro Migdalia , Reyes Calvo Roy , Silva Ortega Jorge Iván , Gómez Sarduy Julio
Published: April 2023

As part of the design process of a chiller plant, one of the final stages is the energy testing of the system in relation to future operating conditions. Recent studies have suggested establishing robust solutions, but a conservative approach still prevails at this stage. However, the results of some recent studies suggest the application of a new co-design (control–design) approach. The present research involves a comparative analysis between the use of conventional staging and the co-design approach in the design phase of a chiller plant. This paper analyzes the energy consumption estimations of six different chiller plant combinations for a Cuban hotel. For the conservative approach using on/off traditional staging, the results suggest that the best option would be the adoption of a chiller plant featuring a symmetrical configuration. However, the outcomes related to the co-design approach suggest that the best option would be an asymmetrical configuration. The energy savings results were equal to 24.8% and the resulting coefficient of performance (COP) was 59.7% greater than that of the symmetrical configuration. This research lays firm foundations for the correct choice and design of a suitable chiller plant configuration for a selected hotel, allowing for significant energy savings in the tourism sector.
Multi-Step Wind Power Forecasting with Stacked Temporal Convolutional Network (S-TCN)
Authors: Nguyen Huu Khoa Minh , Phan Quoc-Dung , Wu Yuan-Kang , Phan Quoc-Thang
Published: April 2023

Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management difficulties for power systems. Currently, the methods of wind power forecasting (WPF) are various and numerous. An accurate forecasting method of WPF can help system dispatchers plan unit commitment and reduce the risk of the unreliability of electricity supply. In order to improve the accuracy of short-term prediction for wind power and address the multi-step ahead forecasting, this research presents a Stacked Temporal Convolutional Network (S-TCN) model. By using dilated causal convolutions and residual connections, the suggested solution addresses the issue of long-term dependencies and performance degradation of deep convolutional models in sequence prediction. The simulation outcomes demonstrate that the S-TCN model’s training procedure is extremely stable and has a powerful capacity for generalization. Besides, the performance of the proposed model shows a higher forecasting accuracy compared to other existing neural networks like the Vanilla Long Short-Term Memory model or the Bidirectional Long Short-Term Memory model.
Secondary Frequency Regulation Control Strategy with Electric Vehicles Considering User Travel Uncertainty
Authors: Dong Xiaohong , Ma Yang , Yu Xiaodan , Wei Xiangyu , Ren Yanqi , Zhang Xin
Published: April 2023

The premise of electric vehicles (EVs) participating in the frequency regulation (FR) of power systems is to satisfy the charging demands of users. In view of problems such as the uncertainty of EV users’ departure time and the increase in power supply pressure due to disordered charging in the frequency regulation process of EV clusters, a secondary frequency regulation control strategy with EVs considering user travel uncertainty is proposed. Firstly, EV charging history was analyzed, a reliability parameter was introduced to describe the user travel uncertainty, and an individual EV controllable domain model based on reliability correction was constructed. Then, EV clusters were grouped according to charging urgency and state of charge (SOC), and the controllable capacity of EV clusters was determined. Finally, EV frequency regulation capability parameters and charging urgency parameters were defined to determine the EV frequency regulation priority list, combined with the EV state grouping and priority list, and the EV cluster frequency control strategy was proposed. The simulation results show that the proposed strategy can satisfy the charging demands of users under uncertain travel conditions, reduce the power supply pressure of the power system caused by EVs entering the forced charging state, and effectively suppress frequency deviation.
Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation
Authors: Kedir Nebiyu , Nguyen Phuong H. D. , Pérez Citlaly , Ponce Pedro , Fayek Aminah Robinson
Published: April 2023

The application of fuzzy hybrid methods has significantly increased in recent years across various sectors. However, the application of fuzzy hybrid methods for modeling systems or processes, such as fuzzy machine learning, fuzzy simulation, and fuzzy decision-making, has been relatively limited in the energy sector. Moreover, compared to standard methods, the benefits of fuzzy-hybrid methods for capturing complex problems are not adequately explored for the solar energy sector, which is one of the most important renewable energy sources in electric grids. This paper investigates the application of fuzzy hybrid systems in the solar energy sector compared to other sectors through a systematic review of journal articles published from 2012 to 2022. Selection criteria for choosing an appropriate method in each investigated fuzzy hybrid method are also presented and discussed. This study contributes to the existing literature in the solar energy domain by providing a state-of-the-art review of existing fuzzy hybrid techniques to (1) demonstrate their capability for capturing complex problems while overcoming limitations inherent in standard modeling methods, (2) recommend criteria for selecting an appropriate fuzzy hybrid technique for applications in solar energy research, and (3) assess the applicability of fuzzy hybrid techniques for solving practical problems in the solar energy sector.
Elimination of the Ion–Molecule Association in Electrospray High-Resolution Mass Spectrometry Analyses for Petroleum Fractions
Authors: Wang Yidi , Li Shuofan , Wu Jianxun , Zhang Bo , Zhang Yahe
Published: April 2023

