bluuebunny/crossref_metadata_2025 huggingface dataset:
The Potential Role of Neutrophil-Reactive Intensity (NEUT-RI) in the Diagnosis of Sepsis in Critically Ill Patients: A Retrospective Cohort Study
Authors: Mantovani Elena Maria Alessandra , Formenti Paolo , Pastori Stefano , Roccaforte Vincenzo , Gotti Miriam , Panella Rossella , Galimberti Andrea , Costagliola Roberto , Vetrone Francesco , Umbrello Michele , Pezzi Angelo , Sabbatini Giovanni
Published: May 2023

The diagnosis of sepsis is often difficult and belated, substantially increasing mortality in affected patients. Its early identification allows for us to choose the most appropriate therapies in the shortest time, improving patients’ outcomes and eventually their survival. Since neutrophil activation is an indicator of an early innate immune response, the aim of the study was to evaluate the role of Neutrophil-Reactive Intensity (NEUT-RI), which is an indicator of their metabolic activity, in the diagnosis of sepsis. Data from 96 patients consecutively admitted to the Intensive Care Unit (ICU) were retrospectively analyzed (46 patients with and 50 without sepsis). Patients with sepsis were further divided between sepsis and septic shock according to the severity of the illness. Patients were subsequently classified according to renal function. For the diagnosis of sepsis, NEUT-RI showed an AUC of >0.80 and a better negative predictive value than Procalcitonin (PCT) and C-reactive protein (CRP) (87.4% vs. 83.9% and 86.6%, p = 0.038). Unlike PCT and CRP, NEUT-RI did not show a significant difference within the “septic” group between patients with normal renal function and those with renal failure (p = 0.739). Similar results were observed among the “non-septic” group (p = 0.182). The increase in NEUT-RI values could be useful in the early ruling-out of sepsis, and it does not appear to be influenced by renal failure. However, NEUT-RI has not proved to be efficient in discriminating the severity of sepsis at the time of admission. Larger, prospective studies are needed to confirm these results.
The Role of Dental Practitioners in the Management of Oncology Patients: The Head and Neck Radiation Oncology Patient and the Medical Oncology Patient
Authors: Owosho Adepitan A. , DeColibus Katherine , Hedgepeth Beverly , Wood Burton C. , Sansoni Ritter E. , Gleysteen John P. , Schwartz David L.
Published: May 2023

This narrative review addresses the role of a dentist in the management of oncology patients, highlighting the oral complications that arise in head and neck radiation oncology patients and medical oncology patients. The prevention and management of these complications are discussed.
Subdomain Adaptation Capsule Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear
Authors: Wu Yanze , Yan Jing , Xu Zhuofan , Sui Guoqing , Qi Meirong , Geng Yingsan , Wang Jianhua
Published: May 2023

Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.
Evolutionary Method of Heterogeneous Combat Network Based on Link Prediction
Authors: Qiu Shaoming , Chen Fen , Wang Yahui , Zhao Jiancheng
Published: May 2023

Currently, research on the evolution of heterogeneous combat networks (HCNs) mainly focuses on the modeling process, with little attention paid to the impact of changes in network topology on operational capabilities. Link prediction can provide a fair and unified comparison standard for network evolution mechanisms. This paper uses link prediction methods to study the evolution of HCNs. Firstly, according to the characteristics of HCNs, a link prediction index based on frequent subgraphs (LPFS) is proposed. LPFS have been demonstrated on a real combat network to be superior to 26 baseline methods. The main driving force of research on evolution is to improve the operational capabilities of combat networks. Adding the same number of nodes and edges, 100 iterative experiments demonstrate that the evolutionary method (HCNE) proposed in this paper outperforms random evolution and preferential evolution in improving the operational capabilities of combat networks. Furthermore, the new network generated after evolution is more consistent with the characteristics of a real network.
On the Possibility of Reproducing Utsu’s Law for Earthquakes with a Spring-Block SOC Model
Authors: Salinas-Martínez Alfredo , Perez-Oregon Jennifer , Aguilar-Molina Ana María , Muñoz-Diosdado Alejandro , Angulo-Brown Fernando
Published: May 2023

