bluuebunny/crossref_metadata_2025 huggingface dataset:
Whole-Blood Gene Expression Profiles Correlate with Response to Immune Checkpoint Inhibitors in Patients with Metastatic Renal Cell Carcinoma
Authors: Nagumo Yoshiyuki , Kandori Shuya , Kojima Takahiro , Hamada Kazuki , Nitta Satoshi , Chihara Ichiro , Shiga Masanobu , Negoro Hiromitsu , Mathis Bryan J. , Nishiyama Hiroyuki
Published: December 2022

In metastatic renal cell carcinoma (mRCC), the clinical response to immune checkpoint inhibitors (ICIs) is limited in a subset of patients and the need exists to identify non-invasive, blood-based, predictive biomarkers for responses. We performed RNA sequencing using whole-blood samples prospectively collected from 49 patients with mRCC prior to the administration of ipilimumab (IPI) and/or nivolumab (NIVO) to determine whether gene expression profiles were associated with responses. An analysis from 33 mRCC patients with complete responses (n = 5), partial responses (n = 14), and progressive disease (n = 14) showed 460 differentially expressed genes (DEGs) related to immune responses between the responder and non-responder groups with significant differences. A set of 14 genes generated from the initial 460 DEGs accurately classified responders (sensitivity 94.7% and specificity 50.0%) while consensus clustering defined clusters with significantly differing response rates (92.3% and 35.0%). These clustering results were replicated in a cohort featuring 16 additional SD patients (49 total patients): response rates were 95.8% and 48.0%. Collectively, whole-blood gene expression profiles derived from mRCC patients treated with ICIs clearly differed by response and hierarchical clustering using immune response DEGs accurately classified responder patients. These results suggest that such screening may serve as a predictor for ICI responses in mRCC patients.
Fabric Phase Sorptive Extraction Combined with HPLC-UV for the Quantitation of Amphotericin B in Human Urine
Authors: Asimakopoulou Evmorfia , Manousi Natalia , Anaxagorou Nikoleta , Kabir Abuzar , Furton Kenneth G. , Zacharis Constantinos K.
Published: December 2022

Herein, a fabric phase sorptive extraction-based scheme was reported for the determination of amphotericin B in human urine. The developed method allowed the direct extraction of the analyte from the biological matrix with improved selectivity, repeatability and recovery. Due to the membrane’s engineered affinity towards the analyte, extraction equilibrium was achieved in 30 min. Moreover, no additional sample pretreatment was required due to the high permeability of the FPSE membrane and the small volume of eluting solvent required for quantitative back-extraction of the analytes. The hydrophobic sol–gel polydimethylphenylsiloxane (sol–gel PDMDPheS) coated membrane provided the optimum extraction performance. Important parameters that affect the extraction efficiency (such as sample volume, extraction time, membrane size, stirring rate, ion strength, elution solvent and time) were thoroughly investigated. The analyte was separated from the internal standard (nimesulide) and endogenous compounds of the human urine using a gradient elution program. The proposed assay was linear within the range of 0.10–10.0 μg mL−1 while the relative standard deviation of the repeatability (sr) and within-laboratory reproducibility (sR) were less than 12.7% in all cases. The method exhibited good accuracy which varied between 88.1 to 110.3%. The developed method was successfully applied for the monitoring of amphotericin B concentration in human urine.
Nanoporous Ag-Decorated Ag7O8NO3 Micro-Pyramids for Sensitive Surface-Enhanced Raman Scattering Detection
Authors: Guo Linfan , Tang Haibin , Wang Xiujuan , Yuan Yupeng , Zhu Chuhong
Published: December 2022

Porous noble metal nanomaterials can be employed to construct sensitive surface-enhanced Raman scattering (SERS) substrates, because the plasmonic nanopores and nanogaps of the porous materials can provide a larger number of hotspots, and can also serve as containers of analyte molecules. However, the fabrication processes of nanoporous noble metal are generally complicated. Here, a facile method is presented to prepare nanoporous Ag nanoparticles-decorated Ag7O8NO3 micro-pyramids, which are fabricated through the chemical reduction of the electrodeposited Ag7O8NO3 micro-pyramids using NaBH4. The Ag7O8NO3 micro-pyramids are fabricated by electrodeposition by using a simple aqueous solution of AgNO3 as electrolyte. Then, porous Ag-decorated Ag7O8NO3 micro-pyramids are achieved by the chemical reduction of the surface of the electrodeposited Ag7O8NO3 micro-pyramids with NaBH4. The high-density nanopores and nanogaps of the fabricated nanoporous Ag can provide plenty of hot spots for Raman enhancement. Additionally, the nanopores have an effective capacity to trap and enrich analytes. Using rhodamine 6G (R6G) as a probe molecule, the SERS performance of the fabricated SERS substrate has been investigated. It is found that a limit of detection (LOD) ~1.0 × 10−15 M can be achieved for R6G. Then, the SERS substrates are employed to detect dye molecule (crystal violet) and pesticide (thiram), and their LODs are calculated down to 9.6 × 10−13 M and 1.3 × 10−15 M, respectively. The enhancement factor of the fabricated SERS substrate is estimated to be as high as 5.6 × 108. Therefore, the nanoporous Ag-decorated Ag7O8NO3 micro-pyramids have shown promising application in the sensitive SERS detection of organic molecules.
A CMOS Voltage Reference with Output Voltage Doubling Using Modified 2T Topology
Authors: Li Junyao , Chan Pak Kwong
Published: December 2022

