bluuebunny/crossref_metadata_2025 huggingface dataset:
AddAG-AE: Anomaly Detection in Dynamic Attributed Graph Based on Graph Attention Network and LSTM Autoencoder
Authors: Miao Gongxun , Wu Guohua , Zhang Zhen , Tong Yongjie , Lu Bing
Published: June 2023

Recently, anomaly detection in dynamic networks has received increased attention due to massive network-structured data arising in many fields, such as network security, intelligent transportation systems, and computational biology. However, many existing methods in this area fail to fully leverage all available information from dynamic networks. Additionally, most of these methods are supervised or semi-supervised algorithms that require labeled data, which may not always be feasible in real-world scenarios. In this paper, we propose AddAG-AE, a general dynamic graph anomaly-detection framework that can fuse node attributes and spatiotemporal information to detect anomalies in an unsupervised manner. The framework consists of two main components. The first component is a feature extractor composed of a dual autoencoder, which captures a joint representation of both the network structure and node attributes in a latent space. The second component is an anomaly detector that combines a Long Short-Term Memory AutoEncoder (LSTM-AE) and a predictor, effectively identifying abnormal snapshots among most normal graph snapshots. Compared with baselines, experimental results show that the method proposed has broad applicability and higher robustness on three datasets with different sparsity.
A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model
Authors: Liu Lisang , Guo Kaiqi , Chen Jian , Guo Lin , Ke Chengyang , Liang Jingrun , He Dongwei
Published: June 2023

Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps.
Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach
Authors: Khan Sajjad Ahmad , Lee Hyun Jun , Lim Huhnkuk
Published: June 2023

Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.
Validated Questionnaires in Flow Theory: A Systematic Review
Authors: Rosas David Antonio , Padilla-Zea Natalia , Burgos Daniel
Published: June 2023

Psychological flow has been measured in several areas to analyse to what extent users are engaged in particular tasks, and is relevant in the design of products like software, videogames, and eLearning courses. Although there is an unknown number of questionnaires for evaluating different aspects of psychological flow, the research problem faced in this paper is the analysis of the validity of these questionnaires, since it has only been probed for some of them. Thus, our goal is to synthesize the current evidence regarding validated questionnaires in the English language for psychological flow measurement by conducting a systematic review according to the PRISMA framework. As a result, we found a total number of 34 validated questionnaires to assess flow. The number of their items ranged from 3 to 66, while 63 different dimensions of optimal experiences were taken into consideration. Moreover, the contexts of use differed, including methods to assess flow intensity, prevalence, variations, proneness, metacognitions, in crowds, observed, as dimensions of autotelic personalities, or to differentiate flow from clutch states. As a consequence, this paper facilitates the selection of the questionnaires for research or applied aims, far beyond the classic dichotomy of prevalence–proneness. Moreover, we present a reinterpretation of the nine-dimensional scheme of flow in stages, and recommend future research for engineering and computer science.
Valorization of Corn Cobs for Xylitol and Bioethanol Production through Column Reactor Process
Authors: Antunes Felipe A. F. , Freitas Jade B. F. , Prado Carina A. , Castro-Alonso Maria J. , Diaz-Ruiz Erick , Mera Alain E. , Santos Júlio C. , da Silva Silvio S.
Published: June 2023

Corncobs are a plentiful lignocellulosic material that can be utilized for energy production as well as the generation of other high-value products. Within the modern concept of biorefineries, we present processes conducted in a column reactor for the valorization of corncobs as a substrate for ethanol and xylitol production. In the first step, corncobs were subjected to acid hydrolysis, resulting in a hemicellulosic hydrolysate rich in xylose sugars intended for xylitol production by Candida tropicalis UFMGBX12-a. The YP/S (yield coefficient of product to substrate) and QP (productivity) values were approximately 0.2 g/g and 0.15 g/L·h, respectively, for the assays conducted in the column reactor. Next, the remaining solid portion of cellulignin was used for ethanol production through semi-simultaneous saccharification and fermentation process by Scheffersomyces parashehatae UFMG-HM 52.2. This approach involved an intensified successive process consisting of alkaline pretreatment of cellulignin, followed by enzymatic hydrolysis and fermentative processes conducted in the same reactor without biomass transfer. After obtaining the enzymatic hydrolysate, a QP value of 0.4 g/L·h for ethanol production was observed in the fermentation process conducted in the column reactor. The results demonstrate the potential of corncobs as a carbon source for biomolecules production, utilizing a process conducive to scale-up.
Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control
Authors: Xu Wenya , Li Yanxue , He Guanjie , Xu Yang , Gao Weijun
Published: June 2023