The Olami, Feder and Christensen (OFC) spring-block model has proven to be a powerful tool for analyzing and comparing synthetic and real earthquakes. This work proposes the possible reproduction of Utsu’s law for earthquakes in the OFC model. Based on our previous works, several simulations characterizing real seismic regions were performed. We located the maximum earthquake in these regions and applied Utsu’s formulae to identify a possible aftershock area and made comparisons between synthetic and real earthquakes. The research compares several equations to calculate the aftershock area and proposes a new one with the available data. Subsequently, the team performed new simulations and chose a mainshock to analyze the behavior of the surrounding events, so as to identify whether they could be catalogued as aftershocks and relate them to the aftershock area previously determined using the formula proposed. Additionally, the spatial location of those events was considered in order to classify them as aftershocks. Finally, we plot the epicenters of the mainshock, and the possible aftershocks comprised in the calculated area resembling the original work of Utsu. Having analyzed the results, it is likely to say that Utsu’s law is reproducible using a spring-block model with a self-organized criticality (SOC) model.
Swarming Transition in Super-Diffusive Self-Propelled Particles
Authors: Nattagh Najafi Morteza , Zayed Rafe Md. Abu , Nabavizadeh Seyed Amin
Published: May 2023

A super-diffusive Vicsek model is introduced in this paper that incorporates Levy flights with exponent α. The inclusion of this feature leads to an increase in the fluctuations of the order parameter, ultimately resulting in the disorder phase becoming more dominant as α increases. The study finds that for α values close to two, the order–disorder transition is of the first order, while for small enough values of α, it shows degrees of similarities with the second-order phase transitions. The article formulates a mean field theory based on the growth of the swarmed clusters that accounts for the decrease in the transition point as α increases. The simulation results show that the order parameter exponent β, correlation length exponent ν, and susceptibility exponent γ remain constant when α is altered, satisfying a hyperscaling relation. The same happens for the mass fractal dimension, information dimension, and correlation dimension when α is far from two. The study reveals that the fractal dimension of the external perimeter of connected self-similar clusters conforms to the fractal dimension of Fortuin–Kasteleyn clusters of the two-dimensional Q=2 Potts (Ising) model. The critical exponents linked to the distribution function of global observables vary when α changes.
Feelings about School in Gifted and Non-Gifted Children: What Are the Effects of a Fine Art Program in Primary School?
Authors: Sanchez Christine , Blanc Nathalie
Published: May 2023

There is a consensus about the benefits of an artistic activity on health and well-being. In France, a gifted child is considered a special needs student for whom enrichment is advocated. Therefore, this study examines the extent to which a whole-class art enrichment program delivered to both gifted and non-gifted children benefits both student populations with respect to their school well-being. The art program was implemented in classrooms over the course of an entire school year (during the COVID-19 pandemic). The self-report French version of the Feelings About School scale (i.e., FAS) was completed in three steps (i.e., before, mid-program, and after) by a sample of gifted and non-gifted children benefiting from the program. The FAS scores of those students were also compared at the end of the school year with those of students who did not participate in the art program. Despite the pandemic context that requires caution in drawing definite conclusions, this study supports that (i) the fine arts practice is a lever of development, (ii) the sanitary situation was detrimental for elementary school students, and (iii) better adaptive capacities were exhibited by gifted children in this context.
The Relationship between Orthorexia Nervosa and Obsessive Compulsive Disorder
Authors: Duradoni Mirko , Gursesli Mustafa Can , Fiorenza Maria , Guazzini Andrea
Published: May 2023

Orthorexia nervosa (ON) is characterized by an intense avoidance of foods considered unhealthy, obsession with healthy eating behaviors, and pathological fixation on healthy foods. Although there are still debates in the literature about the psychological factors and symptoms of ON, it should be noted that many of the symptoms share common features with obsessive compulsive disorder (OCD). The aim of the present study was to investigate the relationship between ON and OCD with its subtypes. In this framework, the cross-sectional study was conducted with an opportunistic sample of 587 participants (86% women and 14% men), with an average age of 29.32 (s.d. = 11.29; age range = 15–74). Our work showed that almost all OCD subtypes were largely correlated with ON. The lowest correlation was for “Checking” and the highest for “Obsession”. Overall, the OCD subtypes (i.e., Indecisiveness, Just Right, Obsession, and Hoarding) were more strongly associated with ON measures, while subtypes Checking and Contamination, although positively associated, had lower correlation coefficients.
Reconfiguration Using Bio-Inspired Conduction Mode of Field-Effect Transistors toward the Creation of Recyclable Devices
Authors: Baca-Arroyo Roberto
Published: May 2023

A bio-inspired conduction mode in silicon-based field-effect transistors was studied here using the frequency-dependent reconfiguration principle in a size-reduced circuit architecture. Analog circuits comprising neuromorphic and reconfigurable behavior were analyzed across their physical quantities using a set of equations governing circuit performance. Practical examples were built, analyzed, and discussed from a phenomenological viewpoint. Upon exploiting their reconfiguration properties when semiconductor devices and passive components are interconnected, novel operating principles might inspire optimized signal processing and manufacturing facilities to design circular device-based complex systems.
Millimeter-Wave Dual-Band MIMO Channel Capacity Analysis Based on Climate Data: A Samsun Province Case Study
Authors: Kola Ahmet Furkan , Kurnaz Çetin , Cheema Adnan Ahmad , Rahimian Ardavan
Published: May 2023

