bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Self-Adaptive Rectifier with High Efficiency and Wide Input Power Range
Authors: Lai Shimiao , Zhang Zihao , Liu Zhijun , Wang Ge , Zhou Yongjie , Zhu Huacheng , Yang Yang
Published: February 2023

A novel 2.45 GHz self-adaptive rectifier with high efficiency and a wide input power range is proposed in this paper. It consists of a high-power sub-rectifier branch, a low-power sub-rectifier branch, an impedance transform and isolation network (ITIN), and a feedback network. Impedance matching is realized by ITIN for both branches. The proposed design is able to switch between these two branches by the feedback network according to its output voltage level. The rectifier has been simulated, fabricated, and tested. The measured power conversion efficiency (PCE) exceeds 50% over the input power range from 5 to 29 dBm, with a total dynamic range of 24 dB. The input range when PCE exceeds 60% is from 10 dBm to 28 dBm. The maximum efficiency is 75.2% at 26 dBm input power.
A Novel Geometric Parameter Self-Calibration Method for Portable CBCT Systems
Authors: Zhang Jie , He Bing , Yang Zhengwei , Kang Weijie
Published: February 2023

In outdoor environments or environments with space restrictions, it is difficult to transport and use conventional computed tomography (CT) systems. Therefore, there is an urgent need for rapid reconstruction of portable cone-beam CT (CBCT) systems. However, owing to its portability and the characteristics of temporary construction environments, high precision spatial location is difficult to achieve with portable CBCT systems. To overcome these limitations, we propose an iterative self-calibration improvement method with a self-calculated initial value based on the projection relationship and image features. The CT value of an open field image was used as the weight value of the projection data in the subsequent experiments to reduce the nonlinear attenuation of the projection intensity. Subsequently, an initial value was obtained based on the invariance of the rotation axis. Finally, self-calibration was realized iteratively using the reconstructed image. This method overcomes the main problem of the rotation axis invariance calibration algorithm—high similarity between the adjacent positions of symmetrical homogeneous materials. The proposed method not only improves the precision of self-calibration based on the projection relationship, but also reduces the performance cost and solution time of the self-calibration algorithm based on the image features. Thus, it satisfies the precision requirements for self-calibration of portable CBCT systems.
Feature-Selection-Based Attentional-Deconvolution Detector for German Traffic Sign Detection Benchmark
Authors: Chung Junho , Park Sangkyoo , Pae Dongsung , Choi Hyunduck , Lim Myotaeg
Published: February 2023

In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When applying feature selection inside a detection algorithm, the network divides the extracted feature maps after the convolution layer into similar and non similar feature maps. Generally, the feature maps obtained after the convolution layers are the outputs of filters with random weights. Owing to the randomness of the filter, the network obtains various kinds of feature maps with unnecessary components, which degrades the detection performance. However, grouping feature maps with high similarities can increase the relativeness of each feature map, thereby improving the network detection of specific targets from images. Furthermore, the proposed FSADD model has modified sizes of the receptive fields for improved traffic sign detection performance. Many of the available general detection algorithms are unsuitable for the German traffic sign detection benchmark (GTSDB) because of the small sizes of these signs in the images. Experimental comparisons were performed with respect to the GTSDB to show that the proposed FSADD is comparable to the state-of-the-art while detecting 29 kinds of traffic signs with 73.9% accuracy of classification performances.
Deployment of IoT-Based Smart Demand-Side Management System with an Enhanced Degree of User Comfort at an Educational Institution
Authors: Charles Raja S. , Vishnu Dharssini A. C. , Jeslin Drusila Nesmalar J. , Karthick T.
Published: February 2023

Nowadays, the Internet of Things (IoT) has a wide impact on many potential applications. The impact of IoT on performing demand-side management (DSM) in an Indian educational institution has not been researched in depth before. In this research work, an IoT-enabled SDSMS (Smart DSM System) has been deployed with the main objective of minimizing electricity tariff and also to tweak the quality of user comfort. It can be feasible by prioritizing available renewable PV solar energy during peak hours in an Indian educational institution. DSM has been performed using day-ahead load shifting and rescheduling the different classes of institutional loads by applying hybrid BPSOGSA (Binary Particle Swarm Optimization and Gravitational Search Algorithm). The BPSOGSA performance on DSM has been evaluated based on electricity tariff, peak demand range, and PAR and compared with the outcomes of both binary conventional algorithms BPSO and BGSA, respectively. The proposed method enhances the degree of user comfort (DUC) by tripping the operation of non-critical institutional loads. Simulation results obtained using MATLAB corroborate that BPSOGSA outperforms both BPSO and BGSA under both DSM scenarios. Before DSM, Peak demand, PAR, and Electricity tariffs were found to be 1855.47 kW, 4.1286, and $2030.67 while after DSM, they reduced to 1502.24 kW, 3.263, and $1314.40 respectively. This indicates a 35.273% reduction in electricity tariff, a 19.037% scale down in peak demand, and a 20.97% reduction in PAR. Finally, the real-time IoT-based SDSMS hardware is implemented at the Renewable energy laboratory for real monitoring of energy consumption via the Blynk application.
Numerical Research on the Jet-Mixing Mechanism of Convergent Nozzle Excited by a Fluidic Oscillator and an Air Tab
Authors: Li Ming , Lei Zhijun , Deng Hanliu , Ouyang Xiaoqing , Zhang Yanfeng , Lu Xingen , Xu Gang , Zhu Junqiang
Published: February 2023