This paper presents an ultra-low power CMOS voltage reference which operates in the subthreshold region. Modified from the conventional 2T circuit, the proposed circuit is capable of generating higher output voltage by using the resistor subdivision. The design comprises a negative-threshold native NMOS transistor as the current generator, a high-threshold PMOS transistor as the active load and an active voltage doubling network to generate the reference voltage. Implemented in TSMC 40 nm CMOS technology, the proposed circuit operates at a minimum supply of 0.65 V and consumes 5.5 nA. Under one sample simulation, the obtained T.C. is 16.64 ppm/°C and the nominal Vref is 489.6 mV (75.3% of  Vddmin) for the temperature range from −20 °C to 80 °C. For Monte-Carlo simulation of 200 samples at room temperature, the average output voltage is 488 mV and the average T.C. is 29.6 ppm/°C whilst with the standard deviation of 13.26 ppm/°C. Finally, at room temperature, the proposed voltage reference has achieved a process sensitivity (σ/μ) of 3.9%, a line sensitivity of 0.51%/V and a power supply rejection of −45.5 dB and −76.3 dB at 100 kHz and 100 MHz. Compared to the representative prior-art works realized in the same technology and a similar supply current, the proposed circuit has offered the best 1-sampe T.C., the best average T.C. in multiple samples, the highest output voltage, the maximum output voltage per minimum supply voltage and the lowest process sensitivity in the output, Vref.
Genome-Wide Identification of Brassica napus PEN1-LIKE Genes and Their Expression Profiling in Insect-Susceptible and Resistant Cultivars
Authors: Sheng Lei , Feng Zengbei , Hao Zhongping , Hou Shumin
Published: December 2022

Recently, it has been reported that a gene (PEN1) in Arabidopsis thaliana is highly resistant to Plutella xylostella. We screened all the homologous genes of PEN1 in Arabidopsis thaliana and found that the motif of these genes was very conserved. At present, few insect resistance genes have been identified and characterized in Brassica napus. Therefore, we screened all the homologous genes containing this motif in the Brassica napus genome and systematically analyzed the basic information, conserved domain, evolutionary relationship, chromosomal localization and expression analysis of these genes. In this study, 12 PEN1 homologous genes were identified in the Brassica napus genome, which is more than the number in Arabidopsis thaliana. These genes are unevenly distributed on the 12 chromosomes in Brassica napus. Furthermore, all the PEN1 homologous genes contained light responsiveness elements, and most of the genes contained gibberellin-responsive elements, meJA-responsive elements and abscisic-acid-responsive elements. The results will provide a theoretical basis for screening insect resistance genes from the genome of Brassica napus and analyzing the molecular mechanism of insect resistance in Brassica napus.
Volatile Organic Compounds (VOCs) Diversity in the Orchid Himantoglossum robertianum (Loisel.) P. Delforge from Sardinia (Italy)
Authors: De Agostini Antonio , Robustelli della Cuna Francesco Saverio , Cortis Pierluigi , Cogoni Annalena , Sottani Cristina , Soddu Francesca , Sanna Cinzia
Published: December 2022

Volatile Organic Compounds (VOCs) are produced by plants to address a variety of physiological and ecological tasks (among others, stress resistance, and pollinator attraction). Genetics is a key factor in determining plants’ VOCs content and emission, nevertheless, environment strongly influences VOCs profiles in plants. Orchids are a widespread group of plants that colonize diverse environments and rely on complex and refined pollination mechanisms to reproduce. Orchids VOCs are rarely studied and discussed in relation to growing conditions. In the present study, we compare the volatile profiles of inflorescences of Himantoglossum robertianum (Loisel.) P. Delforge sampled in six ecologically diverse populations on Sardinia Island (Italy). The essential oils obtained by steam distillation were characterized by GC-FID and GC-MS analysis. A total of 79 compounds were detected, belonging to the chemical classes of saturated hydrocarbons, esters, alcohols, ketones, unsaturated hydrocarbons, sesquiterpenes, oxygenated terpenes, terpenes, acids, and aldehydes. Multivariate statistics separated H. robertianum populations based on their chemical profiles. Differences were positively linked to the distance separating populations and reflected climatological features of the sampling sites. Interestingly, our results differed from those available in the literature, pointing out the high variability of VOCs profiles in this food-deceptive orchid.
The Utilityof 28S rDNA for Barcoding of Freshwater Sponges (Porifera, Spongillida)
Authors: Itskovich Valeria , Kaluzhnaya Oxana , Glyzina Olga
Published: December 2022