The analysis of multiple-input multiple-output (MIMO) channel capacity is important for developing and optimizing high-speed wireless communication systems that can meet the growing demand for data-intensive applications. This study aims to analyze the 4 × 4 MIMO channel capacity of outdoor urban and rural environments using the NYUSIM simulator. The channel models are designed for 28 GHz and 39 GHz frequencies for both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Realistic channel models are simulated using annual climate data collected in Samsun province, Turkey in three different environments: urban microcell (UMi), urban macrocell (UMa), and rural macrocell (RMa) areas. According to the annual average channel capacity analysis, it is observed that there is a very small capacity difference between UMi and UMa areas at 28 GHz and 39 GHz frequencies in the LOS region, while the RMa area is found to have a very low capacity compared to the UMi and UMa areas. The channel capacity for RMa is found to be approximately eight times smaller than UMi and UMa. In the NLOS region, channel capacities decrease significantly (between 312 and 3953 times) compared to the LOS region, with the UMa area having the greatest capacity and the UMi area having the lowest capacity. Compared to the UMi channel capacity, the RMa channel capacity is 1.36 times higher for 28 GHz and 1.28 times higher for 39 GHz. When the monthly changes in channel capacity are examined, it is discovered that the amount of precipitation has the greatest impact on channel capacity, and the capacity decreases as the rain rate increases. The highest correlation between channel capacity and rain rate was −0.97 for RMa, with a 28 GHz frequency and LOS conditions. Additionally, it becomes clear that channel capacities increase in the summer months as the temperature rises and humidity and pressure fall.
An Improved Lightweight Real-Time Detection Algorithm Based on the Edge Computing Platform for UAV Images
Authors: Cao Lijia , Song Pinde , Wang Yongchao , Yang Yang , Peng Baoyu
Published: May 2023

Unmanned aerial vehicle (UAV) image detection algorithms are critical in performing military countermeasures and disaster search and rescue. The state-of-the-art object detection algorithm known as you only look once (YOLO) is widely used for detecting UAV images. However, it faces challenges such as high floating-point operations (FLOPs), redundant parameters, slow inference speed, and poor performance in detecting small objects. To address the above issues, an improved, lightweight, real-time detection algorithm was proposed based on the edge computing platform for UAV images. In the presented method, MobileNetV3 was used as the YOLOv5 backbone network to reduce the numbers of parameters and FLOPs. To enhance the feature extraction ability of MobileNetV3, the efficient channel attention (ECA) attention mechanism was introduced into MobileNetV3. Furthermore, in order to improve the detection ability for small objects, an extra prediction head was introduced into the neck structure, and two kinds of neck structures with different parameter scales were designed to meet the requirements of different embedded devices. Finally, the FocalEIoU loss function was introduced into YOLOv5 to accelerate bounding box regression and improve the localization accuracy of the algorithm. To validate the performance of the proposed improved algorithm, we compared our algorithm with other algorithms in the VisDrone-Det2021 dataset. The results showed that compared with YOLOv5s, MELF-YOLOv5-S achieved a 51.4% reduction in the number of parameters and a 38.6% decrease in the number of FLOPs. MELF-YOLOv5-L had 87.4% and 47.4% fewer parameters and FLOPs, respectively, and achieved higher detection accuracy than YOLOv5l.
To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition
Authors: Shen Qiang , Teso Stefano , Giunchiglia Fausto , Xu Hao
Published: May 2023

Human activity recognition (HAR) plays a central role in ubiquitous computing applications such as health monitoring. In the real world, it is impractical to perform reliably and consistently over time across a population of individuals due to the cross-individual variation in human behavior. Existing transfer learning algorithms suffer the challenge of “negative transfer”. Moreover, these strategies are entirely black-box. To tackle these issues, we propose X-WRAP (eXplain, Weight and Rank Activity Prediction), a simple but effective approach for cross-individual HAR, which improves the performance, transparency, and ease of control for stakeholders in HAR. X-WRAP works by wrapping transfer learning into a meta-learning loop that identifies the approximately optimal source individuals. The candidate source domains are ranked using a linear scoring function based on interpretable meta-features capturing the properties of the source domains. X-WRAP is optimized using Bayesian optimization. Experiments conducted on a publicly available dataset show that the model can effectively improve the performance of transfer learning models consistently. In addition, X-WRAP can provide interpretable analysis according to the meta-features, making it possible for stakeholders to get a high-level understanding of selective transfer. In addition, an extensive empirical analysis demonstrates the promise of the approach to outperform in data-sparse situations.
Dynamic Models with Sigmoid Corrections to Generation of an Achievable 4D-Trajectory for a UAV and Estimating Wind Disturbances
Authors: Krasnova Svetlana A. , Kokunko Julia G. , Utkin Victor A.
Published: May 2023