Electrospray ionization (ESI) coupled with high-resolution mass spectrometry (HRMS) is rapidly developing into a powerful tool for the molecular characterization of petroleum fractions. However, the occurrence of ion–molecule aggregates seriously affects the interpretation of the mass spectrometry results. Ion–molecule aggregates are not inherent compounds in petroleum but they are produced during the analysis of petroleum samples by high-resolution mass spectrometry. Also, the classes of aggregates are the same as many compounds in petroleum. Therefore, the removal of these species is crucial, and their analysis from methods of dissociation and mechanism points of view is essential for this purpose. In this study, aggregates of a vacuum gas oil fraction were analyzed and the types were defined. The compositions of the aggregates were characterized by changing operating conditions. Several factors affecting the aggregates were evaluated by a parameter of dissociation efficiency. By establishing the operating methods, the residual content of the aggregates can be controlled by less than 1% of the total ions. The results are instructive for the molecular characterization of petroleum fractions by using mass spectrometry with ESI ionization.
Addressing Multidimensional Energy Poverty Implications on Achieving Sustainable Development
Authors: Halkos George E. , Aslanidis Panagiotis-Stavros C.
Published: April 2023

This study examines whether shifts in the stance of policymaking can account for the observed predictability in excess energy poverty (EP) or fuel poverty (FP) levels. Energy-related poverty is a subcategory of global poverty and can be categorized into accessibility problems related to EP and affordability issues associated with FP, which have a similar but not identical meaning. Furthermore, developed and developing countries have different energy issues, as the former deal with FP and the latter with EP. However, there are discrepancies in EP not only between countries but within counties as well; for instance, there are differences in urban and rural areas too. Difficulties in energy access can be devastating for people living at risk of poverty. Social welfare, although at stake due to the energy crisis sparked at the same time as the warfare in eastern Europe. Renewables and green fossil fuels have price fluctuations, and inflation is also a stress factor in EP. Generally, solutions to EP and FP could be, inter alia, the adoption of renewables, governmental regulation, and supranational support through the green deals and sustainable development goals (SDGs). In short, the inflationary trend disequilibrium and raging war have put Agenda 2030 at stake due to the provocation of sustainability via energy-related vulnerability, insecurity, and poverty phenomena.
Variable-Speed Propeller Turbine for Small Hydropower Applications
Authors: Bílková Eva , Souček Jiří , Kantor Martin , Kubíček Roman , Nowak Petr
Published: April 2023

Standard technical solutions are not cost-effective for many small hydropower sites. This study aims to demonstrate the workflow for the tailor-made variable-speed axial propeller turbine and provide proof of this concept. The turbine is designed to meet the site’s specific space limitations and operating range needs. The runner shape is adjusted to the variable-speed operation and defined hydraulic profile using a parametric geometry model and CFD-based optimization. The variable-speed propeller turbine shows excellent flow control while keeping the mechanical design simple. The tailor-made approach minimizes construction costs using existing structures and is highly suitable for mini-hydropower applications. The prototype—an atypical turbine designed for highly restricted space and installed on-site—serves as proof of the concept. The findings on the design of axial variable-speed turbines are presented.
Spatiotemporal Variability in Disturbance Frequency and Severity across Mexico: Evidence from Conifer Tree Rings
Authors: Pompa-García Marín , Altman Jan , Paéz-Meráz Daniela Sarahi , Martínez Rivas José Alexis
Published: April 2023