Unsteady numerical simulations, coupled with the SST (Shear Stress Transport) k-ω turbulence model, were conducted to study the mixing-enhancement characteristics of the excited jet generated by the fluidic oscillator and the air tab in a single channel convergent nozzle with an inlet total pressure of 140–200 kPa. Compared with the steady air-tab jet, the sweeping jet generated by the fluidic oscillator has roughly the same penetration in the main flow, but it can induce streamwise vortices and planar vortices of larger scale and longer duration, which is beneficial to enhance jet mixing efficiency in the range of 1.0 DN (DN represents the diameter of the main nozzle outlet) downstream from the main nozzle. When x > 1.0 DN, the jet mixing is mainly dominated by the shear layer between the main jet and the ambient. As the sweeping jet suppresses the expansion of the main jet, which reduces the contact area between the main jet and the ambient, its mixing efficiency is less than that of the air tab in this region. With the increasing inlet total pressure of the fluidic oscillator, the influence range of the sweeping jet is increased, but its mixing efficiency does not increase significantly. In general, the fluidic oscillator can use a small jet flow (<5%) to achieve a high mixing efficiency (i.e., 60% at x = 2.0 DN) at the expense of low total pressure loss (<2.3%), which indicates that it has good engineering applicability.
A Novel Technique for Fast Ohmic Resistance Measurement to Evaluate the Aging of Lithium-Ion xEVs Batteries
Authors: Sheraz Muhammad , Choi Woojin
Published: February 2023

Lithium-ion batteries are gaining more attention due to the rapid growth of electrical vehicles (EVs). Additionally, the industry is putting a lot of effort into reusing EV batteries in energy storage systems (ESS). The optimal performance of the repurposed battery system is highly dependent on the individual batteries used in it. These batteries need to be similar in terms of battery capacity, state of health (SOH), and remaining useful life (RUL). Therefore, battery grading techniques are expected to play a vital role in this newly emerging industry. There are various methods suggested to evaluate the aging of a battery in terms of capacity, SOH, and RUL. The use of ohmic resistance is one approach, as it varies with the aging of the battery. In order to measure the ohmic resistance, electrochemical impedance spectroscopy (EIS) is used, followed by the curve fitting procedures. In this research a novel method is suggested to measure the ohmic resistance without performing the broadband conventional EIS test and the curve fitting. Since the battery is perturbed for a specified frequency band (1 kHz to 100 Hz) using the linearly distributed phased multi-sine signal, only 1 sec perturbation is required, and the ohmic resistance can be directly calculated by using two impedance values. Thus, the measurement speed is several times faster than that of the conventional EIS methods. Hence, it is a suitable and convenient technique for the mass testing of the batteries. The accuracy and validity of the proposed technique are verified by testing three types of batteries. The percentage difference in the measured ohmic resistance value between the conventional and the proposed technique is less than 0.15% for all the batteries tested.
Cooperation of a Non-Linear Receiver with a Three-Phase Power Grid
Authors: Zajkowski Konrad , Duer Stanisław , Paś Jacek , Pokorádi László
Published: February 2023

This article presents an analysis of electrical parameters in a three-phase circuit characterized by the generation of harmonics. The Currents Physical Components (CPC) power theory for three-phase circuits were used. Relationships for three-wire circuits were used, and mathematical relationships were determined to enable decomposition into those components that depend on the direction of energy flow and the reasons for their creation. A calculation example using the previously determined dependencies was presented, and the results of the calculations were discussed. When mathematical analyses are required in circuits with non-linear receivers, and in particular, when there is a cooperation of several such receivers with a common power grid, the calculation concept presented is important. The generation of identical harmonic orders by several receivers causes a disturbance in the direction of energy flow in the power grid. For this reason, the case of a non-linear receiver generating harmonics of orders that has existed before in the power grid seems very interesting. Determining the value of individual powers can be used to estimate the impact of individual receivers on the quality of electricity.
Research on State-of-Health Estimation for Lithium-Ion Batteries Based on the Charging Phase
Authors: Du Changqing , Qi Rui , Ren Zhong , Xiao Di
Published: February 2023