For an unmanned aerial vehicle (UAV) of an aircraft type, the problems of planning achievable trajectories as well as robust control under wind disturbances are considered. A computationally simple method for compiling a primary non-smooth 4D trajectory is proposed. Its segments connect the given waypoints and determine the desired average velocity in various sections. Instead of time-consuming methods of analytical smoothing of broken path joints using polynomials, a tracking differentiator with S-shaped smooth and limited sigmoid corrective actions is developed. This virtual dynamic model provides natural smoothing of the primary trajectory considering the design constraints on the velocity, acceleration, and thrust of the UAV. The tracking differentiator variables create an achievable trajectory and are used to synthesize the UAV tracking system. To compensate for the action of wind disturbances on the UAV, a disturbance observer is developed. It is a replica of the equations of the control plant model, which are directly affected by external uncontrolled disturbances. These algorithms also use sigmoid corrections. Unlike standard disturbances observers, this approach does not require the development of a dynamic model of external disturbances and does not assume their smoothness. The effectiveness of the developed algorithms was confirmed by numerical simulation.
A High-Throughput Processor for GDN-Based Deep Learning Image Compression
Authors: Shao Hu , Liu Bingtao , Li Zongpeng , Yan Chenggang , Sun Yaoqi , Wang Tingyu
Published: May 2023

Deep learning-based image compression techniques can take advantage of the autoencoder’s benefits to achieve greater compression quality at the same bit rate as traditional image compression, which is more in line with user desires. Designing a high-performance processor that can increase the inference speed and efficiency of the deep learning image compression (DIC) network is important to make this technology more extensively employed in mobile devices. To the best of our knowledge, there is no dedicated processor that can accelerate DIC with low power consumption, and general-purpose network accelerators based on field programmable gate arrays (FPGA) cannot directly process compressed networks, so we propose a processor suitable for DIC in this paper. First, we analyze the image compression algorithm and quantize the data of the network into 16-bit fixed points using a dynamic hierarchical quantization. Then, we design an operation module, which is the core computational part for processing. It is composed of convolution, sampling, and normalization units, which pipeline the inference calculation for each layer of the network. To achieve high-throughput inference computing, the processing elements group (PEG) array with local buffers is developed for convolutional computation. Based on the common components in encoding and decoding, the sampling and normalization units are compatible with codec computation and utilized for image compression with time-sharing multiplexing. According to the control signal, the operation module could change the order of data flow through the three units so that they perform encoding and decoding operations, respectively. Based on these design methods and schemes, DIC is deployed into the Xilinx Zynq ZCU104 development board to achieve high-throughput image compression at 6 different bit rates. The experimental results show that the processor can run at 200 MHz and achieve 283.4 GOPS for the 16 bits fixed-point DIC network.
Nuclear-Renewable Hybrid Energy System with Load Following for Fast Charging Stations
Authors: Esteves Otavio Lopes Alves , Gabbar Hossam A.
Published: May 2023

The transportation sector is a significant source of greenhouse gas emissions. Electric vehicles (EVs) have gained popularity as a solution to reduce emissions, but the high load of charging stations poses a challenge to the power grid. Nuclear-Renewable Hybrid Energy Systems (N-RHES) present a promising alternative to support fast charging stations, reduce grid dependency, and decrease emissions. However, the intermittent problem of renewable energy sources (RESs) limits their application, and the synergies among different technologies have not been fully exploited. This paper proposes a predictive and adaptive control strategy to optimize the energy management of N-RHES for fast charging stations, considering the integration of nuclear, photovoltaics, and wind turbine energy with a hydrogen storage fuel cell system. The proposed dynamic model of a fast-charging station predicts electricity consumption behavior during charging processes, generating probabilistic forecasting of electricity consumption time-series profiling. Key performance indicators and sensitivity analyses illustrate the practicability of the suggested system, which offers a comprehensive solution to provide reliable, sustainable, and low-emission energy to fast-charging stations while reducing emissions and dependency on the power grid.
Natural Gas–Electricity Price Linkage Analysis Method Based on Benefit–Cost and Attention–VECM Model
Authors: Zhou Sheng , Gan Wen , Liu Ang , Jiang Xinyue , Shen Chengliang , Wang Yunchu , Yang Li , Lin Zhenzhi
Published: May 2023

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods.
Experimental Characterization of an Additively Manufactured Inconel 718 Heat Exchanger for High-Temperature Applications
Authors: Battaglia Fabio , Arie Martinus , Zhang Xiang , Ohadi Michael , Shooshtari Amir
Published: May 2023