The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.
A Review on Battery Thermal Management for New Energy Vehicles
Authors: Li Wenzhe , Zhou Youhang , Zhang Haonan , Tang Xuan
Published: June 2023

Lithium-ion batteries (LIBs) with relatively high energy density and power density are considered an important energy source for new energy vehicles (NEVs). However, LIBs are highly sensitive to temperature, which makes their thermal management challenging. Developing a high-performance battery thermal management system (BTMS) is crucial for the battery to retain high efficiency and security. Generally, the BTMS is divided into three categories based on the physical properties of the cooling medium, including phase change materials (PCMs), liquid, and air. This paper discusses the effect of temperature on the performance of individual batteries and battery systems, at first. Then, a systematic survey of the state-of-the-art BTMS is presented in terms of liquid-based, PCM-based, and air-based BTMS. To further utilize the heat source of the vehicle, the BTMS integrated with the vehicle thermal management system (VTMS) is discussed. Finally, the challenges and future prospects for BTMS with the ability to cut off the thermal runaway are discussed. The primary aim of this review is to offer some guidelines for the design of safe and effective BTMS for the battery pack of NEVs.
Optimizing Load Frequency Control in Standalone Marine Microgrids Using Meta-Heuristic Techniques
Authors: Alahakoon Sanath , Roy Rajib Baran , Arachchillage Shantha Jayasinghe
Published: June 2023

Integrating renewable resources into the electrical systems of marine vessels achieves the dual goal of diversifying energy resources and reducing greenhouse gas emissions. The presence of intermittent renewable sources and sudden nonlinear load changes can cause frequency deviations in isolated hybrid marine microgrids. To address this issue, the paper proposes a conventional PID (proportional–integral–derivative)-controller-based LFC (load frequency controller) which is optimized by meta-heuristic optimization algorithms, namely, PSO (particle swarm optimization), GWO (grey wolf optimization) and hybrid PSO-GWO. The proposed LFC was designed using transfer functions of various microgrid components, with ITAE (integral time absolute error) and ITSE (integral time square error) serving as performance indices. The proposed LFC’s validation was performed through HIL (hardware-in-loop) real-time simulation using a DS 1104 R&D controller board, with simulation results showing the better performance of the optimized frequency response compared to the nonoptimized LFC controller in terms of rise time, fall time, slew rate and overshoot. The hybrid PSO-GWO algorithm performs better than the other optimization algorithms. The simulation results demonstrate the stability and robustness of the proposed controller. In summary, the proposed PID-controller-based LFC can regulate frequency deviation in standalone hybrid marine microgrids effectively.
Pyrolysis and Extraction of Bark in a Biorefineries Context: A Critical Review
Authors: Şen Umut , Esteves Bruno , Pereira Helena
Published: June 2023

Bark-based biorefineries are estimated to become a trending topic in the coming years, particularly with their adsorbent applications and antioxidant production. While the potential benefits of bark-based biorefineries are well-recognized, it is not known how to develop a bark-based biorefinery considering different unit operations and the potential end products. The characterization and screening of different barks for chemical composition is the initial step in biorefinery development, along with the selection of the relevant conversion processes. This state-of-the-art review provides background knowledge on the pyrolysis and extraction studies of bark as the key conversion operations. The results of recent (2010–2022) bark pyrolysis and extraction studies were critically analyzed for process conditions, product properties, and product yields, as well as a discussion of energy-saving possibilities. A biorefinery scheme was proposed based on these data. The current knowledge gaps were identified and future directions were evaluated, which include the production of charcoals, platform chemicals, and extract profiling for specific applications. The results indicate that barks are particularly rich in hydrophilic extractives with potential antioxidant properties and pyrolysis operations, resulting in functional chars that may be used in value-added applications as adsorbent materials. A biorefinery scheme allows for the production of platform chemicals, antioxidant extracts and biochars from barks while contributing to the reduction of waste and environmental pollution.
Hybrid Machine Learning for Modeling the Relative Permeability Changes in Carbonate Reservoirs under Engineered Water Injection
Authors: Reginato Leonardo Fonseca , Gioria Rafael dos Santos , Sampaio Marcio Augusto
Published: June 2023