The lithium-ion battery state of health (SOH) estimation is an essential parameter to ensure the safety and stability of the life cycle of electric vehicles. Accurate SOH estimation has been an industry puzzle and a hot topic in academia. To solve the problem of low fitting accuracy of lithium-ion battery SOH estimation in a traditional neural network, a nonlinear autoregressive with exogenous input (NARX) neural network is proposed based on the charging stage. Firstly, six health factors related to the lithium-ion battery aging state are acquired at the charging stage because the charging process has better applicability and simplicity than the discharging process in actual operation. Then six health factors are pre-processed using the principal component analysis (PCA) method. The principal component of the input variable is selected as the input of the neural network, which reduces the dimension of input compared with the neural network model without principal component analysis. The correlation between the inputs is eliminated. To verify the rationality of the proposed algorithm, two public aging datasets are used to develop and validate it. Moreover, the proposed PCA-NARX method is compared with the other two neural networks. The simulation results show that the proposed method can achieve accurate SOH estimation for different types of lithium-ion batteries under different conditions. The average mean absolute error (MAE) and root mean square error (RMSE) are 0.68% and 0.94%, respectively. Compared with other neural networks, the prediction error is reduced by more than 50% on average, which demonstrates the effectiveness of the proposed SOH estimation method.
Robust Finite Control-Set Model Predictive Control for Power Quality Enhancement of a Wind System Based on the DFIG Generator
Authors: Alami Houda El , Bossoufi Badre , Mahfoud Mohammed El , Bouderbala Manale , Majout Btissam , Skruch Paweł , Mobayen Saleh
Published: February 2023

For many academics, it has proven difficult to operate a wind energy conversion system (WECS) under changeable wind speed while also enhancing the quality of the electricity delivered to the grid. In order to increase the effectiveness and performance of the DFIG-based Wind Energy Conversion System, this research suggests an updated model predictive control technique. This study intends to regulate the generator in two ways: first, to follow the reference wind speed with high precision using the rotor side and grid side converters; second, to reduce system error. The suggested approach optimizes a value function with current magnitude errors based on the discrete mathematical model to forecast the converter’s switching state. In this system, the converter switching states are used directly as control inputs. Thus, the converter may be immediately subjected to improved control action. The key advantage of the suggested strategy over current FCS-MPC methods is error reduction. The originality of this research is in the proposal of a cost function that allows for both successful results and computation time minimization. To achieve this, the system is first presented, followed by a description of the predictive control, and then this method is applied to the rotor side control and grid side control. To demonstrate the efficacy and robustness of the suggested technique, a random wind profile was used to examine the system’s performance with a unitary power factor. This was done in order to compare the results with other controls that have been reported in the literature. The simulation results, which were conducted using a 1.5 kW DFIG in the MATLAB/Simulink environment, demonstrate that the FCS-MPC technique is highly effective in terms of speed, accuracy, stability, and output current ripple.
Modulus Estimation of Composites with High Porosity, High Particle Volume Fraction, and Particle Eigenstrain: Application to the LIB Active Layer with a Bridged-Particle Mesostructure
Authors: Song Kaituo , Lu Bo , He Yaolong , Song Yicheng , Zhang Junqian
Published: February 2023

Due to the complex mesostructure and components of composite active layers in lithium-ion battery (LIB) electrodes, coupled with the concentration-dependent material properties and eigenstrains, efficiently estimating the effective modulus of the active layers remains a great challenge. In this work, the classic Mori–Tanaka method is found to be unable to estimate the modulus of the active layer. By realizing the importance of the mesostructure feature, a rod-rod model is proposed. The resulting modulus is expressed analytically. It is shown that the rod-rod model can accurately estimate the modulus evolution of the active layer if the material properties of the components and the evolution of volume fractions are known in advance. Moreover, a simplified rod-rod model is also developed to reduce the complexity of the proposed method. By knowing the volume fractions at two arbitrary states of charge and subsequently determining two constants, the simplified model can estimate the modulus efficiently. Considering both its accuracy and its simplicity, the simplified rod-rod model is the most suitable for the estimation. Thus, the methods developed in this work provide a new perspective for analyzing the material properties of composite active layers in LIB electrodes.
Evaluation of Numerical Methods for Predicting the Energy Performance of Windows
Authors: Pavlenko Anatoliy M. , Sadko Karolina
Published: February 2023

Windows are important structural components that determine the energy efficiency of buildings. A significant parameter in windows technology is the overall heat transfer coefficient, U. This paper analyzes the methods of numerical determination of the U-value, including for windows that use passive technologies to improve thermal performance. The analysis was intended to evaluate the heat flux and temperature distribution across glazed surfaces and the accuracy of traditional approaches to the determination of heat loss through window structures. The results were obtained using the heat flux measurement method described in the international standard ISO 9869-1:2014. The paper shows that the non-uniformity of the heat flux density on a window surface can be as high as 60%, which in turn generates an error in the calculations based on stationary heat transfer conditions.
Estimating the Operating Reserve Demand Curve for Efficient Adoption of Renewable Sources in Korea
Authors: Jeon Wooyoung , Mo Jungyoun
Published: February 2023