This work presents the experimental results of a novel, air-to-air, additively manufactured manifold-microchannel heat exchanger with straight fins on both sides. The heat exchanger was made of Inconel 718 using a direct metal laser sintering technique. The overall core size of the heat exchanger was 94 mm × 87.6 mm × 94.4 mm, with a fin thickness of 0.220 mm on both the hot and cold sides. The heat exchanger was tested with pressurized nitrogen gas at 300 °C and 340 kPa for the hot side, while air at an ambient condition was used for the cold side. An overall heat transfer of 276 W/m2K was obtained for Reynolds number values of 132 and 79 for the cold and hot sides, respectively. A gravimetric heat transfer density (Q/m∆T) of 4.7–6.7 W/kgK and a volumetric heat transfer density (Q/V∆T) of 6.9–9.8 kW/m3K were recorded for this heat exchanger with a coefficient of performance value that varied from 42 to 52 over the operating conditions studied here. The experimental pressure drop results were within 10% of the numerical values, while the corresponding heat transfer results were within 17% of the numerical results, mainly due to imperfections in the fabrication process. Despite this penalty, the performance of the tested heat exchanger was superior to the conventional plate-fin heat exchangers: more than 60% of improvements in both gravimetric and volumetric heat transfer densities were recorded for the entire range of experimental data.
Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters
Authors: Kujawska Justyna , Kulisz Monika , Oleszczuk Piotr , Cel Wojciech
Published: May 2023

Recently, biomass has become an increasingly widely used energy resource. The problem with the use of biomass is its variable composition. The most important property that determines the energy content and thus the performance of fuels such as biomass is the heating value (HHV). This paper focuses on selecting the optimal number of input variables using linear regression (LR) and the multivariate adaptive regression splines approach (MARS) to create an artificial neural network model for predicting the heating value of selected biomass. The MARS model selected the input data better than the LR model. The best modeling results were obtained for a network with three input neurons and nine neurons in the hidden layer. This was confirmed by a high correlation coefficient of 0.98. The obtained results show that artificial neural network (ANN) models are effective in predicting the calorific value of woody and field biomass, and can be considered a worthy simulation model for use in selecting biomass feedstocks and their blends for renewable fuel applications.
Application of Deep Neural Network in Gearbox Compound Fault Diagnosis
Authors: Zhang Xiangfeng , Xu Qinghong , Jiang Hong , Li Jun
Published: May 2023

Gearbox fault diagnosis is vital to ensure the efficient operation of rotating machinery, and most gearbox faults in industrial production occur in the form of compound faults. To realize the diagnosis of compound faults in gearboxes at different speeds, an “end-to-end” intelligent diagnosis method based on an efficient channel attention capsule network (ECA-CN) is proposed. First, the process uses a deep convolutional neural network to extract fault features from the collected raw vibration signals, embeds the efficient channel attention module to filter important fault features, uses the capsule network to vectorize the feature space information and, finally, calculates the correlation between different levels of capsules using a dynamic routing algorithm to achieve accurate gearbox compound fault diagnosis. The effectiveness of the proposed ECA-CN fault diagnosis method is verified using the composite fault dataset of the 2009 PHM Challenge gearbox, with an average accuracy of 99.63 ± 0.22%. In the comparison experiments using the traditional fault diagnosis method, the average accuracy of the ECA-CN method improved by 4.62%, and the standard deviation was reduced by 0.58%. The experimental results show that the ECA-CN has a more competitive diagnostic performance than traditional shallow machine learning models and CNNs.
An Artificial Neural Network-Based Approach for Real-Time Hybrid Wind–Solar Resource Assessment and Power Estimation
Authors: Shafi Imran , Khan Harris , Farooq Muhammad Siddique , Diez Isabel de la Torre , Miró Yini , Galán Juan Castanedo , Ashraf Imran
Published: May 2023

The precise prediction of power estimates of wind–solar renewable energy sources becomes challenging due to their intermittent nature and difference in intensity between day and night. Machine-learning algorithms are non-linear mapping functions to approximate any given function from known input–output pairs and can be used for this purpose. This paper presents an artificial neural network (ANN)-based method to predict hybrid wind–solar resources and estimate power generation by correlating wind speed and solar radiation for real-time data. The proposed ANN allows optimization of the hybrid system’s operation by efficient wind and solar energy production estimation for a given set of weather conditions. The proposed model uses temperature, humidity, air pressure, solar radiation, optimum angle, and target values of known wind speeds, solar radiation, and optimum angle. A normalization function to narrow the error distribution and an iterative method with the Levenberg–Marquardt training function is used to reduce error. The experimental results show the effectiveness of the proposed approach against the existing wind, solar, or wind–solar estimation methods. It is envisaged that such an intelligent yet simplified method for predicting wind speed, solar radiation, and optimum angle, and designing wind–solar hybrid systems can improve the accuracy and efficiency of renewable energy generation.
Homeowners’ Perceptions of Renewable Energy and Market Value of Sustainable Buildings
Authors: Vardopoulos Ioannis , Vannas Ioannis , Xydis George , Vassiliades Constantinos
Published: May 2023

