bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Entropy Generation in a SCR-DeNOx System with AdBlue Spray Dynamic Using Large Eddy Simulation
Authors: Nishad Kaushal , Agrebi Senda
Published: March 2023

In this work, the entropy generation analysis is extended to the multi-phase fluid flow within a Large Eddy Simulation (LES) framework. The selected study case consists of a generic selective catalytic reduction (SCR) configuration in which the water/AdBlue is injected into a cross-flow of the internal combustion (IC) engine exhaust gas. The adopted numerical modules are first assessed by comparing with experimental data for film thickness in the case of AdBlue injection and then with H2O mass fraction and temperature for water injection case. Subsequently, the impact of heat transfer, fluid flow, phase change, mixing and chemical reaction due to AdBlue injection on the entropy generation is assessed. Hence, the individual contributions of viscous and heat dissipation together with the species mixing, chemical reaction during the thermal decomposition of urea into NH3 and dispersed phase are especially evaluated and analysed. In comparison to the shares of the viscous and mixing processes, the entropy generation is predominated by the heat, chemical and dispersed phase contributions. The influence of the operating parameters such as exhaust gas temperature, flow rate and AdBlue injection on entropy generation is discussed in details. Using a suitable measures, the irreversibility map and some necessary inferences are also provided.
Being with a Puppet: Literacy through Experiencing Puppetry and Drama with Young Children
Authors: Karaolis Olivia
Published: March 2023

Puppets have a long association with early childhood education and have played a much-loved role in children’s learning and development. This paper tells the research story that investigated how the magical creature of a puppet facilitated connection, play, communication, and engagement with children who experience disability. We discovered how puppets can be combined with drama approaches and utilized in group activities for enabling literacy development by early childhood educators. In being with a puppet, adults found new ‘ways’ of supporting all children’s interest, meaning making, and contribution to group learning experiences. Puppets were found to invite children into conversations, to encourage their expression and creativity, in a way that was uniquely their own. Educators found that being with a puppet supported their relationship with the children to one that was more playful and positive as it altered their perception of the children and their ability to recognize their non-verbal communication.
Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel
Authors: Wu Lei , Tang Yong , Zhang Pei , Zhou Ying
Published: March 2023

Estimating Time of Travel (ETT) is a crucial element of intelligent transportation systems. In most previous studies, time of travel is estimated by identifying the spatio-temporal features of road segments or intersections independently. However, due to continuous changes in road segments and intersections in a path, dynamic features should be coupled and interactive. Therefore, employing only road segment or intersection features is inadequate for improving the accuracy of ETT. To address this issue, we proposed a novel deep learning framework for ETT based on a spatio-temporal heterogeneous graph neural network (STHGNN). Specifically, a heterogeneous traffic graph was first created based on intersections and road segments, which implies an adjacency correlation. Next, a learning approach for spatio-temporal heterogeneous convolutional attention networks was proposed to obtain the spatio-temporal correlations of joint intersections and road segments. This approach integrates temporal and spatial features. Finally, a fusion prediction approach was employed to estimate the travel time of a given path. Experiments were conducted on real-world path datasets to evaluate our proposed model. The results showed that STHGNN significantly outperformed the baselines.
A Single-Tree Point Cloud Completion Approach of Feature Fusion for Agricultural Robots
Authors: Xu Dali , Chen Guangsheng , Jing Weipeng
Published: March 2023

With the continuous development of digital agriculture and intelligent forestry, the demand for three-dimensional modeling of trees or plants using agricultural robots is also increasing. Laser radar technology has gradually become an important technical means for agricultural robots to obtain three-dimensional information about trees. When using laser radar to scan trees, incomplete point cloud data are often obtained due to leaf occlusion, visual angle limitation, or operation error, which leads to quality degradation of the subsequent 3D modeling and quantitative analysis of trees. At present, a lot of research work has been carried out in the direction of point cloud completion, in which the deep learning model is the mainstream solution. However, the existing deep learning models have mainly been applied to urban scene completion or the point cloud completion of indoor regularized objects, and the research objects generally have obvious continuity and symmetry characteristics. There has been no relevant research on the point cloud completion method for objects with obvious individual morphological differences, such as trees. Therefore, this paper proposes a single-tree point cloud completion method based on feature fusion. This method uses PointNet, based on point structure, to extract the global features of trees, and EdgeConv, based on graph structure, to extract the local features of trees. After integrating global and local features, FoldingNet is used to realize the generation of a complete point cloud. Compared to other deep learning methods on the open source data set, the CD index using this method increased by 21.772% on average, and the EMD index increased by 15.672% on average, which proves the effectiveness of the method in this paper and provides a new solution for agricultural robots to obtain three-dimensional information about trees.
AQSA: Aspect-Based Quality Sentiment Analysis for Multi-Labeling with Improved ResNet Hybrid Algorithm
Authors: Irfan Muhammad , Ayub Nasir , Ahmed Qazi Arbab , Rahman Saifur , Bashir Muhammad Salman , Nowakowski Grzegorz , Alqhtani Samar M. , Sieja Marek
Published: March 2023