Advanced production methods utilize complex fluid iteration mechanisms to provide benefits in their implementation. However, modeling these effects with efficiency or accuracy is always a challenge. Machine Learning (ML) applications, which are fundamentally data-driven, can play a crucial role in this context. Therefore, in this study, we applied a Hybrid Machine Learning (HML) solution to predict petrophysical behaviors during Engineered Water Injection (EWI). This hybrid approach utilizes K-Means and Artificial Neural Network algorithms to predict petrophysical behaviors during EWI. In addition, we applied an optimization process to maximize the Net Present Value (NPV) of a case study, and the results demonstrate that the HML approach outperforms conventional methods by increasing oil production (7.3%) while decreasing the amount of water injected and produced (by 28% and 40%, respectively). Even when the injection price is higher, this method remains profitable. Therefore, our study highlights the potential benefits of utilizing HML solutions for predicting petrophysical behaviors during EWI. This approach can significantly improve the accuracy and efficiency of modeling advanced production methods, which may help the profitability of new and mature oil fields.
Power Quality Issues of Electro-Mobility on Distribution Network—An Overview
Authors: Letha Shimi Sudha , Bollen Math H. J. , Busatto Tatiano , Espin Delgado Angela , Mulenga Enock , Bakhtiari Hamed , Sutaria Jil , Ahmed Kazi Main Uddin , Nakhodchi Naser , Sakar Selçuk , Ravindran Vineetha
Published: June 2023

The journey towards sustainable transportation has significantly increased the grid penetration of electric vehicles (EV) around the world. The connection of EVs to the power grid poses a series of new challenges for network operators, such as network loading, voltage profile perturbation, voltage unbalance, and other power quality issues. This paper presents a coalescence of knowledge on the impact that electro-mobility can impose on the grid, and identifies gaps for further research. Further, the study investigates the impact of electric vehicle charging on the medium-voltage network and low-voltage distribution network, keeping in mind the role of network operators, utilities, and customers. From this, the impacts, challenges, and recommendations are summarized. This paper will be a valuable resource to research entities, industry professionals, and network operators, as a ready reference of all possible power quality challenges posed by electro-mobility on the distribution network.
Microgrids with Model Predictive Control: A Critical Review
Authors: Joshal Karan Singh , Gupta Neeraj
Published: June 2023

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations.
Enhancing Environmental Protection in Oil and Gas Wells through Improved Prediction Method of Cement Slurry Temperature
Authors: Feng Bo , Li Jin , Li Zaoyuan , Wu Xuning , Liu Jian , Huang Sheng , Sun Jinfei
Published: June 2023

Accurate cement slurry temperature prediction is a prerequisite for improving cementing quality and ensuring wellbore integrity and sealing of oil and gas wells. It plays a crucial role in preventing “gas migration” and “sustained casing pressure” problems and reducing environmental pollution. The construction links before the start of cementing are neglected by the existing prediction methods, and thus, it is not reasonable to assume initial temperature conditions. In this paper, a two-dimensional transient temperature field model for cementing is developed and its reasonableness is verified. The distribution of wellbore and formation temperature fields at cementing beginning is calculated. In addition, the influence rules of several factors on the cement slurry circulation temperature are calculated and discussed. The results show that the initial temperature varies significantly and that each factor affects the fluid circulation temperature in different ways and to different degrees. If the circulating wash operation before cementing is considered, the temperature field decreases in the downhole section and increases in the uphole section compared to the assumption that the initial condition is the original formation temperature. By correcting the initial conditions, the accuracy of cement slurry circulation temperature prediction can be improved.
Loads Calculation and Strength Calculation of Landing String during Deepwater Drilling
Authors: Zhao Shaowei , Yang Jin , Jiang Kun , Song Yu , Chen Kejin
Published: June 2023

As an important drilling tool, the landing string will be directly exposed to seawater during surface riserless drilling operations, and it will undergo complex deformation under the action of environmental loads such as ocean currents and waves and the displacement, heave and vibration of the drilling platform. At the same time, with the increase in water depth, the floating weight of drill pipe and casing also increases significantly, and the landing string will bear great tensile loads, which can easily cause accidents. Therefore, in this paper, based on ANSYS software, the finite element analysis models of three stages of the landing string lowering operation were established, and finite element simulations under different combinations of working conditions were carried out. Through theoretical analysis and numerical simulation, the ultimate load and stress distribution of the string under different operating sea conditions were obtained, and the external force requirements for restraining the lateral displacement of the string were proposed according to the existing standards of the industry. Thereby, the recommended safe operating time, safe operating conditions window and the required restraint reaction force of the support mechanism are given for landing string lowering operation throughout the year.
Building a Sustainable Future: How Eco-Friendly Homes Are Driving Local Economic Development in Lisbon Metropolitan Area
Authors: Koengkan Matheus , Fuinhas José Alberto , Oliveira Fernanda Paula , Ursavaş Uğur , Moreno Natália
Published: June 2023