As the proportions of variable renewable sources (VRSs) such as solar and wind energy increase rapidly in the power system, their uncertainties inevitably undermine power supply reliability and increase the amount of operating reserve resources required to manage the system. However, because operating reserves have the characteristics of a public good and their value is related to the social cost of blackouts, it is difficult to determine their market price efficiently, which leads to inefficiencies in procuring operating reserves. This study estimates the operating reserve demand curve (ORDC) of the Korean power system to provide an effective basis for measuring the proper value and quantity of operating reserves needed to meet the reliability standard. A stochastic dynamic optimization model is applied to incorporate the probabilistic characteristics of VRS and the inter-hour constraint, which is necessary for analyzing load-following reserves. An econometric model and the Monte Carlo simulation method are used to generate the forecast profiles of solar and wind generation. The results indicate that the proper amount of hourly operating reserves needed in 2034 is approximately 4.4 times higher than that in 2020 at the current reserve offer price. The ORDC of 2020 has a price-inelastic shape, whereas the ORDC of 2034 has a price-elastic shape because the reserve requirement varies considerably with its offer price level in the high-VRS penetration case. This variability is due to alternatives, such as VRS curtailment or load shedding, which can replace the reserve requirement. This study also showed that VRS curtailment is an effective balancing resource as an alternative to reserves.
Green and Renewable Energy Innovations: A Comprehensive Bibliometric Analysis
Authors: Mentel Grzegorz , Lewandowska Anna , Berniak-Woźny Justyna , Tarczyński Waldemar
Published: February 2023

Taking into account factors such as unfavorable climate changes, shrinking fossil fuel resources, low energy efficiency, and the pace of population growth, the transformation towards green and renewable energy is one of the most important goals and challenges facing the world. The energy sector is the source of about 75% of global greenhouse gas emissions and energy-related emissions are reaching new record levels. For the energy transition to succeed, innovation at the level of technology, business processes and policies (local, national, and international) are necessary. Therefore, the aim of this article is to analyze the size, structure, and dynamics of research on innovations in the field of green and renewable energy in the last decade in order to identify the main topics and research trends in this field. The authors conducted a bibliometric review based on the PRISMA guidance together with visualization analysis based on the VOSviewer software. For this purpose, the Web of Science Core Collection (WoS CC) database was used, and based on defined inclusion criteria, the authors selected 1144 records for bibliographic analysis. The database was subjected to a performance analysis from the perspective of the number of publications per year, dominant countries, and journals. Further, science mapping was employed to analyze such features of the publications as co-citations, co-occurrences, and bibliometric coupling. Based on the results, gaps in green and renewable energy innovations were identified and issues for future research were defined and recommended.
Design of an Energy Supply and Demand Forecasting System Based on Web Crawler and a Grey Dynamic Model
Authors: Lin Gang , Liang Yanchun , Tavares Adriano
Published: February 2023

An energy supply and demand forecasting system can help decision-makers grasp more comprehensive information, make accurate decisions and even plan a carbon-neutral future when adjusting energy structure, developing alternative energy resources and so on. This paper presents a hierarchical design of an energy supply and demand forecasting system based on web crawler and a grey dynamic model called GM(1,1) which covers all the process of data collection, data analysis and data prediction. It mainly consists of three services, namely Crawler Service (CS), Algorithm Service (AS), Data Service (DS). The architecture of multiple loose coupling services makes the system flexible in more data, and more advanced prediction algorithms for future energy forecasting works. In order to make higher prediction accuracy based on GM(1,1), this paper illustrates some basic enhanced methods and their combinations with adaptable variable weights. An implementation for testing the system was applied, where the model was set up for coal, oil and natural gas separately, and the enhanced GM was better with relative error about 9.18% than original GM on validation data between 2010 and 2020. All results are available for reference on adjusting of energy structure and developing alternative energy resources.
Possibility of Marine Low-Speed Engine Piston Ring Wear Prediction during Real Operational Conditions
Authors: Kamiński Włodzimierz , Michalska-Pożoga Iwona
Published: February 2023