It is critical to understand the ecological processes of forest dynamics by identifying past forest disturbances to take appropriate management actions. Tree-rings are commonly used for this purpose due to their reliability and accuracy. Here, we used a network of ring-width data distributed along a broad ecological gradient for the spatiotemporal identification of forest disturbances in 15 species of 13 sites of young Mexican conifers. We found different spatial and temporal patterns in the occurrence of forest disturbance. The species with the highest disturbance rates were Pinus patula (Ppa; El Jacalón), Pinus montezumae (Pmo; La Cueva), Pinus hartwegii (Phc; Cerro El Moro), and Pinus teocote (Pts; Rancho Joyas del Durazno) of the Gulf of Mexico, and Pinus ayacahuite (Pay; Corral de los Borregos) of the Mexico Center. The highest number of disturbances was found in 1953, 1956, 1976, and 1980; however, we noted that the number of disturbances tended to increase from 1975 to 1990 and particularly from 2000 onwards. The species close to the Gulf of Mexico presented a higher frequency and severity of disturbances than those located close to the Pacific Ocean. Thus, our results demonstrate a spatiotemporal pattern of disturbance events, which deserves further analysis, including regarding the drivers of disturbance. This knowledge is crucial for a better understanding of the dynamics of contemporary species.
Exploring a New O3 Index as a Proxy for the Avoidance/Tolerance Capacity of Forest Species to Tolerate O3 Injury
Authors: Manzini Jacopo , Hoshika Yasutomo , Moura Barbara Baesso , Paoletti Elena
Published: April 2023

Tropospheric ozone (O3) is a detrimental air pollutant causing phytotoxic effects. Several O3 indices are used to assess the risk for vegetation, e.g., the exposure-based AOT40 (accumulated ozone exposure over a threshold of 40 ppb) and the stomatal-flux based POD1 (Phytotoxic Ozone Dose above a threshold of 1 nmol m−2 s−1). Leaf Mass per Area (LMA) is recommended as a simple index to explain the plant tolerance capacity to O3. We therefore tested a new species-specific O3 index (Leaf Index Flux—LIF: calculated as stomatal O3 flux/LMA) as a proxy of the avoidance/tolerance capacity against O3 stress according to datasets of visible foliar injury (VFI) in forest monitoring and a manipulative Free-Air Controlled Exposure (FACE) experiment. For the forest monitoring, AOT40, POD1, and LIF were calculated from hourly O3, soil moisture, and meteorological measurements at nine Italian forest sites over the period 2018–2022. The results were tested for correlation with the O3 VFI annually surveyed at the same sites along the forest edge (LESS) or inside the forest (ITP) and expressed as relative frequency of symptomatic species in the LESS (SS_LESS) and Plant Injury Index per tree in the plot (PII_ITP). Based on VFI occurrence at ITP and LESS, Fagus sylvatica was considered the most O3-sensitive species, whereas conifers (Pinus pinea and Picea abies) and other deciduous/evergreen broadleaf (Quercus petraea, Q. cerris, Q. ilex, and Phyllirea latifolia) showed rare and no O3 VFI. Shrub species such as Rubus spp. and Vaccinium myrtillus were O3-sensitive, as they showed VFI along the LESS. AOT40 did not show significant correlations with the VFI parameters, POD1 increased with increasing SS_LESS (p = 0.005, r = 0.37) and PII_ITP (p < 0.001, r = 0.53), and LIF showed an even higher correlation with SS%_LESS (p < 0.001, r = 0.63) and PII_ITP (p < 0.001, r = 0.87). In the FACE experiment, PII was investigated for five deciduous and three evergreen tree species following one growing season of exposure to ambient and above-ambient O3 levels (PII_FACE). Moreover, PII_FACE resulted better correlated with LIF (r = 0.67, p < 0.001) than with POD1 (r = 0.58, p = 0.003) and AOT40 (r = 0.35, p = 0.09). Therefore, LIF is recommended as a promising index for evaluating O3 VFI on forest woody species and stresses high O3 risk potential for forest species with high stomatal conductance and thin leaves.
Enhancement of Boring Vibrations Based on Cascaded Dual-Domain Features Extraction for Insect Pest Agrilus planipennis Monitoring
Authors: Shi Haopeng , Chen Zhibo , Zhang Haiyan , Li Juhu , Liu Xuanxin , Ren Lili , Luo Youqing
Published: April 2023