This article explored the impact of eco-friendly houses on economic development in the Lisbon metropolitan area. The study analyzed data from 18 municipalities between 2014 and 2020 using regression analysis with ordinary least squares (OLS) and fixed effects. The results indicate that national policies promoting residential energy efficiency positively impact economic development. Policies like subsidies, loans, and tax relief encourage homeowners to invest in energy-efficient technologies, boosting household disposable income and contributing to economic growth. The study found that an increase in the number of new constructions positively impacts economic development, leading to job creation and increased demand for construction materials and services. Additionally, growing eco-friendly houses can reduce energy consumption, lowering energy costs for homeowners and businesses and ultimately stimulating economic growth. In contrast, increasing the number of non-eco-friendly houses can have negative economic impacts. A robustness check using the method of moments quantile regression (MM-QR) confirmed the results from OLS with fixed effects, providing additional evidence supporting the robustness of the results.
Revisiting Market Power in the Polish Power System
Authors: Kaszyński Przemysław , Komorowska Aleksandra , Kamiński Jacek
Published: June 2023

The consequences of the liberalisation of electricity markets have been widely discussed in the literature emphasising the successes or failures of privatisation and deregulation. While most developed power systems have undergone a form of economic transformation, they still require to be monitored and analysed to assess market power. The Polish power system is an example wherein the potential of market power examined fifteen years ago was summarised as significant. Since then, the transformation process and changes in the ownership structure have taken place. This study focuses on the assessment of the potential of market power in the Polish electricity market. For this purpose, statistics on power companies were collected and processed. Then, structural and behavioural measures were applied, including concentration ratios, the entropy coefficient, the Gini coefficient, the Herfindahl–Hirschman Index (HHI), the Residual Supply Index (RSI), and the Lerner Index. The results reveal that, despite a dynamic increase in renewable capacity, market concentration has increased in recent years, achieving an HHI of 2020.9 in 2021. An increase in the Lerner Index of lignite and hard coal-fired units is also observed, indicating high mark-ups by the key market players. Based on quantitative analysis, policy recommendations are outlined to reduce the negative impact of market power on consumers.
Effect of Various Dusts and Humidity on the Performance of Renewable Energy Modules
Authors: Sher Anis Ahmad , Ahmad Naseem , Sattar Mariyam , Ghafoor Usman , Shah Umer Hameed
Published: June 2023

Due to the depletion of fossil fuels, cost-effective and efficient alternate energy resources are a major topic of research nowadays. Solar energy is one of the renewable energy sources which is under research. The main emphasis is on developing new technologies for harnessing the sun’s energy efficiently. Solar photovoltaic (PV) cells are one such technology that works on the principle of the photovoltaic effect, with the electric output generated by it being directly influenced by the amount of light reaching the surface of the cell. Identification of those parameters, which act as an obstacle between light and the solar cell surface and decrease the efficiency of the cell drastically, is necessary. This research study focuses on the environmental parameters (dust and humidity) that directly influence PV cell performance. Here, experiments were conducted by using different types of dusts (sand, soil, ash) of varying quantities and then finding their effect on PV cell output. The results clearly indicate an adverse effect of quantity of dust on performance of the solar PV cell. Moreover, the relative humidity effect on the PV cell performance was also checked, which shows significant change in efficiency for high relative humidity.
A High-Voltage-Gain DC–DC Boost Converter with Zero-Ripple Input Current for Renewable Applications
Authors: Hidalgo Héctor , Orosco Rodolfo , Huerta Héctor , Vázquez Nimrod , Hernández Claudia , Pinto Sergio
Published: June 2023