A long-stroke, low-speed marine engine is used as the prime mover of a ship. During the operation of such engines, the excessive wear of the cylinder liners and piston rings frequently occurs. The breakdown of cylinder liners or piston rings is very dangerous for the safety of a vessel, the environment, and the people on board. The reliability of engine components is an extremely important topic, as it influences the efficient operation of the vessel. Therefore, to prevent such undesired events, it is essential that the condition of the cylinder liners and piston rings is frequently assessed. This paper presents research that finds prediction models for the rate of piston ring wear. The compiled prediction models are verified using verification tests. The models can be implemented to evaluate the tendency of piston rings to wear, and can be used to evaluate the quality of cylinder liner lubrication. Our findings will help to obtain the required optimal piston ring wear rates, maintain the good operational condition of the engine, reduce the costs of engine maintenance, and reduce the total consumption of lubricating oil and the emission of noxious substances into the atmosphere. All the mentioned benefits are related to a reduction in the ship’s operational costs and are directly related to energy efficiency.
On the Benefits of Using Metaheuristics in the Hyperparameter Tuning of Deep Learning Models for Energy Load Forecasting
Authors: Bacanin Nebojsa , Stoean Catalin , Zivkovic Miodrag , Rakic Miomir , Strulak-Wójcikiewicz Roma , Stoean Ruxandra
Published: February 2023

An effective energy oversight represents a major concern throughout the world, and the problem has become even more stringent recently. The prediction of energy load and consumption depends on various factors such as temperature, plugged load, etc. The machine learning and deep learning (DL) approaches developed in the last decade provide a very high level of accuracy for various types of applications, including time-series forecasting. Accordingly, the number of prediction models for this task is continuously growing. The current study does not only overview the most recent and relevant DL for energy supply and demand, but it also emphasizes the fact that not many recent methods use parameter tuning for enhancing the results. To fill the abovementioned gap, in the research conducted for the purpose of this manuscript, a canonical and straightforward long short-term memory (LSTM) DL model for electricity load is developed and tuned for multivariate time-series forecasting. One open dataset from Europe is used as a benchmark, and the performance of LSTM models for a one-step-ahead prediction is evaluated. Reported results can be used as a benchmark for hybrid LSTM-optimization approaches for multivariate energy time-series forecasting in power systems. The current work highlights that parameter tuning leads to better results when using metaheuristics for this purpose in all cases: while grid search achieves a coefficient of determination (R2) of 0.9136, the metaheuristic that led to the worst result is still notably better with the corresponding score of 0.9515.
Structural Properties Evolution and Influencing Factors of Global Virtual Water Scarcity Risk Transfer Network
Authors: Dong Gaogao , Zhang Jing , Tian Lixin , Chen Yang , Zhang Mengxi , Nan Ziwei
Published: February 2023

Loss of production due to local water scarcity, i.e., Local Water Scarcity Risk (LWSR), is transferred downstream through international supply chains to distant economies, causing potential economic losses to countries and sectors that do not directly experience actual water scarcity, which is defined as Virtual Water Scarcity Risk (VWSR). Much research has focused on assessing VWSR and characterizing the structure of VWSR transfer networks, without explaining the formation and dynamics of VWSR transfer network patterns. In this study, the global VWSR transfer networks for 2001–2016 are then constructed based on a multi-regional input-output model and complex network theory. The determinants influencing the formation of VWSR transfer networks are further explored using the time-exponential random graph model. The results demonstrate that: (1) The VWSR transfer networks exhibit a distinctly small-world and heterogeneous nature; (2) Asia and Europe are the main targets of VWSR transfers, and Asia is also the main source of risks; (3) China and the USA play a leading role on the import side of VWSR, and India is the largest exporter of VWSR; (4) The evolution of VWSR transfer networks is significantly influenced by transitivity and stability. Countries located on the same continent, sharing geographical borders and having a higher level of economic development, have a facilitating effect on the formation and evolution of VWSR transfer networks. Countries with a higher share of merchandise trade are more inclined to receive VWSR inflows, while the urbanization rate has a restraining effect on VWSR outflows. The study provides a network-based insight that explores the structural evolution of VWSR transfer networks and the determinants of their formation, informing policy makers in developing strategies to mitigate the cascading spread of VWSR.
Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database
Authors: Mądziel Maksymilian , Campisi Tiziana
Published: February 2023

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.
A Photovoltaic System Model Integrating FAIR Digital Objects and Ontologies
Authors: Schweikert Jan , Stucky Karl-Uwe , Süß Wolfgang , Hagenmeyer Veit
Published: February 2023

Smart grids of the future will create and provide huge data volumes, which are subject to FAIR (Findable, Accessible, Interoperable, and Reusable) data management solutions when used within the scientific domain and for operation. FAIR Digital Objects (FDOs) provide access to (meta)data, and ontologies explicitly describe metadata as well as application data objects and domains. The present paper proposes a novel approach to integrate FAIR digital objects and ontologies as metadata models in order to support data access for energy researchers, energy research applications, operational applications and energy information systems. As the first example domain to be modeled using an ontology and to get integrated with FAIR digital objects, a photovoltaic (PV) system model is selected. For the given purpose, a discussion of existing energy ontologies shows the necessity to develop a new PV ontology. By integration of FDOs, this new PV ontology is introduced in the present paper. Furthermore, the concept of FDOs is integrated with the PV ontology in such a way that it allows for generalization. By this, the present paper contributes to a sustainable data management for smart grid operation, especially for interoperability, by using ontologies and, hence, unambiguous semantics. An information system application that visualizes the PV system, its describing data and collected sensor data, is proposed. As a proof of concept the details of the use case implementation are presented.
Experimental and Numerical Analysis of a Low-Cost Solar Still
Authors: da Silva Junior Luis Gonçalves , de Oliveira João Pedro Jenson , Ribeiro Guilherme Borges , Ferreira Pinto Leandro
Published: February 2023