Growing concerns over environmental issues and sustainable living have resulted in increased interest in renewable energy and energy efficiency. The real estate market is no exception, with homeowners increasingly considering the market value of green and sustainable buildings, which can offer both energy efficiency and potential health benefits. This study investigates the level of interest among homeowners in investing in renewable energy sources and energy efficiency measures for their homes and how it relates to their perception of the market value of green or sustainable buildings in the real estate market. A survey was conducted in the Paphos urban complex in Cyprus, with 180 participants over the age of 18. The participants were selected through a random sampling method and were representative of the general population in terms of gender, age, and income. Data were collected on their attitudes towards renewable energy sources and energy efficiency, as well as their perceptions of the market value of green buildings. The data collected were analyzed using various statistical methods, including Cronbach’s α coefficient, the non-parametric Friedman test, descriptive statistics, and factor analysis, with the Statistical Package for the Social Sciences (SPSS) being used for coding and analysis. Results indicate that 64% of the homeowners surveyed were interested in investing in renewable energy sources, and 72% were interested in energy efficiency measures. Additionally, findings suggest a moderate level of interest (58%) among homeowners in investing in renewable energy sources and that this is positively associated with their perception of the market value of green buildings. Furthermore, homeowners with higher income and education levels tend to be more interested in investing in renewable energy sources and energy efficiency measures and perceive green buildings as having higher market value. This study provides insights into the factors that drive homeowners’ investment in renewable energy sources and energy efficiency measures, shedding light on the relationship between homeowners’ perceptions of the market value of green buildings and their interest in such investments.
The Faster the Better? Optimal Warm-Up Strategies for a Micro Combined Heat and Power Plant
Authors: Zobel Tammo , Ritter Andreas , Onder Christopher H.
Published: May 2023

The warm-up process is a critical operation phase for micro Combined Heat and Power (mCHP) plants, directly impacting their efficiency, reliability, and lifetime. As small decentralized power generation units are increasingly expected to be operated on demand, start-ups will occur more frequently and thus the importance of the warm-up process will further increase. In this study, we address this problem by presenting a mathematical optimization framework that finds optimal actuator trajectories that significantly reduce the warm-up time and improve the thermal efficiency of an mCHP plant. The proposed optimization framework is highly flexible and adaptable to various objective functions, such as maximizing efficiency or minimizing the deviation from desired temperature references. The underlying mathematical model has been experimentally validated on a physical mCHP test rig. Selected case studies further demonstrate the effectiveness and flexibility of the framework and show that with the optimized actuator trajectories, the mCHP plant can reach its steady-state operating temperature in 40% less time. The results also indicate that the shortest warm-up time does not necessarily lead to the highest thermal efficiency. Accordingly, the methodology proposed in this paper provides a powerful tool to study higher-level operational strategies of mCHP plants and thus to maximize their overall performance, which directly translates into an improved operational cost-effectiveness, particularly in demand-driven energy landscapes.
Impacts of Foreign Trade on the Economy of Wood-Based Sectors Generating Different Levels of Value Added in the Slovak and Czech Republics
Authors: Janáková Sujová Andrea , Marcineková Katarína , Kupčák Václav
Published: May 2023

Foreign trade belongs among the main sources of economic growth as classical theories of international trade affirm. The aim of the paper is to evaluate the impact of trade balance flows on sectors generating different value-added in the wood-based industries (WBI) of the Czech and Slovak Republics. The multivariate regression method (MLR) was applied to identify the relationship between foreign trade and economic indicators and also specific indicators assessing impacts of foreign trade on the economy of wood-based industries. The results showed that the performance of high value-added production is only slightly affected by foreign trade. It means that both monitored countries do not utilize raw wood so intensively that the positive effects of foreign trade are manifested. Growth in net exports represents a positive influence on the economy of the sector regardless of the value-added rate only if the increase in imports is smaller than in exports. The contribution of the study to existing knowledge is in using specific indicators evaluating trade impacts on the industry’s economy. The article provides new empirical insights into the influence of foreign trade balance flows on the economy of wood-based sectors with a different value-added rate.
Identification and Characterization of Colletotrichum Species Causing Tea-Oil Camellia (Camellia oleifera C.Abel) Anthracnose in Hainan, China
Authors: Zhu Hui , He Chaozu
Published: May 2023