Renewable energy sources in DC microgrids require high-performance conversion systems to increase their capacity and reliability. Among other characteristics in conversion systems, the current ripple is a characteristic that must be considered since it affects the performance of PV panels and batteries. In this paper, a high-voltage-gain DC–DC boost converter for performing current ripple elimination that is based on a variable inductor is proposed. The topology is composed of a diode–capacitor voltage multiplier and a modified cascaded boost converter. To achieve voltage regulation, a reduced-order switched model is obtained considering the switched capacitor’s dynamics. To address the inductance variation and external disturbances, the H∞ control theory is adapted to systematically design a robust proportional–integral (PI) controller. Details of the working principles and the sizing of passive components are presented. The simulation and experimental results demonstrate that the input current ripple of the proposed converter can be removed in both transitory and steady states.
Preliminary Design and Study of a Small Modular Chlorine Salt Fast Reactor Cooled by Supercritical Carbon Dioxide
Authors: Peng Minyu , Liu Yafen , Zou Yang , Dai Ye
Published: June 2023

Small modular reactors with power below 300 MW have the advantages of small specific mass, long lifetime, and flexible power supply, and they are suitable for providing power support for small and medium-sized towns with small populations and remote areas without grid coverage. In this paper, a small modular S-CO2-cooled molten salt reactor is proposed, and the design of a 10 MW small modular chlorine salt fast reactor (sm-MCFR) with 20 years of operation without refueling is presented. The neutron feasibility of the S-CO2-cooled small modular chlorine fast reactor is analyzed in terms of neutron energy spectrum, reactivity control, temperature reactivity coefficient, and power distribution. A distinctive feature of the sm-MCFR is the use of chlorine salts with high heavy metal solubility and a hard energy spectrum, allowing the core size to be minimized while maintaining the maximum lifetime. The designed core is about 2.44 m in diameter and 2.24 m in height. Meanwhile, the sm-MCFR uses control drum control as the control system, which can effectively achieve reactivity control without increasing the reactor size. The final optimized sm-MCFR has a negative temperature reactivity coefficient, which is necessary to ensure the safe operation of the reactor.
Impact of Diethyl Ether on Performance and Emission Characteristics of a VCR Diesel Engine Fueled by Dual Biodiesel
Authors: Pillai Sundar Kamalesan , Rajamanickam Udayakumar , Khurana Shashank
Published: June 2023

Biodiesel is widely known as the alternative fuel for the diesel engine, but it comes with its fair share of challenges. To minimize the pertinent drawbacks, fuel additives become an essential tool and help improve renewable fuel’s properties. In this current research, biodiesel is obtained from rice bran oil and cotton seed oil, with diethyl ether as an additive. Diethyl ether is known for good oxygen content, high cetane number, and low viscosity. Experiments were performed on a variable compression ratio engine at a compression ratio of 18:1, and the injection pressure (170 bar, 180 bar, 190 bar and 200 bar) and the injection timing (20, 21, 22 and 23°bTDC) with four different dual biodiesel blends (5%, 10%, 15% and 20% by volume). Different ratios of 1%, 2.5% and 5% of diethyl ether with biodiesel combination were examined in a single-cylinder, four-stroke diesel engine. The engine emission and performance features were discussed at different loads and constant engine speeds. It was observed that using 5% of diethyl ether with biodiesel blends improved brake thermal efficiency, brake-specific fuel consumption and decreased carbon dioxide and oxides of nitrogen emissions. The reduction of oxides of nitrogen emission contributes to biodiesel’s acceptability for the environment’s benefit. This investigation found that diethyl ether, along with dual biodiesel blends, has a better viability in diesel engines.
Numerical-Well-Testing Interpretation of Injection/Falloff Testing for Coalbed Methane Well in Hedong Coalfield
Authors: Fang Shiyue , Zhang Xujing , Li Xinzhan , Chen Yue , He Baiyi , Bao Yuan , Ma Dongmin
Published: June 2023

Numerical well testing is used mostly in oil/gas, geothermal, and coalbed methane injection/falloff well-testing interpretations while few published studies have been presented on how to adjust the models and numerical experiments parameters. Meanwhile, there is no simple and highly applicable evaluation standard on the approximation degree between the simulated and field-measured pressure response. In this paper, seven groups of numerical experiments were conducted to obtain the simulated pressure response. The Pearson correlation coefficients and the grey correlation between the simulated and field-measured pressure response were calculated to evaluate the approximation degree. In homogeneous, stress-independent, multi-layered, heterogeneous and integrated models, the simulated pressure response curves all fit to the field data well at the early and late time of the falloff period. However, the highest approximation degree was only found in the integrated model. Finally, the permeability, initial pressure, skin factor and investigation radius of the tested CBM reservoir were determined. The results show that, to obtain a reliable interpretation result, it is best to give an approximation degree evaluation standard on the approximation degree between the simulated and field-measured pressure response, build an integrated numerical model, and input the correct parameters, such as the effective thickness and the testing fluid viscosity. Otherwise, it will also drop into a pitfall of multi-results. The method we used is very relevant to CBM exploration and safe mining in Hedong coalfield.
Altitudinal Differentiation of Forest Resilience to Drought in a Dryland Mountain
Authors: Li Jie , Gao Xiang , Yan An , Chang Shuhang , Li Qiuran
Published: June 2023