Sponges (Porifera, Spongillida) make up the bulk of the benthic biomass in Lake Baikal and are represented by the family Lubomirskiidae, a collection of endemic species, and several species of the cosmopolitan family Spongillidae. We conducted an analysis of the D3 domain of the 28S rDNA of 16 freshwater sponge species. Based on molecular data, we were able to identify all of the collected Spongillidae specimens whose identification was difficult due to the lack of gemmules. Phylogenetic trees have shown that Ephydatia muelleri, Spongilla lacustris, and Eunapius fragilis formed monophyletic clades, and the D3 domain of the 28S rDNA can be used for their DNA barcoding. For the Baikal sponges, the use of this marker is important since the gemmule-less Spongillidae and Lubomirskiidae are, in some cases, indistinguishable from each other in morphology. The 28S rDNA has been shown to be useful for family and species-level identification of freshwater sponges within the Spongillida.
Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals
Authors: Li Qi , Liu Yunqing , Liu Quanyang , Zhang Qiong , Yan Fei , Ma Yimin , Zhang Xinyu
Published: December 2022

As a major daily task for the popularization of artificial intelligence technology, more and more attention has been paid to the scientific research of mental state electroencephalogram (EEG) in recent years. To retain the spatial information of EEG signals and fully mine the EEG timing-related information, this paper proposes a novel EEG emotion recognition method. First, to obtain the frequency, spatial, and temporal information of multichannel EEG signals more comprehensively, we choose the multidimensional feature structure as the input of the artificial neural network. Then, a neural network model based on depthwise separable convolution is proposed, extracting the input structure’s frequency and spatial features. The network can effectively reduce the computational parameters. Finally, we modeled using the ordered neuronal long short-term memory (ON-LSTM) network, which can automatically learn hierarchical information to extract deep emotional features hidden in EEG time series. The experimental results show that the proposed model can reasonably learn the correlation and temporal dimension information content between EEG multi-channel and improve emotion classification performance. We performed the experimental validation of this paper in two publicly available EEG emotional datasets. In the experiments on the DEAP dataset (a dataset for emotion analysis using EEG, physiological, and video signals), the mean accuracy of emotion recognition for arousal and valence is 95.02% and 94.61%, respectively. In the experiments on the SEED dataset (a dataset collection for various purposes using EEG signals), the average accuracy of emotion recognition is 95.49%.
Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression
Authors: Ahmed Syed Ejaz , Aydın Dursun , Yılmaz Ersin
Published: December 2022

This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model. In this context, the methods used to obtain the estimators are ridge, lasso, adaptive lasso, SCAD, MCP, and elasticnet penalty functions. The most important contribution that distinguishes this article from its peers is that it uses the local polynomial method as a smoothing method. The theoretical estimation procedures for the obtained estimators are explained. In addition, a simulation study is performed to see the behavior of the estimators and make a detailed comparison, and hepatocellular carcinoma data are estimated as a real data example. As a result of the study, the estimators based on adaptive lasso and SCAD were more resistant to censorship and outperformed the other four estimators.
Prioritized Hindsight with Dual Buffer for Meta-Reinforcement Learning
Authors: Beyene Sofanit Wubeshet , Han Ji-Hyeong
Published: December 2022

Sharing prior knowledge across multiple robotic manipulation tasks is a challenging research topic. Although the state-of-the-art deep reinforcement learning (DRL) algorithms have shown immense success in single robotic tasks, it is still challenging to extend these algorithms to be applied directly to resolve multi-task manipulation problems. This is mostly due to the problems associated with efficient exploration in high-dimensional state and continuous action spaces. Furthermore, in multi-task scenarios, the problem of sparse reward and sample inefficiency of DRL algorithms is exacerbated. Therefore, we propose a method to increase the sample efficiency of the soft actor-critic (SAC) algorithm and extend it to a multi-task setting. The agent learns a prior policy from two structurally similar tasks and adapts the policy to a target task. We propose a prioritized hindsight with dual experience replay to improve the data storage and sampling technique, which, in turn, assists the agent in performing structured exploration that leads to sample efficiency. The proposed method separates the experience replay buffer into two buffers to contain real trajectories and hindsight trajectories to reduce the bias introduced by the hindsight trajectories in the buffer. Moreover, we utilize high-reward transitions from previous tasks to assist the network in easily adapting to the new task. We demonstrate the proposed method based on several manipulation tasks using a 7-DoF robotic arm in RLBench. The experimental results show that the proposed method outperforms vanilla SAC in both a single-task setting and multi-task setting.
Forecasting of Wind Speed and Power through FFNN and CFNN Using HPSOBA and MHPSO-BAACs Techniques
Authors: Ellahi Manzoor , Usman Muhammad Rehan , Arif Waqas , Usman Hafiz Fuad , Khan Waheed A. , Satrya Gandeva Bayu , Daniel Kamran , Shabbir Noman
Published: December 2022

Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.
Real-Time 3D Object Detection and Classification in Autonomous Driving Environment Using 3D LiDAR and Camera Sensors
Authors: Arikumar K. S. , Deepak Kumar A. , Gadekallu Thippa Reddy , Prathiba Sahaya Beni , Tamilarasi K.
Published: December 2022

The rapid development of Autonomous Vehicles (AVs) increases the requirement for the accurate prediction of objects in the vicinity to guarantee safer journeys. For effectively predicting objects, sensors such as Three-Dimensional Light Detection and Ranging (3D LiDAR) and cameras can be used. The 3D LiDAR sensor captures the 3D shape of the object and produces point cloud data that describes the geometrical structure of the object. The LiDAR-only detectors may be subject to false detection or even non-detection over objects located at high distances. The camera sensor captures RGB images with sufficient attributes that describe the distinct identification of the object. The high-resolution images produced by the camera sensor benefit the precise classification of the objects. However, hindrances such as the absence of depth information from the images, unstructured point clouds, and cross modalities affect assertion and boil down the environmental perception. To this end, this paper proposes an object detection mechanism that fuses the data received from the camera sensor and the 3D LiDAR sensor (OD-C3DL). The 3D LiDAR sensor obtains point clouds of the object such as distance, position, and geometric shape. The OD-C3DL employs Convolutional Neural Networks (CNN) for further processing point clouds obtained from the 3D LiDAR sensor and the camera sensor to recognize the objects effectively. The point cloud of the LiDAR is enhanced and fused with the image space on the Regions of Interest (ROI) for easy recognition of the objects. The evaluation results show that the OD-C3DL can provide an average of 89 real-time objects for a frame and reduces the extraction time by a recall rate of 94%. The average processing time is 65ms, which makes the OD-C3DL model incredibly suitable for the AVs perception. Furthermore, OD-C3DL provides mean accuracy for identifying automobiles and pedestrians at a moderate degree of difficulty is higher than that of the previous models at 79.13% and 88.76%.
A Data-Efficient Training Method for Deep Reinforcement Learning
Authors: Feng Wenhui , Han Chongzhao , Lian Feng , Liu Xia
Published: December 2022

Data inefficiency is one of the major challenges for deploying deep reinforcement learning algorithms widely in industry control fields, especially in regard to long-horizon sparse reward tasks. Even in a simulation-based environment, it is often prohibitive to take weeks to train an algorithm. In this study, a data-efficient training method is proposed in which a DQN is used as a base algorithm, and an elaborate curriculum is designed for the agent in the simulation scenario to accelerate the training process. In the early stage of the training process, the distribution of the initial state is set close to the goal so the agent can obtain an informative reward easily. As the training continues, the initial state distribution is set farther from the goal for the agent to explore more state space. Thus, the agent can obtain a reasonable policy through fewer interactions with the environment. To bridge the sim-to-real gap, the parameters for the output layer of the neural network for the value function are fine-tuned. An experiment on UAV maneuver control is conducted in the proposed training framework to verify the method. We demonstrate that data efficiency is different for the same data in different training stages.
Valuing Resilience Benefits of Microgrids for an Interconnected Island Distribution System
Authors: Nassif Alexandre B. , Ericson Sean , Abbey Chad , Jeffers Robert , Hotchkiss Eliza , Bahramirad Shay
Published: December 2022

Extreme climate-driven events such as hurricanes, floods, and wildfires are becoming more intense in areas exposed to these threats, requiring approaches to improve the resilience of the electrical infrastructure serving these communities. Long-duration outages caused by such high impact events propagate to economic, health, and social consequences for communities. As essential service providers, electric utilities are mandated to provide safe, economical and reliable electricity to their customers. The public is becoming less tolerant to these more frequent disruptions, especially in view of technological advances that are intended to improve power quality, reliability and resilience. One promising solution is state-of-the-art microgrids and the advanced controls employed therein. This paper presents and demonstrates an approach to technoeconomic analysis that can be used to value the avoided economic consequences of grid resilience investments, as applied to the islands of Vieques and Culebra in Puerto Rico. This valuation methodology can support policies to incorporate resilience value into any investment decision-making process, especially those which serve the public interest.
Low-Carbon Scheduling of Integrated Electricity and Gas Distribution System Considering V2G
Authors: Li Yicheng , Xu Lixiong , Lv Xiangmei , Xiao Yiran
Published: December 2022