Wood-boring beetles are among the most destructive forest pests. The larvae of some species live in the trunks and are covered by bark, rendering them difficult to detect. Early detection of these larvae is critical to their effective management. A promising surveillance method is inspecting the vibrations induced by larval activity in the trunk to identify whether it is infected. As convenient as it seems, it has a significant drawback. The identification process is easily disrupted by environmental noise and results in low accuracy. Previous studies have proven the feasibility and necessity of adding an enhancement procedure before identification. To this end, we proposed a small yet powerful boring vibration enhancement network based on deep learning. Our approach combines frequency-domain and time-domain enhancement in a stacked network. The dataset employed in our study comprises the boring vibrations of Agrilus planipennis larvae and various environmental noises. After enhancement, the SNR (signal-to-noise ratio) increment of a boring vibration segment reaches 18.73 dB, and our model takes only 0.46 s to enhance a 5 s segment on a laptop CPU. The accuracy of several well-known classification models showed a substantial increase using clips enhanced by our model. All experimental results proved our contribution to the early detection of larvae.
Organizational Models in European Forestry: An Attempt of Conceptualization and Categorization
Authors: Loreggian Francesco , Secco Laura , Pettenella Davide
Published: April 2023

The changes and challenges that are tackling the forest sector in recent decades have prompted governments and foresters to work hard to find innovative solutions. Research in the forestry domain has focused on product and process innovation, and more recently on business systems and social innovation. In addition, organizational innovation is recognized and documented. However, while consistent conceptualization work has been conducted for business models and social innovation, the organizational domain in forestry seems less clear, characterized by multiple actors and often overlapping in literature, while a clear framework to describe it is missing. This work proposes a conceptualization of the “organizational model”, a concept embracing different approaches to build an analytical framework used to describe and characterize organizations in the forestry sector. The framework is drafted referring to existing theories, then tested (and further developed) through a semi-systematic literature review on organizations operating in forest management in Europe that are identified, categorized, and characterized. This exercise confirms that forest management organizations can be described with several diverse arrangements and can be complex entities: a holistic and comprehensive approach is more likely to be used by policy initiatives addressing improvement of forest management.
Fuelwood Value Chain in Northern Nigeria: Economic, Environment, and Social Sustainability Concerns
Authors: Ali Bulama , Saadun Norzanalia , Kamarudin Norizah , Alias Mohamad Azani , Nawi Nolila Mohd , Azhar Badrul
Published: April 2023

The increased fuelwood demand and commercialization activities in Nigeria over the years have incurred various sustainability issues and concerns along the fuelwood value chain. This study employed value chain analysis to examine the sustainability of the fuelwood value chain in Yobe, Nigeria, in relation to economic, environmental, and social aspects. From the analysis, it was found that fuelwood business activities were conducted by registered licensed actors (formally) and informally by non-licensed actors who do not register with the government agency that controls forest-related activities. Profit was not equally distributed along the value chain, whereby 65% of the total profits were reaped by the transporters. Natural forests were the main source of fuelwood, with 18 tree species being preferred and commonly traded by both formal and informal chain actors. Women’s participation in the formal fuelwood value chain was limited and mainly concentrated in the informal chain. The findings of this study can inform policymakers to enhance the sustainability of fuelwood production and trade along the value chain, especially in terms of profit sharing, exploitation of forest resources, and gender equality.
Seeing Trees from Drones: The Role of Leaf Phenology Transition in Mapping Species Distribution in Species-Rich Montane Forests
Authors: Jiang Meichen , Kong Jiaxin , Zhang Zhaochen , Hu Jianbo , Qin Yuchu , Shang Kankan , Zhao Mingshui , Zhang Jian
Published: April 2023

The complex topography of subtropical montane forests favors the coexistence of diverse plant species, making these species-rich forests a high priority for biodiversity monitoring, prediction, and conservation. Mapping tree species distribution accurately in these areas is an essential basis for biodiversity research and is often challenging due to their complex structure. Remote sensing has widely been used for mapping tree species, but relatively little attention has been paid to species-rich montane forests. In this study, the capability of high-resolution UAV remote sensing imagery for mapping six tree species, standing dead trees, and canopy gaps was tested in a subtropical montane forest at an elevation of 816~1165 m in eastern China. Spectral, spatial geometrical, and textural features in a specific phenological period when obvious color differences among the leaves of different species were extracted, and four object-based classification algorithms (K-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), and random forest (RF)) were used for tree species classification. We found that: (1) mapping tree species distribution using low-cost UAV RGB imagery in a specific leaf phenological period has great application potential in subtropical montane forests with complex terrain. (2) Plant spectral features in the leaf senescence period contributed significantly to species classification, while the contribution of textural features was limited. The highest classification accuracy was 83% using KNN with the combination of spectral and spatial geometrical features. (3) Topographical complexity had a significant impact on mapping species distribution. The classification accuracy was generally higher in steep areas, especially in the low slope area.
14-3-3 Proteins Participate in Regulation of Natural Rubber Biosynthesis in Hevea brasiliensis
Authors: Zhang Miao , Yang Ziping , Guo Dong , Li Huiliang , Zhu Jiahong , Peng Shiqing , Wang Ying
Published: April 2023