Drought is one of the major climate disasters leading to forest degradation in dryland mountains. Hence, revealing the response of forest resilience to drought is crucial to predict forest succession in dryland mountains under future global warming. Here, we chose the Qilian Mountains as the study area and calculated the recovery time and drought intensity along elevation from 1982 to 2020 using the Leaf Area Index (LAI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the forest resilience to drought was calculated using the area of an exponentially fitted curve between drought intensity and corresponding recovery time. Finally, the dominant climate factors underlying altitude differentiation of forest resilience were analyzed using a random forest (RF) regression model, and correlations were determined based on a generalized additive model (GAM). The results indicate that forests in the elevation range of 2600–3900 m exhibited faster recovery rates and greater resilience compared to those in 1700–2600 m. The attributional analysis shows that altitudinal differentiation of forest resilience to drought was mainly constrained by precipitation with a non-monotonic correlation, and resilience was strongest when monthly precipitation reaches 30 mm. In terms of the occurrence of historical drought events, increased potential evapotranspiration improved resilience in the elevation range of 2600–3900 m and enhanced cloud cover initially enlarged the resilience and then decreased it in the elevation range of 3000–3400 m and 3400–3900 m, with resilience being strongest when cloud cover reached 24% and 33%, respectively. Under future climate change, global warming will further exacerbate the drought impact in arid regions, increasing the risk of primary forest collapse. The results of this study provide a scientific basis for predicting the potential changes in vegetation resilience and developing policies for ecological protection in dryland mountains, and we will take addressing the difficult study of the quantitative effects of tree species on resilience altitude differentiation based on ecosystem scales as our future direction.
Landscape Characteristics in Relation to Ecosystem Services Supply: The Case of a Mediterranean Forest on the Island of Cyprus
Authors: Kefalas George , Lorilla Roxanne Suzette , Xofis Panteleimon , Poirazidis Konstantinos , Eliades Nicolas-George Homer
Published: June 2023

The Mediterranean area is one of the most significantly altered biodiversity hotspots on the Earth’s surface; it has been intensively affected by anthropogenic activity for millennia, forming complex socioecological systems. In parallel, the long history of natural ecological processes and the deep interlinking with human populations led to landscape patterns, such as spatial heterogeneity, that facilitate the provision of essential ecosystem services (ESs). As such, a comprehensive understanding of the underlying factors that influence the supply of ESs is of paramount importance for effective forest management policies that ensure both ecological integrity and human welfare. This study aimed at identifying local specific interactions across three different spatial scales between landscape metrics and ESs using global and geographical random forest models. The findings showed that dense forest cover may have a positive effect on the supply of ESs, such as climate regulation and timber provision. Although landscape heterogeneity is considered among the main facilitators of ecosystem multifunctionality, this did not fully apply for the Marathasa region, as forest homogeneity seems to be linked with provision of multiple services. By assessing under which landscape conditions and characteristics forest ESs thrive, local stakeholders and managers can support effective forest management to ensure the co-occurrence of ESs and societal wellbeing.
In Vitro Mycotoxin Decontamination by Saccharomyces cerevisiae Strains Isolated from Bovine Forage
Authors: Moebus Victor Farias , Pinto Leonardo de Assunção , Köptcke Felipe Braz Nielsen , Keller Kelly Moura , Aronovich Marcos , Keller Luiz Antonio Moura
Published: June 2023