With the development of EVs (Electric Vehicles) and the rapidly developing policies on low carbon and environmental protection, electric power systems and natural gas systems become increasingly larger. Under these circumstances, the V2G (Vehicle-to-grid) and the coordinated operation of an integrated electricity–gas distribution system (IEGDS), considering CO2 emissions, can play a part together in the process of energy conservation. Firstly, the V2G model is discussed; this paper presents the cost differences between out-of-order and order for the car. Secondly, the IEGDS model presents coupling constraints of gas turbines and power-to-gas. Lastly, carbon emission is considered in this paper; a carbon capture plant (CCP) captures the CO2 burning by fossil fuel in the power generation process and stores it in a carbon storage tank. This paper also considers trading with the carbon market via a carbon storage warehouse. With the cooperation of various components, a comprehensive model considers the use of V2G to store power in the IEGDS system, with consideration of the carbon trade. Numerical experiments validate the effectiveness of the combination between V2G and IEGDS, considering carbon emissions and carbon trading.
Measurements of Dispersed Phase Velocity in Two-Phase Flows in Pipelines Using Gamma-Absorption Technique and Phase of the Cross-Spectral Density Function
Authors: Hanus Robert , Zych Marcin , Golijanek-Jędrzejczyk Anna
Published: December 2022

This paper concerns the application of the gamma radiation absorption method in the measurements of dispersed phase velocity in two-phase flows: liquid–gas flow in a horizontal pipeline and liquid–solid particles in a vertical pipe. Radiometric sets containing two linear 241Am gamma radiation sources and two NaI(Tl) scintillation detectors were used in the research. Due to the stochastic nature of the signals obtained from the scintillation probes, statistical methods were used for their analysis. The linear average velocity of the dispersed phase transportation was calculated using the phase of the cross-spectral density function of the signals registered by the scintillation detectors. It is shown that in the presented cases, the phase method can be more accurate than the most commonly used classical cross-correlation one.
State of the Art in Humidified Gas Turbine Configurations
Authors: Hamdan Al Assaf Anwar , Amhamed Abdulkarem , Fawwaz Alrebei Odi
Published: December 2022

This research investigates the most modern approaches to water treatment and recovery in power plants because of the scarcity of water sources and the significance of those sources in enhancing the performance of power-generating cycles. Gas turbines, which use mixes of air and water as the working fluid, provide superior efficiency, high specific power outputs, and reduced investment costs compared to combined cycles. Several different cycles for humidified gas turbines, including cycles of direct water injection, cycles of steam injection, and evaporative cycles that include humidity control towers, have been proposed. Despite this, only a few of these cycles have been put into practice, and even fewer are available for purchase on the market. This work aims to analyze the research and development literature on humidification-based gas turbines and highlight the cycles that have the most significant promise for the long run. In addition, work on development that still has to be carried out in order to deploy humidification-based gas turbine cycles is advised. This article may also be used as an overview of the research and development work that has taken place on humidification-based gas turbines over the course of the last thirty years.
Unsteady Study on the Influence of the Angle of Attack of the Blade on the Stall of the Impeller of the Double-Suction Centrifugal Pump
Authors: Wang Hao , Li Yibin , Kong Yunshan , Zhang Shengfu , Niu Teng
Published: December 2022

In order to clearly show the influence on the rotating stall of the impeller of a double-suction centrifugal pump, this paper, using the numerical simulation method of Shear Stress Transform (SST), analyzes the effects of different inlet angles of the blade on hydraulic performance, internal flow field and pressure pulsation in the impeller. The results show that the small angle of attack of the blade inlet scheme can effectively suppress the impeller rotation stall and that the design point head and efficiency are increased by 6.4% and 5.7% respectively. This paper, using turbulence intensity to determine the generation of rotating stall, proposed that the average of turbulence intensity exceeding 2% is a necessary condition for the generation of rotating stall and discovered that the standard deviation of the big angle of attack of the scheme is always greater than that of the small angle being analyzed by the impeller pressure pulsation. The basic critical frequencies of blade inlet pressure pulsation with components of a low frequency is dominated by the impeller rotating frequency F0 and its harmonic frequencies 2F0, and 3F0, but the basic critical frequencies of blade outlet pressure pulsation is governed by Blade Passing Frequency (BPF). The research results can provide some theoretical support for stall research and hydraulic performance optimization of a double-suction pump.
Topic Taxonomy and Metadata to Support Renewable Energy Digitalisation
Authors: Michiorri Andrea , Sempreviva Anna Maria , Philipp Sean , Perez-Lopez Paula , Ferriere Alain , Moser David
Published: December 2022

Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i.e., datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.e., missing standards for data formats; second, data owners having a negative perception of sharing data. To make data ready for data science exploitation, one of the necessary steps to map the existing data and their availability to facilitate their access is to create a taxonomy for the field’s topics. For this, a group of experts in different renewable technologies such as photovoltaics, wind and concentrated solar power and in transversal fields such as life cycle assessment and the EU taxonomy for sustainable activities have been gathered to propose a coherent and detailed taxonomy for renewable energy-related data. The result is a coherent classification of relevant data sources, considering both the general aspects applicable to electricity generation from selected renewable energy technologies and the specific aspects of each of them. It is based on previous relevant work and can be easily extended to other renewable resources not considered in this work and conventional energy technology.
Research on the Modification of Coal Adaptability and Carbon Emissions Reduction Technology for Coal-Fired Boilers
Authors: Peng Dan , Wang Ben , Cai Xingfei , Bei Lei
Published: December 2022