The ability to treat saltwater to make it suitable for human consumption has long been sought by mankind. More than three-quarters of the earth’s surface is covered with saltwater. Although this water is important for some forms of transportation and fishing, it contains too much salt to sustain human life or agricultural activities. The current work consists of building a low-cost solar still and numerically modeling this device to predict the performance of the solar still without using any experimental measurements. The simulated results were compared with the best experimental values obtained from the water-covering temperatures and desalinated water yield under Brazilian climatic conditions (coordinates: 23°26′31.344″ S and 46°27′27.468″ W). The simulation results were in acceptable agreement with the experimental data. The main results obtained indicate that the solar still has greater efficiency when the volume of water is smaller inside the equipment owing to the lower height of the water and when the global radiation has greater intensity. In addition, numerical modeling allows the analysis of the behavior of the volume fraction over time for water and vapor and indicates better performance in water production after 30 min.
Linking Microbial Decomposition to Dissolved Organic Matter Composition in the Revegetation of the Red Soil Erosion Area
Authors: Chen Wenxin , Hu Huaying , Heal Kate , Sohi Saran , Tigabu Mulualem , Qiu Weijuan , Zhou Chuifan
Published: February 2023

Studying the changes and linkages between dissolved organic matter (DOM) and microorganisms in soils during vegetation restoration will help to understand the role of vegetation restoration in soil carbon sequestration and thus improve the understanding of the global soil carbon cycle. Soil DOM molecules were characterized by Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and the results showed that the soil DOM consisted mainly of lignin/carboxylic rich alicyclic molecule (CRAM)-like structures, while the ratios of lipids and aliphatic/protein decreased in sequence with recovery time. Lipids and aliphatic/proteins with high H/C DOM (labile DOM) degrade preferentially, while lignin/CRAM-like structures and tannins with low H/C DOM (recalcitrant DOM) are recalcitrant during vegetation restoration. With the restoration of vegetation, DOM molecules tend to be diversified and complicated, and DOM compounds with low double bond equivalent (DBE), low aromatic, and low alkyl structures will be converted into persistent organic matter with high carbon numbers and high DBE. The diversity of soil microorganisms was determined by high-throughput sequencing. The results showed that the abundance and diversity of soil bacteria increased significantly after revegetation, while the abundance and diversity of soil fungi began to increase when the ecosystem became a more mature coniferous forest. The soil microbial community exhibited complex connectivity and strong interaction with DOM molecules during vegetation restoration. As most of the DOM molecules are recalcitrant, vegetation restoration facilitates C sequestration in the soil, thereby contributing to climate change mitigation.
Perceived Economic Values of Cultural Ecosystem Services in Green and Blue Spaces of 98 Urban Wetland Parks in Jiangxi, China
Authors: Zheng Yu , Zhu Jinli , Wang Shan , Guo Peng
Published: February 2023

Cultural ecosystem services (CES) of urban wetland parks (UWPs) can be priced according to monetary values. Urban green and blue spaces (UGS and UBS, respectively) provide stands of nature in UWPs, wherein visitors’ emotions related to the enjoyment of CES values can be assessed through analyzing the facial expressions of visitors. In this study, a total of 98 UWPs were selected as study stands in Jiangxi, where a total of 1749 photographs showing facial expressions were obtained from Sina Weibo for local visitors experiencing UGS and UBS in 2021. The CES of UBS were evaluated at a widely used price of USD 881 ha−1 yr−1, and those of UGS were evaluated at USD 1583 ha−1 yr−1. The averaged CES values were estimated to be USD 941.26 and 39.54 thousand yr−1 for UGS and UBS per UWP in Jiangxi, respectively. The large number of UGS in an UWP had no relationship with the examined facial expressions; however, areas of UBS and, accordingly, the CES values therein, can both be perceived and exposed as positive emotions. CES in UBS only accounted for lower than 5% of that in a UWP, whereas those in UGS together explained over 95%. Overall, people smiled more when perceiving the values of services in UBS of UWPs than when experiencing UGS.
A Multi-Parametric Investigation on Waterlogged Wood Using a Magnetic Resonance Imaging Clinical Scanner
Authors: Longo Sveva , Egizi Federica , Stagno Valeria , Di Trani Maria Giovanna , Marchelletta Gianni , Gili Tommaso , Fazio Enza , Favero Gabriele , Capuani Silvia
Published: February 2023