Camellia oleifera C.Abel, commonly known as tea-oil camellia, is a type of significant woody oil crop that is widely cultivated in southern China. During 2017–2021, a fungal foliar disease was detected in routine surveys in Hainan. However, diseases of tea-oil camellia are seldom reported in Hainan. In this study, 51 Colletotrichum spp. isolates were obtained from the symptomatic samples of tea-oil camellia, collected from three production sites located in Hainan. A polyphasic approach was applied to distinguish Colletotrichum species. All 51 isolates were primarily characterized morphologically, and six gene regions, including an internally transcribed space of ribosomal DNA (ITS), chitinsynthase (CHS-1), β-tubulin (TUB), actin (ACT), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and manganese-superoxide (SOD2), were sequenced for each isolate. By combining morphological characterization with multilocus sequence analysis (MLSA) based on the six genes, the fungal isolates were identified, representing three Colletrotrichum species: C. fructicola, C. siamense, and C. cordylinicola. The most predominant species was C. fructicola. In pathogenicity tests on the tea-oil camellia cultivar (Reyan1), all collected isolates were pathogenic on tea-oil camellia leaves and were reisolated from symptomatic leaves. Colletotrichum fructicola was the most aggressive species on the attached leaves. This is the first report of C. cordylinicola affecting tea-oil camellia anthracnose worldwide. These results will improve our understanding of the pathogens and provide important insights on the diagnosis and efficient disease management of tea-oil camellia anthracnose.
Differences in the Responses of Tree-Ring Stable Carbon Isotopes of L. sibirica and P. schrenkiana to Climate in the Eastern Tianshan Mountains
Authors: Jia Yi , Lv Guanghui , Guligena Hailili , Qin Li , Peng Zhengbing , Abudureheman Ruxianguli , Zhang Ruibo
Published: May 2023

The eastern Tianshan Mountains are located in the arid interior of Asia, where tree growth is especially sensitive to climate. The ratio of stable carbon isotopes (δ13C) in the tree rings can provide information on changes in atmospheric CO2 concentrations, water availability, and physiological processes within the tree. In particular, the use of tree-ring δ13C values as a proxy for past atmospheric CO2 concentrations has gained widespread acceptance. In this study, detrended stable carbon isotope chronologies (13Ccorr) of Larix sibirica Ledeb. and Picea schrenkiana Fisch. et Mey was established using tree-ring samples from high elevations in the eastern Tianshan Mountains of Xinjiang, China. The relationships between the tree-ring 13Ccorr and different climatic factors were explored using the correlation function and collinearity analysis. Our results demonstrate that the tree-ring δ13Ccorr of L. sibirica is significantly and negatively correlated with precipitation and relative humidity during the growing season. The main climate factor affecting the stable carbon isotope fractionation of L. sibirica during the growing season is relative humidity during the growing season. The tree-ring δ13Ccorr of P. schrenkiana is significantly and negatively correlated with the mean temperature, mean minimum temperature, precipitation, and vapor pressure deficit from the end of the previous growing season and throughout the current growing season, especially in summer. However, it is significantly and positively correlated with relative humidity, indicating that the relationship between the climate factors and the tree-ring stable carbon isotope fractionation of P. schrenkiana is more complex. Further analysis showed that summer temperature and summer precipitation jointly controlled the tree-ring stable carbon isotope fractionation of P. schrenkiana at a high elevation. This research has important implications for our understanding of past and future climate change, as well as for the development of effective strategies to mitigate and adapt to these changes. This study also contributed to the development of a more in-depth understanding of the effects of climate change on tree growth in extremely arid environments and provided evidence to support effective forest management in arid regions.
Effect of Changes in Throughfall on Soil Respiration in Global Forest Ecosystems: A Meta-Analysis
Authors: Xu Xingkai
Published: May 2023