In order to solve the problems of the high temperature of flue gas, low boiler efficiency, and the high concentration of nitrogen oxide (NOx) emissions for a 330 MW boiler fired with lean coal in a power plant, an adaptation modification by using different type of coals in the power generation unit (including pulverizing system, burners, heating surface, and so on) was carried out. The performances of boilers were tested under different combustion conditions before and after the modification. The results of the test show that the volatile content is higher and easy to burn out, and the combustible content of fly ash and slag are greatly reduced after the change in coal type (while lean coal is changed into bituminous coal). At the same time, the low-temperature economizer can greatly reduce the flue gas temperature, thus increasing the efficiency from 90.36% (lean coal, corrected) to 92.71% (bituminous coal). After the change in coal type (lean coal to bituminous coal) and the shift to low-nitrogen combustion (using low-nitrogen burner and OFA technology), the flame temperature in the main combustion area of the boiler decreases, the thermal-type NOx is reduced, and the volatile content of bituminous coal is higher in the anoxic atmosphere of the main combustion zone where the excess air coefficient is small. The intermediate reductive products tend to produce more, which can restrain and reduce NOx. Therefore, the concentration of NOx emissions can be greatly reduced. NOx average emissions at the economizer outlet decreased by 68%, from 864 mg/Nm3 to 279.4 mg/Nm3. A low-temperature economizer uses waste heat to heat feed water, which reduces coal consumption by about 1.32 g/(kW·h). The coal consumption for power supply after modification is reduced by 9.83 g/(kW·h) and the annual energy saving is 16,776 tons of standard coal, while the total carbon dioxide emissions reduction is 41,213.60 tons after the unit modification.
Comparative Analysis of Energy Storage Methods for Energy Systems and Complexes
Authors: Rogalev Nikolay , Rogalev Andrey , Kindra Vladimir , Naumov Vladimir , Maksimov Igor
Published: December 2022

The daily non-uniform power demand is a serious problem in power industry. In addition, recent decades show a trend for the transition to renewable power sources, but their power output depends upon weather and daily conditions. These factors determine the urgency of energy accumulation technology research and development. The presence of a wide variety of energy storage mechanisms leads to the need for their classification and comparison as well as a consideration of possible options for their application in modern power units. This paper presents a comparative analysis of energy storage methods for energy systems and complexes. Recommendations are made on the choice of storage technologies for the modern energy industry. The change in the cost of supplied energy at power plants by integrating various energy storage systems is estimated and the technologies for their implementation are considered. It is revealed that in the large-scale power production industry, the most productive accumulation methods for energy systems and complexes are the following: pumped hydroelectric energy storage systems, thermal and thermochemical accumulations, and hydrogen systems. These methods have the best technical and economic characteristics. The resulting recommendations allow for the assessment of the economic and energy effect achieved by integration of storage systems at the stage of designing new power units.
Simulation Study on the Performance of an Enhanced Vapor-Injection Heat-Pump Drying System
Authors: Dong Suiju , Liu Yin , Meng Zhaofeng , Zhai Saina , Hu Ke , Zhang Fan , Zhou Dong
Published: December 2022

The performance of an enhanced vapor-injection heat-pump drying system was designed and theoretically studied in cold areas. According to the simulation findings, the ideal vapor-injection charge of the system ranges from 12.3 to 13.9%, and its ideal intermediate pressure is between 1.278 and 1.498 MPa when the evaporation temperature is above 0 °C. The ideal vapor-injection charge of the system ranges from 13 to 20%, and its optimal intermediate pressure ranges from 1.078 to 1.278 MPa when the evaporation temperature is −15–0 °C. The ideal vapor-injection charge of the system ranges from 20 to 24%, and the intermediate pressure ranges from 0.898 to 1.078 MPa when the evaporation temperature is below −15 °C. The heat and humidity exhausted air source heat-pump drying (HHE–ASHPD) system has higher dehumidification efficiency than the closed heat-pump drying (CHPD) system under the same air temperature, humidity, and volume parameters.
Origin of Calcite Cements in Tight Sandstone Reservoirs of Chang 8 Member of the Yanchang Formation in Zhijing-Ansai Area, Ordos Basin, China
Authors: Zhang Zehan , Xi Kelai , Xin Honggang , Yang Chunming , Zhao Hui , Wang Youcheng , Dan Weidong , Luo Bin
Published: December 2022