In cultural heritage conservation science, moisture content (MC) is an essential factor to determine. At the same time, it is essential to choose non-destructive and non-invasive approaches for more sustainable investigations and make them safe for the environment and the sample. The question addressed in this work concerns the possibility and the opportunity to investigate waterlogged wood by using nuclear magnetic resonance imaging (MRI) clinical scanners to carry out non-destructive volumetric diagnostics. In this study, MRI, the most important non-invasive medical imaging technique for human tissue analysis, was applied to study archaeological waterlogged wood samples. This type of archaeological material has a very high moisture content (400%–800%), thus, it is an ideal investigative subject for MRI which detects water molecules inside matter. By following this methodology, it was possible to obtain information about water content and conservation status through a T1, T2, and T2* weighted image analysis, without any sampling or handling, and the samples were directly scanned in the water where they were stored. Furthermore, it permited processing 3D reconstruction, which could be an innovative tool for the digitalization of marine archaeological collections. In this work, 16 modern species of wood and a waterlogged archaeological wood sample were studied and investigated using a clinical NMR scanner operating at 3T. The results were compared with X-ray computed tomography (CT) images, as they had already been used for dendrochronology. The comparison highlights the similar, different, and complementary information about moisture content and conservation status in an all-in-one methodology obtainable from both MRI and CT techniques.
Genetic Variation among Somatic Embryo Clones of Nordmann Fir Grown as Christmas Trees
Authors: Xu Jing , Nielsen Ulrik Braüner
Published: February 2023

Somatic embryogenesis (SE) cloning techniques for Nordmann fir Christmas tree production have been pursued and refined in Denmark, and three SE clonal trials with 201 SE clones from seeds of 27 Nordmann fir trees and seedlings from bulk harvest seeds in Georgia, Caucasus, were established from 2014 to 2015. This study was the first to evaluate the genetic performance of these SE clones in different sites. In addition, the performance of SE clones and (zygotic) normal seedlings in the field was compared in this study. This study was based on three key groups of traits for Christmas tree production, measured 5 and 6 years after planting: growth, commercial tree quality, and disorder. Seedlings grew faster and had better Christmas tree quality than SE clones. There were significant family effects for all traits, indicating that a good family also produced good clones on average. Growth and disorder traits were under moderate genetic control, with estimated broad-sense heritability (H2) from 0.19 to 0.31 and from 0.20 to 0.28, respectively. Quality traits had low to moderate H2, ranging from 0.09 to 0.24. Significant genotype-by-environment interactions were shown for Christmas tree quality, post-harvest needle retention, and branch angle, suggesting the importance of choosing the right clone at each site. There was no significant interaction in height among the years. Superior clones, compared to the unbred seed source, were identified by combining several important traits. Overall, the somatic clones were well established but had slower growth compared to standard seedlings.
Quantifying the Probability of Decline in Quality: Implications for Selection Management in Northern Hardwood Forests
Authors: Moreau Guillaume , Cecil-Cockwell Malcolm J. L. , Achim Alexis , Bédard Steve , Guillemette François , Caspersen John
Published: February 2023

Northern hardwoods are susceptible to a wide range of defects that can reduce the amount of sound wood with desirable qualities, such as the clear sapwood of sugar maple trees. Yet, the rate at which trees decline in quality due to the development of such defects has never been quantified in northern hardwood forests due to a dearth of repeat inventories that record the appearance of defects over time. As a result, it remains uncertain whether, and how, selection management reduces the probability of decline in quality. In this study, we quantify the rate at which trees decline in quality due to the development of defects, and we test several hypotheses regarding the influence of selection management on quality. Our results show that (1) the probability of decline in quality increases as trees grow larger; (2) crown dieback also increases the probability of decline in quality; (3) the probability of decline in quality is slightly lower in managed stands than in unmanaged stands, and (4) the probability of decline in quality increases with the mean annual temperature of the site. Finally, we combined our estimates of the probability of decline in quality with previous estimates of the probability of mortality to assess the overall risk associated with retaining trees of different species, sizes, and vigour profiles. The resulting metric can inform efforts to improve the management of northern hardwood forests by providing an integrated estimate of the risk that the value of a tree will be reduced, or eliminated, due to mortality or decline in quality.
Performance Assessment and Comparison of Deployment Options for 5G Millimeter Wave Systems
Authors: Mokrov Evgeni , Samouylov Konstantin
Published: February 2023