Aflatoxin B1 (AFB1) and Zearalenone (ZEN) are among the most common and important mycotoxin contaminants in agricultural products, with AFB1 comprising a liver carcinogen and ZEN responsible for reproductive dysfunctions. As mycotoxins are heat-stable, their removal is carried out mainly by anti-mycotoxin additives. This includes the yeast Saccharomyces cerevisiae. In this context, this study aimed to evaluate the in vitro detoxification of AFB1 and ZEN at pH 3 and 6 by three S. cerevisiae strains isolated from bovine forage, coded LL74, LL08, and LL83, determining the adsorption and biotransformation capacities of each strain. The yeast were freeze-dried and added, in triplicate, at 0, 0.5, 1.0, 1.5, and 2.0 mg mL−1 to a static gastrointestinal model. Final mycotoxin concentrations were determined by HPLC-FL. The evaluated strains exhibited high mycotoxin adsorption rates (20–55%), especially the LL08 strain, although low biotransformation, both equivalent to a commercial strain. The results indicate that pH does not interfere in AFB1 detoxification, while the use of 2.0 mg mL−1 of the LL83 S. cerevisiae strain led to higher ZEN adsorption at pH 3. The investigated strains indicate the possibility for use in in vivo conditions and high potential for commercial applications, with LL08 as the most promising strain.
Bus Travel Time Prediction Based on the Similarity in Drivers’ Driving Styles
Authors: Yin Zhenzhong , Zhang Bin
Published: June 2023

Providing accurate and real-time bus travel time information is crucial for both passengers and public transportation managers. However, in the traditional bus travel time prediction model, due to the lack of consideration of the influence of different bus drivers’ driving styles on the bus travel time, the prediction result is not ideal. In the traditional bus travel time prediction model, the historical travel data of all drivers in the entire bus line are usually used for training and prediction. Due to great differences in individual driving styles, the eigenvalues of drivers’ driving parameters are widely distributed. Therefore, the prediction accuracy of the model trained by this dataset is low. At the same time, the training time of the model is too long due to the large sample size, making it difficult to provide a timely prediction in practical applications. However, if only the historical dataset of a single driver is used for training and prediction, the amount of training data is too small, and it is also difficult to accurately predict travel time. To solve these problems, this paper proposes a method to predict bus travel times based on the similarity of drivers’ driving styles. Firstly, the historical travel time data of different drivers are clustered, and then the corresponding types of drivers’ historical data are used to predict the travel time, so as to improve the accuracy and speed of the travel time prediction. We evaluated our approach using a real-world bus trajectory dataset collected in Shenyang, China. The experimental results show that the accuracy of the proposed method is 13.4% higher than that of the traditional method.
Cultivation and Nutritional Evaluation of Agaricus bisporus with Tea Residue as Culture Medium
Authors: Wang Zhuochen , Li Mengru , Fan Jundi , Bao Yuting , Chen Qi
Published: June 2023

Different constituents of the cultivation substrate have significant effect on the yield and quality of edible mushrooms. The residue after the extraction of instant tea has exhibited to be biologically significant, and could be used as a substrate for cultivation. This study aimed to investigate the feasibility of tea extraction residue (TER) on button mushroom (Agaricus bisporus) cultivation, as an ingredient in the substrate, and assess the growth status, nutritional values, and sensory characteristics of fruiting body. The results showed that the strains could grow well on the cultivated substrate with 20% addition of TER. The total amount of hydrolyzed amino acids in the fruiting bodies of three TER-based groups (TER accounted for 10%, 20%, and 37.5%, respectively) was higher than that of the control group, and the total amount of essential amino acids was increased by 33.33%, 22.47%, and 9.92% compared with the control group, respectively. In addition, the results of gas chromatography-mass spectrometry (GC-MS) revealed that the addition of TER to the cultivation of substrate significantly enhanced the content of typical mushroom-flavor compounds in button mushroom, such as 1-octen-3-ol, 3-octanol, and 1-octen-3-one. It can be concluded that TER may be an ideal choice for the substrate in commercial cultivation of button mushroom.
Research on the Differences in Phenotypic Traits and Nutritional Composition of Acer Truncatum Bunge Seeds from Various Regions
Authors: Le Xiaona , Zhang Wen , Sun Guotao , Fan Jinshuan , Zhu Mingqiang
Published: June 2023