Calcite cement is a common type of cementation in tight sandstone reservoirs of the Upper Triassic Yanchang Formation Chang 8 Member in the Zhijing-Ansai area of the Ordos Basin, which has significant influence on reservoir densification and heterogeneity. Calcite cements affect the quality of the reservoir conspicuously. The characteristics and origins of calcite were investigated using a series of approaches from the perspective of petrography and geochemistry, including thin section observation and identification, cathode luminescence, scanning electron microscopy, AMICS analysis, LA-ICP-MS elements analysis, and carbon and oxygen isotopes analysis. The results of all analytical tests indicated that calcite cements can be divided into two types according to their occurrence and origins. Type-I calcite cements mainly occur in sandstone reservoirs near the sandstone–mudstone interface or the sandstone layers adjacent to mudstone. Generally, there is no chlorite coating around them, and they appear dark orange under cathode luminescence. The carbon source of Type-I calcite cements may be related to the compaction and drainage of mudstone. Type-II calcite cements are formed in the early stage, and their carbon source may be related to the compaction and drainage of the adjacent mudstone. Type-II calcite cements are surrounded by chlorite coating primarily present in the interior of fine-grained sandstone, showing as bright yellow under cathode luminescence. Decarboxylation of organic matter in the source rocks may provide a crucial source of carbon for Type-II calcite cements.
Will Climate Change Affect Photovoltaic Performances? A Long-Term Analysis from 1971 to 2100 in Italy
Authors: Matera Nicoletta , Mazzeo Domenico , Baglivo Cristina , Congedo Paolo
Published: December 2022

This work aims to assess the impacts of climate change on photovoltaic (PV) electricity in two Italian cities, with different latitudes and Köppen–Geiger climate classifications. This was undertaken using the recent EURO-CORDEX set of high-resolution climate projections and PV power generation models, implemented on TRNSYS software. Data for two variables (surface air temperature and solar radiation) were analysed over a long period from 1971 to 2100. For future periods, two of the Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) used in the Intergovernmental Panel on Climate Change Fifth Assessment Report were considered. In both RCP scenarios and both locations, it is estimated that the yearly PV energy produced in the future period will not undergo significant variations on average given that the rate of decrease is foreseen almost constant; instead, a slight reduction in the PV energy was detected in the past period. It can be concluded that the PV market in Italy will grow in the next years considering that the reduction in the foreseen PV purchase costs will be also supported by the slight positive effect of climate change on PV manufacturability.
Investigation of Hydrogen Production Performance Using Nanoporous NiCr and NiV Alloys in KBH4 Hydrolysis
Authors: Akkus Meryem Sena
Published: December 2022

Studies of storage and production of hydrogen, which is an alternative to fossil fuels, have been intensified. Hydrogen production from metal borohydrides via catalyst is very attractive because of its advantages, such as controlled production, high hydrogen content, nontoxicity, etc. In this study, the catalytic performances of nanoporous nickel–chromium alloy and nickel–vanadium alloy catalysts prepared with magnetron sputtering in hydrolysis of potassium borohydride, which is a hydrogen storage material, were investigated. Parameters that affected the hydrolysis reaction rate, such as the temperature, the amount of catalyst, and the volume of 0.5 M HCl solution were investigated using response surface methodology. In addition, the prepared catalysts were characterized with XRD and FE-SEM analysis, and the remaining solutions after the reactions were characterized with FE-SEM/EDS analysis. Using response surface methodology, optimum conditions for the maximum hydrogen production rate were determined to be 1.65 g of catalyst, 6% KBH4, 3% NaOH, and 7 mL of 0.5 M HCl at 333 K. Under these conditions, the hydrogen production rates were calculated as 68.9 L·min−1·gcat−1 and 76.5 L·min−1·gcat−1 for NiCr and NiV, respectively.
Transcriptome and Expression Analysis of Genes Related to Regulatory Mechanisms in Holly (Ilex dabieshanensis) under Cold Stress
Authors: Li Huihui , Zhou Ting , Chong Xinran , Lu Xiaoqing , Li Yunlong , Zheng Bingsong , Wang Xiaolong , Chen Hong
Published: December 2022

Ilex dabieshanensis (K. Yao and M. B. Deng) is not only an important economic tree species, but also has the characteristics of evergreens in all seasons, as well as strong cold resistance. In order to understand the molecular mechanism of holly’s response to cold stress, we used transcriptome analysis to identify the main signaling pathways and key genes involved in cold stress. The result showed that 5750 differentially expressed genes (DEGs) were identified under different cold treatment times compared with the control (cold—0 h). The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs showed that seven phytohormone signal transduction were the most highly enriched, including abscisic acid (ABA), ethylene (ET), cytokinin (CK), auxin (IAA), gibberellin (GA), jasmonate (JA), and brassinosteroids (BR). In addition, proline metabolism, arginine metabolism, flavonoid biosynthesis, and anthocyanin biosynthesis were also implicated in response to cold stress. The weighted gene co-expression network analysis (WGCNA) showed that the genes in two modules were significantly up-regulated after 12 h and 24 h treatments, suggesting these two module genes may participate in the cold stress. The gene ontology (GO) results of the two module genes showed that calcium, scavenging reactive oxygen species, and nitric oxide might act as signaling molecules to regulate cold tolerance in holly. By calculating the connectivity and function prediction of genes in the two modules, five genes (evm.TU.CHR2.244, evm.TU.CHR1.1507, evm.TU.CHR1.1821, evm.TU.CHR2.89, and evm.TU.CHR2.210) were identified as the key hub genes of I. dabieshanensis response to cold stress. These results provided candidate genes and clues for further studies on the molecular genetic mechanism of cold stress in holly.
Social and Economic Importance of Forest Companies in Asian Russia: Evidence from Corporate Financial Reporting
Authors: Gordeev Roman V. , Pyzhev Anton I.
Published: December 2022