The roll-outs of fifth-generation (5G) New Radio (NR) systems operating in the millimeter-wave (mmWave) frequency band are essential for satisfying IMT-2020 requirements set forth by ITU-R in terms of the data rate at the access interface. To overcome mmWave-specific propagation phenomena, a number of radio access network densification options have been proposed, including a conventional base station (BS) as well as integrated access and backhaul (IAB) with terrestrial and aerial IAB nodes. The aim of this paper is to qualitatively and quantitatively compare the proposed deployments using coverage, spectral efficiency and BS density as the main metrics of interest. To this end, we develop a model capturing the specifics of various deployment options. Our numerical results demonstrate that, while the implementation of terrestrial relaying nodes potentially improves coverage and spectral efficiency, aerial relays provide the highest coverage, three times that of a direct link connection, and also significantly reduce the required BS density. The main benefit is provided by the link between the BS and the aerial relay. However, gains are highly dependent on a number of elements in antenna arrays and targeted outage probability. The use of terrestrial relays can be considered a natural trade-off between coverage and the aggregate rate.
The Value of Current Ante Mortem Meat Inspection and Food Chain Information of Dairy Cows in Relation to Post Mortem Findings and the Protection of Public Health: A Case for a More Risk-Based Meat Inspection
Authors: Jacobs Pieter , Berends Boyd , Lipman Len
Published: February 2023

In this study, the contribution of the ante mortem (AM) inspection and the food chain information (FCI) to ensuring meat safety and public health was investigated, by evaluating the slaughterhouse findings of 223,600 slaughtered dairy cows in the Netherlands. The outcome of this study was that the ante mortem (AM) and post mortem (PM) inspections have a substantial overlap, and that with regard to food safety and public health in over 99% of cases the PM could even be omitted on the basis of the AM. In this study, the data provided by the dairy farmers on the current FCI forms contributed little to nothing with regard to the outcomes of AM and PM inspection. It is concluded that current meat inspection procedures need an update and a more risk-based approach needs to be adopted. Regarding this, the AM inspection of dairy cattle should remain, because it plays an important role in ensuring food safety (e.g., by preventing contamination of the slaughter line by excessively dirty animals, or animals with abscesses), monitoring animal welfare and in detecting some important notifiable diseases. The PM inspection, however, could in many cases be omitted, provided there is a strict AM inspection complemented by a vastly improved (automated) way of obtaining reliable FCI.
Fractional Probability Theory of Arbitrary Order
Authors: Tarasov Vasily E.
Published: February 2023

A generalization of probability theory is proposed by using the Riemann–Liouville fractional integrals and the Caputo and Riemann–Liouville fractional derivatives of arbitrary (non-integer and integer) orders. The definition of the fractional probability density function (fractional PDF) is proposed. The basic properties of the fractional PDF are proven. The definition of the fractional cumulative distribution function (fractional CDF) is also suggested, and the basic properties of these functions are also proven. It is proven that the proposed fractional cumulative distribution functions generate unique probability spaces that are interpreted as spaces of a fractional probability theory of arbitrary order. Various examples of the distributions of the fractional probability of arbitrary order, which are defined on finite intervals of the real line, are suggested.
A Perspective Review on Understanding Drought Stress Tolerance in Wild Banana Genetic Resources of Northeast India
Authors: Thingnam Surendrakumar , Lourembam Dinamani , Tongbram Punshi , Lokya Vadthya , Tiwari Siddharth , Khan Mohd. , Pandey Anamika , Hamurcu Mehmet , Thangjam Robert
Published: February 2023

The enormous perennial monocotyledonous herb banana (Musa spp.), which includes dessert and cooking varieties, is found in more than 120 countries and is a member of the order Zingiberales and family Musaceae. The production of bananas requires a certain amount of precipitation throughout the year, and its scarcity reduces productivity in rain-fed banana-growing areas due to drought stress. To increase the tolerance of banana crops to drought stress, it is necessary to explore crop wild relatives (CWRs) of banana. Although molecular genetic pathways involved in drought stress tolerance of cultivated banana have been uncovered and understood with the introduction of high-throughput DNA sequencing technology, next-generation sequencing (NGS) techniques, and numerous “omics” tools, unfortunately, such approaches have not been thoroughly implemented to utilize the huge potential of wild genetic resources of banana. In India, the northeastern region has been reported to have the highest diversity and distribution of Musaceae, with more than 30 taxa, 19 of which are unique to the area, accounting for around 81% of all wild species. As a result, the area is regarded as one of the main locations of origin for the Musaceae family. The understanding of the response of the banana genotypes of northeastern India belonging to different genome groups to water deficit stress at the molecular level will be useful for developing and improving drought tolerance in commercial banana cultivars not only in India but also worldwide. Hence, in the present review, we discuss the studies conducted to observe the effect of drought stress on different banana species. Moreover, the article highlights the tools and techniques that have been used or that can be used for exploring and understanding the molecular basis of differentially regulated genes and their networks in different drought stress-tolerant banana genotypes of northeast India, especially wild types, for unraveling their potential novel traits and genes.