To date, there has been limited knowledge about how soil carbon dioxide (CO2) emissions from forest ecosystems at a global scale respond to the altered precipitation, and the key influencing mechanisms involved. Thirty-seven studies conducted under throughfall manipulation conditions in forest ecosystems around the globe were selected in this meta-analysis, with a total of 103 paired observations. Experimental categories such as climate types, forest types, soil texture, and the area size of changes in throughfall manipulation were included to qualify the responses of annual soil CO2 emissions to the altered throughfall. The responses of the annual soil CO2 emissions to the altered throughfall would be more sensitive in temperate forests than those in tropical and subtropical forests, probably due to the relatively long residence time of soil carbon (C) and the seasonal freeze–thaw events in temperate forests, as well as the relatively high concentration of non-structural carbohydrates in the belowground part of temperate terrestrial plants. A relatively large positive response of the soil CO2 emissions to the increased throughfall was observed in Mediterranean forests due to small precipitation during the growing season and mostly coarse-textured soils. Besides climate types, the sizes of the effect of the altered throughfall on the soil CO2 emissions (lnRCO2) varied with forest types and soil texture categories. Based on the regression analysis of the lnRCO2 values against the changes in throughfall, the annual soil CO2 emissions in forest ecosystems at a global scale would be increased by 6.9%, provided that the change in annual precipitation was increased by 10%. The results of structural equation modeling analysis indicate that fine root biomass and soil microbial biomass, along with the changes in annual precipitation, would substantially affect the altered throughfall-induced annual soil CO2 emissions in global forest ecosystems. The findings of this meta-analysis highlight that the measurement of soil respiration components, the priming effects of soil organic C decomposition, and C allocation between the aboveground and belowground parts of different tree species under the altered precipitation conditions, deserve more attention in the future.
Power Compensation Strategy and Experiment of Large Seedling Tree Planter Based on Energy Storage Flywheel
Authors: Zhu Binhai , Liu Jiuqing , Yang Chunmei , Qu Wen , Ding Peng
Published: May 2023

The intermittent hole-digging tree-planting machine shows a periodic short-time peak load law in planting operation, and the operation process is “idling” for small loads most of the time, leading to large torque fluctuations in the transmission system, unscientific power matching, and high energy consumption. To solve the above problems, this article proposes to use a series of energy-saving flywheels in the transmission system of the tree planting machine. On the premise of obtaining holes that meet the target young tree planting requirements, the optimal power compensation strategy for the flywheel system of the tree planting machine is studied to reduce torque fluctuations in the power transmission system, use smaller power drive units, and save energy. Firstly, the nonlinear multi-body dynamics simulation model of soil cutting by the hole-digging component is established. The boundary and contact conditions are set to simulate the power consumption of the hole-digging component at three rotating speeds. Based on the simulation results, the flywheel power compensation strategy is discussed, and the torque fluctuation of the flywheel balance system is analyzed. The results showed that the higher the speed, the greater the power consumption. The power value suddenly increased from 17.82 kW (1.28 s) to 27.93 kW (1.43 s) when the speed was 220 r/min. Then, the power value rapidly decreased, and the power consumption presented a short-term peak feature. The transmission system’s maximum input power is determined as 17.82 kW according to the various simulated power consumption characteristics. The part exceeding the power consumption is compensated by the energy storage flywheel. The total compensation energy was 2382.5 J. After the flywheel system was involved, the maximum output power of the tractor power output shaft decreased by 36.2%, and the peak torque decreased from 445.7 N·m to 285.1 N·m. The power consumption obtained from the field test and simulation was similar, but the energy required to overcome peak load was jointly provided by the flywheel and the engine. The actual input power of the power output shaft during the energy release period of the flywheel system was 18.51 kW when the rotating speed of the hole-digging component was 220 r/min, and the relative error with the simulation value was 2.43%. The measured actual speed reduction of the flywheel system was 8.9%. After installing an energy storage flywheel in the transmission system of the tree planting machine, the output power of the power unit can be stabilized. Tree planting machines can be equipped with smaller power units, which can reduce energy consumption and exhaust emissions.
Assessing the Likelihood of Failure Due to Stem Decay Using Different Assessment Techniques
Authors: Okun Ari , Brazee Nicholas J. , Clark James R. , Cunningham-Minnick Michael J. , Burcham Daniel C. , Kane Brian
Published: May 2023

Arborists commonly investigate the extent of stem decay to assess the likelihood of stem failure when conducting tree risk assessments. Studies have shown that: (i) arborists can sometimes judge the extent of internal decay based on external signs; (ii) sophisticated tools can reliably illustrate the extent of internal decay; and (iii) assessing components of tree risk can be highly subjective. We recruited 18 experienced tree risk assessors who held the International Society of Arboriculture’s Tree Risk Assessment Qualification (TRAQ) to assess the likelihood of stem failure due to decay after each of five consecutive assessments on 30 individuals of 2 genera. The five assessment techniques, in stepwise order, were: (1) observing visually, (2) sounding the trunk with a mallet, (3) viewing a scaled diagram of the cross-section that revealed sound and decayed wood ascertained from resistance drilling, (4) viewing sonic and electrical resistance tomograms, and (5) consulting with a peer. For each technique, the assessors assigned two or more likelihood of failure ratings (LoFRs) for at least 83% of trees, which were proportionally greatest after the assessors viewed the tomograms; the proportions did not differ among the other four assessment techniques. Covariates that influenced the distribution of the LoFRs included percent of the cross-section that was decayed, and assessors’ experience using resistance drilling devices and tomography in regular practice. Practitioners should be aware that disagreement on the likelihood of tree failure exists even among experienced arborists.