Acer truncatum Bunge (ATB) is an excellent edible woody oil tree species since it bears a huge amount of fruit and has strong adaptability to be widely cultivated. Selecting an optimal cultivation region for ATB is crucial to improving China’s woody oil industrialization. Chemical analysis, correlation analysis, and affiliation function values were used in the present research to systematically analyze the phenotypic traits, organic compound content, and seed oil chemical composition of the seeds of ATB from nine regions. The average contents of oil, protein, and soluble sugar in ATB seeds were 43.30%, 17.40%, and 4.57%, respectively. Thirteen fatty acids were identified from ATB seed oil, the highest content of which was linoleic acid (37.95%) and nervonic acid content was 5–7%. The maximum content of unsaturated fatty acids in ATB seed oil was 90.09%. Alpha-tocopherol content was up to 80.75 mg/100 g. The degree of variation in seed quality traits (25.96%) was stronger than in morphological traits (14.55%). Compared to environmental factors, the phenotypic traits of seeds contribute more to organic compounds and fatty acids. Combining the values of the indicator affiliation functions, Gilgarang, Tongliao, Inner Mongolia was selected as the optimal source of ATB for fruit applications from nine regions.
Molecular Evidence Supports Five Lineages within Chiropotes (Pitheciidae, Platyrrhini)
Authors: Carneiro Jeferson , Sampaio Iracilda , Silva-Júnior José de S. e , Martins-Junior Antonio , Farias Izeni , Hrbek Tomas , Boubli Jean , Schneider Horacio
Published: June 2023

Pitheciines have unique dental specializations among New World monkeys that allow them to feed on fruits with hard pericarps, thus playing a major role as seed predators. The three extant pitheciine genera, Pithecia, Cacajao and Chiropotes, are all endemic to the Amazon region. Because of the uncertainties about interspecific relationships, we reviewed the systematics and taxonomy of the genus Chiropotes. The phylogenetic analyses were performed based on Maximum Likelihood and Bayesian Inference, while species delimitation analyses were carried out using multispecies coalescent methods. In addition, we estimated genetic distances, divergence time and the probable ancestral distribution of this genus. Our results support five species of Chiropotes that emerged during the Plio-Pleistocene. Biogeographic estimates suggest that the ancestor of the current Chiropotes species occupied the endemism areas from Rondônia and Tapajós. Later, subsequent radiation and founder effects associated with the formation of the Amazonian basins probably determined the speciation events within Chiropotes.
The Relationship between TLR3 rs3775291 Polymorphism and Infectious Diseases: A Meta-Analysis of Case-Control Studies
Authors: Silva Marcos Jessé Abrahão , Silva Caroliny Soares , da Silva Vieira Marcelo Cleyton , dos Santos Pabllo Antonny Silva , Frota Cristiane Cunha , Lima Karla Valéria Batista , Lima Luana Nepomuceno Gondim Costa
Published: June 2023

As the host’s first line of defense against pathogens, Toll-like receptors (TLRs), such as the TLR3, are genes encoding transmembrane receptors of the same name. Depending on their expression, TLRs cause a pro- or anti-inflammatory response. The purpose of the article was to determine whether there is an association between the Toll-like receptor 3 (TLR3) rs3775291 Single Nucleotide Polymorphism—SNP and susceptibility to infections. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and was registered in PROSPERO under the code CRD42023429533. A systematic search for relevant studies was performed using PubMed, Scopus, SciELO, Google Scholar, and Science Direct by the MeSH descriptors and the Boolean Operator “AND”: “Infections”; “TLR3”; “SNP”, between January 2005 and July 2022. Summary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated for genotypic comparison assuming a dominant genetic model (CT + TT vs. CC). A meta-analysis of 18 studies consisting of 3118 cases and 4368 controls found a significant association for risk between the presence of the TLR3 SNP rs3775291 and infections as part of the general analysis (OR = 1.16, 95% CI = 1.04–1.28, p = 0.004). In the subgroups of continents, the SNP had a protective role in Europe for 1044 cases and 1471 controls (OR = 0.83, 95% CI = 0.70–0.99, p = 0.04); however, the Asian (for 1588 patients and 2306 controls) and American (for 486 patients and 591 controls) continents had an increase in infectious risk (OR = 1.37, 95% CI = 1.19–1.58, p < 0.001; OR = 1.42, 95% CI = 1.08–1.86, and p = 0.01, respectively). Heterogeneity between studies was detected (I2 = 58%) but was explained in meta-regression by the subgroup of continents itself and publication bias was not evident. The results of the meta-analysis suggest a significant association between the TLR3 rs3775291 polymorphism and susceptibility to infections. Thus, when analyzing subgroups, the Asian and American continents showed that this SNP confers a higher risk against infections in a dominant genotypic model. Therefore, more studies are necessary to fully elucidate the role of TLR3 rs3775291 in infections.