Plant 14-3-3 proteins mediate a wide range of functionally diverse proteins through protein–protein interactions that are typically phosphorylation-dependent. However, the interactions between 14-3-3 proteins and the major regulators of nature rubber (NR) biosynthesis in H. brasiliensis have not been fully elucidated. In this study, we obtained 81 essential client proteins that interacted with H. brasiliensis 14-3-3 proteins (HbGF14s) through yeast two-hybrid (Y2H) screening. These interaction partners were involved in plant signal transduction, metabolism, development, and NR biosynthesis including small rubber particle protein (SRPP), rubber elongation factor (REF), and MYC2, etc. Furthermore, the interaction of HbGF14c and HbSRPP of H. brasiliensis was confirmed in plants through bimolecular fluorescence complementation (BiFC) assays and in vitro with Pull-down assays. Specifically, the RVSSYLP motif was found to mediate the interaction between HbSRPP and HbGF14c. The findings of this study provide a theoretical basis for the elucidation of the molecular regulation mechanism of the 14-3-3 proteins involved in NR biosynthesis, which could be used to enhance the production of rubber trees through genetic improvement.
Quantitative Evaluation for the Internal Defects of Tree Trunks Based on the Wavefield Reconstruction Inversion Using Ground Penetrating Radar Data
Authors: Feng Deshan , Liu Yuxin , Wang Xun , Ding Siyuan , Xu Deru , Yang Jun
Published: April 2023

A reliable inspection of the tree trunk internal defects is often considered vital in the health condition assessment for the living tree. There has been a desire to reconstruct the internal structure quantitatively using a non-destructive testing technology. This paper intends to apply wavefield reconstruction inversion (WRI) to obtain high-precision information from tree trunk detection using ground penetrating radar data. The variational projection method and the grouped multi-frequency strategy are adopted to strengthen the algorithm stability and adaptability by inverting frequency components sequentially. Through an irregular trunk model test, the influence of the penalty parameter, initial model, frequency strategy, and grid generation methods are investigated on WRI. Additionally, the comparison between full waveform inversion and WRI is discussed in detail. This synthetic case indicates that WRI is efficient and for a reasonable result, a proper multi-frequency strategy and an accurate mesh closer to reality are important. Furthermore, a field case of a historical tree is used to prove the validity and reliability of the algorithm. The success in this case indicates that our algorithm can characterize the distribution of media parameters of tree trunks accurately, which could provide data support for the rejuvenation and maintenance of living trees.
A 1 km Global Carbon Flux Dataset Using In Situ Measurements and Deep Learning
Authors: Shangguan Wei , Xiong Zili , Nourani Vahid , Li Qingliang , Lu Xingjie , Li Lu , Huang Feini , Zhang Ye , Sun Wenye , Dai Yongjiu
Published: April 2023

Global carbon fluxes describe the carbon exchange between land and atmosphere. However, already available global carbon fluxes datasets have not been adjusted by the available site data and deep learning tools. In this work, a global carbon fluxes dataset (named as GCFD) of gross primary productivity (GPP), terrestrial ecosystem respiration (RECO), and net ecosystem exchange (NEE) has been developed via a deep learning based convolutional neural network (CNN) model. The dataset has a spatial resolution of 1 km at three time steps per month from January 1999 to June 2020. Flux measurements were used as a training target while remote sensing of vegetation conditions and meteorological data were used as predictors. The results showed that CNN could outperform other commonly used machine learning methods such as random forest (RF) and artificial neural network (ANN) by leading to satisfactory performance with R2 values of the validation stage as 0.82, 0.72 and 0.62 for GPP, RECO, and NEE modelling, respectively. Thus, CNN trained using reanalysis meteorological data and remote sensing data was chosen to produce the global dataset. GCFD showed higher accuracy and more spatial details than some other global carbon flux datasets with reasonable spatial pattern and temporal variation. GCFD is also in accordance with vegetation conditions detected by remote sensing. Owing to the obtained results, GCFD can be a useful reference for various meteorological and ecological analyses and modelling, especially when high resolution carbon flux maps are required.
Effects of Climate on Variation of Soil Organic Carbon and Alkali-Hydrolyzed Nitrogen in Subtropical Forests: A Case Study of Zhejiang Province, China
Authors: Cheng Xuekun , Zhou Tao , Liu Shuhan , Sun Xiaobo , Zhou Yufeng , Xu Lin , Xie Binglou , Ying Jianping , Shi Yongjun
Published: April 2023

Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems.
Temporal and Spatial Analysis of the Changes in the Supply of Ecological Products in State-Owned Forest Farms after the Reform
Authors: Huang Wei , Chen Rongyuan , Chen Wenhui
Published: April 2023

To reveal the changes in the supply efficiency of forest farm ecological products after the reform of state-owned forest farms, this study uses the ecological product accounting method to calculate the physical quantity and value of state-owned forest farm ecological products using data from 2015 and 2020. Through exploratory spatial data analysis, the spatial distribution of the supply efficiency of ecological products of state-owned forest farms and its primary index are studied to explore the temporal and spatial evolution of ecological products from state-owned forest farms in China. The results show the following: (1) Through the reform of state-owned forest farms, the supply capacity of forest farms’ ecological products has been greatly improved, and the physical quantity and value quantity of ecological products have shown an increasing trend, with growth rates of 31.7% and 24.2%, respectively. (2) There is strong spatial heterogeneity in the supply efficiency of ecological products from state-owned forest farms in various provinces, and the overall distribution pattern is gradually decreasing from the northwest to the southeast coastal provinces. (3) There is a negative correlation between the supply capacity of ecological products and provincial GDP. The supply capacity of ecological products in provinces with high per capita GDP is relatively low. (4) The comprehensive index of the supply efficiency of ecological products and its first-level index of state-owned forest farms are characterized by different degrees of agglomeration in space, which are mainly represented by three distribution modes: “H–H”, “L–L”, and “L–H”. Therefore, it is necessary to clarify the ecological function orientation of state-owned forest farms, improve the endogenous power of ecological product supply, actively explore and connect social ecological needs, and achieve coordinated progress in terms of ecological environmental protection and economic development.
Integrating Machine Learning and a Spatial Contextual Algorithm to Detect Wildfire from Himawari-8 Data in Southwest China
Authors: Liu Chuanfeng , Chen Rui , He Binbin
Published: April 2023

Timely wildfire detection is helpful for fire monitoring and fighting. However, the available wildfire products with high temporal resolutions face problems, such as high omission error and commission error (false alarm) rates. This study proposed a wildfire detection algorithm combined with an improved spatial contextual algorithm and machine learning method in southwest China. First, a dataset consisting of a formation of high-confidence fire pixels combining the WLF (Himawari Wild Fire product) and VIIRS wildfire products was constructed. Then, a model to extract potential fire pixels was built using the random forest method. Additionally, an improved spatial contextual algorithm was used to identify actual fire pixels from potential fire pixels. Finally, strategies such as sun glint rejection were used to remove false alarms. As a result, the proposed algorithm performed better, with both a lower omission error rate and a lower commission error rate than the WLF product. It had a higher F1 score (0.47) than WLF (0.43) with VIIRS for reference, which means it is more suitable for wildfire detection.
Microbiome and Volatile Metabolic Profile of Acetic Acid Fermentation Using Multiple Starters for Traditional Grain Vinegar
Authors: Kong Haram , Kim Sun Hee , Jeong Woo-Soo , Kim So-Young , Yeo Soo-Hwan
Published: April 2023

Traditional grain vinegar is fermented using multiple acetic acid bacteria (AAB) at various temperatures. A single AAB showed high acid-producing ability at 30 °C with a 5% alcohol concentration and an initial pH adjusted to 4.0. Multiple AAB were similar to a single AAB; however, the optimal initial pH was 3.0. Acid production ability according to the type of AAB was higher in multiple AAB than in single AAB. That is, using multiple AAB helped increase the titratable acidity of traditional grain vinegar. In addition, increasing the titratable acidity and content of volatile flavor compounds was advantageous when two, rather than four, AAB types were mixed and used. The titratable acidity was high at medium temperatures (30 °C); however, volatile flavor compounds increased at low temperatures (20 °C) under multiple AAB. A 16S rDNA-based microbiome taxonomic profiling analysis identified differences in beta diversity due to multiple AAB and fermentation temperatures. In particular, beta diversity analysis revealed a specific pattern when a mixture of Acetobacter ascedens GV–8 and Acetobacter pasteurianus GV–22 was fermented at a low temperature (20 °C). Therefore, we propose the application of multiple AAB with acidic and flavor-producing properties in traditional grain vinegar.