In contrast with many other sectors of the Russian economy, there is low market concentration in the forest industry and, consequently, a large number of relatively small enterprises scattered throughout the world’s largest country. In many cases, logging or woodworking companies are the only or key employers in sparsely populated areas, making them important sources of the social and economic stability of small towns and rural settlements. In 2022, Russian forest companies faced dramatic barriers to international trade, which led to the suspension of production with the risk of further layoffs. Thus, the issue of social and economic importance of the forest business in Russia has gained additional sounding. This paper aims to estimate the decline in revenues and the number of employees in forestry companies in Asian Russia because of sanctions. Based on corporate accounting reports, we have generated a dataset covering 4675 forest industry companies in Asian Russia. We use quantile regression to estimate the impact of the number of employees on revenue. All companies were divided into quartiles by revenue and into 6 groups by type of economic activity. A significant differentiation of the return on the number of employees depending on the type of activity and the volume of firms’ revenues was found. Estimates of potential losses of companies during labor force reduction were obtained, which would be 1.2%–3.6% of revenue for a company from Q1, 2.2%–6.6% of revenue for Q2 and 2.7%–8.1% of revenue for Q3. The results clearly demonstrate that forest companies might be very interested in retaining a workforce, even if an opportunistic drop in product demand creates a financial shortfall. Policy makers should take this into account when shaping instruments to support the industry.
Exogenous Calcium Improves Photosynthetic Capacity of Pinus sylvestris var. mongolica under Drought
Authors: Li Yanan , Fang Anqi , Zhang Tengzi , Zhang Songzhu , Zhu Wenxu , Zhou Yongbin
Published: December 2022

Calcium (Ca), a secondary messenger, plays an essential role in improving drought resistance. We used the Fast Chlorophyll Fluorescence Induction Dynamics technique to investigate the effects of exogenous calcium on electron transport and energy fluxes in an 8-year-old Mongolian pine to investigate the mechanism of action of Ca in regulating drought adaptation in Pinus sylvestris var. mongolica. We found water stress significantly decreased Pn and Gs, but exogenous calcium significantly improved photosynthesis under water stress. The chlorophyll a fluorescence transient (OJIP) analysis revealed that water stress increased Fo and decreased Fm, inactivating reaction centers. Water stress reduced VI and VJ while increasing Mo, destroying the electron transport chain. Exogenous calcium increased Sm while decreasing VI and Mo under water stress, enhancing electron transport from QA to QB. Furthermore, 5 mM Ca2+ increased I-P phase and ψPo, δRo, and φRo, decreasing the drought-induced reduction in electron accepters of PSⅠ. The increase in ABS/RC, TRo/RC, ETo/RC, and DIo/RC caused by 5 mM Ca2+ demonstrated that calcium can regulate photoprotection to promote photosynthetic activity. Thus, exogenous calcium alleviated drought-induced reductions in photosynthetic activity by regulating photoprotection and boosting the electron transport efficiency at the acceptor side of PSⅡ and PSⅠ.
Assessment of Variability among Humus Forms and Soil Properties in Relation to Tree Species and Forest Operations in the Kheyrud Forest, Nowshahr
Authors: Sohrabi Hadi , Jourgholami Meghdad , Venanzi Rachele , Picchio Rodolfo
Published: December 2022

Tree species and litter play a key role in the functioning of forest ecosystems as influential factors affecting soil ecological processes and the distribution of humus forms. This study investigates the morphology of humus under the litter of different trees on compacted skid trails at different time intervals after skidding operations (6, 10, and 20 years) in the Kheyrud forest, Nowshahr. Each skid trail was identified with three replications in pure beech, beech-hornbeam and mixed beech stands and a total of 81 profiles with dimensions of 30 × 30 cm, drilled and classified using the European Humus Group Proposal (EHGP). According to the results, the mull of the most dominant humus was under the mixed beech litter, while in the pure beech litter the amphi humus was the most abundant form of humus. Three types of humus mull, moder, and amphi were observed in the skid trail under the beech-hornbeam litter. Oligomull was the most abundant form of humus mull in the skid trail under mixed beech litter, while in the trails under pure beech Eumacroamphi, Eumesoamphi, and Hemimoder were the most common forms of humus amphi and moder. Also, the thickness of the organic layer (Organic litter, Organic fragmentation, and Organic humus) was higher in all skid trails under the pure beech litter as compared to other treatments, while from younger trails (6 years) to older trails (20 years), the thickness of the organic-mineral horizon (Ah) increased under the beech mixed litter. We can conclude that different litter types were the main factor to control the presence of different forms of humus after forest operations.