Sentiment analysis (SA) is an area of study currently being investigated in text mining. SA is the computational handling of a text’s views, emotions, subjectivity, and subjective nature. The researchers realized that generating generic sentiment from textual material was inadequate, so they developed SA to extract expressions from textual information. The problem of removing emotional aspects through multi-labeling based on data from certain aspects may be resolved. This article proposes the swarm-based hybrid model residual networks with sand cat swarm optimization (ResNet-SCSO), a novel method for increasing the precision and variation of learning the text with the multi-labeling method. Contrary to existing multi-label training approaches, ResNet-SCSO highlights the diversity and accuracy of methodologies based on multi-labeling. Five distinct datasets were analyzed (movies, research articles, medical, birds, and proteins). To achieve accurate and improved data, we initially used preprocessing. Secondly, we used the GloVe and TF-IDF to extract features. Thirdly, a word association is created using the word2vec method. Additionally, the enhanced data are utilized for training and validating the ResNet model (tuned with SCSO). We tested the accuracy of ResNet-SCSO on research article, medical, birds, movie, and protein images using the aspect-based multi-labeling method. The accuracy was 95%, 96%, 97%, 92%, and 96%, respectively. With multi-label datasets of varying dimensions, our proposed model shows that ResNet-SCSO is significantly better than other commonly used techniques. Experimental findings confirm the implemented strategy’s success compared to existing benchmark methods.
UAV Abnormal State Detection Model Based on Timestamp Slice and Multi-Separable CNN
Authors: Yang Tao , Chen Jiangchuan , Deng Hongli , Lu Yu
Published: March 2023

With the rapid development of UAVs (Unmanned Aerial Vehicles), abnormal state detection has become a critical technology to ensure the flight safety of UAVs. The position and orientation system (POS) data, etc., used to evaluate UAV flight status are from different sensors. The traditional abnormal state detection model ignores the difference of POS data in the frequency domain during feature learning, which leads to the loss of key feature information and limits the further improvement of detection performance. To deal with this and improve UAV flight safety, this paper presents a method for detecting the abnormal state of a UAV based on a timestamp slice and multi-separable convolutional neural network (TS-MSCNN). Firstly, TS-MSCNN divides the POS data reasonably in the time domain by setting a set of specific timestamps and then extracts and fuses the key features to avoid the loss of feature information. Secondly, TS-MSCNN converts these feature data into grayscale images by data reconstruction. Lastly, TS-MSCNN utilizes a multi-separable convolution neural network (MSCNN) to learn key features more effectively. The binary and multi-classification experiments conducted on the real flight data, Air Lab Fault and Anomaly (ALFA), demonstrate that the TS-MSCNN outperforms traditional machine learning (ML) and the latest deep learning methods in terms of accuracy.
Maximizing the Integration of a Battery Energy Storage System–Photovoltaic Distributed Generation for Power System Harmonic Reduction: An Overview
Authors: Owosuhi Adedayo , Hamam Yskandar , Munda Josiah
Published: March 2023

The highly variable power generated from a battery energy storage system (BESS)–photovoltaic distributed generation (PVDG) causes harmonic distortions in distribution systems (DSs) due to the intermittent nature of solar energy and high voltage rises or falls in the BESS. Harmonic distortions are major concerns in the DS, especially when the sizes and locations of these resources are sub-optimal. As a result, many studies are being performed on the optimal allocation of BESS/PVDG systems in distribution network systems. In this regard, this paper seeks to review the existing planning models, optimization methods and renewable energy resources that uncertainty models have employed in solving BESS/PVDGs allocation problems in terms of obtaining optimal solutions/allocations and curtailing the harmonic contents of the DSs. However, studies on optimal allocation planning of BESS/PVDGs have achieved minimum cost but were not able to meet the standard harmonic level of the DSs. The results identified GA, PSO and AIS as some of the most used methodologies while LP, MILP and different configurations of NLP were used in the model formulations of BESS/PVDGs problems. The results also revealed DC-link voltage and switching and grid voltage harmonics as the notable causes and sources of harmonic distortions in BESS/PVDG systems. The current allocation models presented in the recent literature for the planning of BESS/PVDGs do not include the variables necessary for curtailing the harmonic contents in their planning formulations. This paper, therefore, recommends an improved and all-encompassing planning model with an efficient intelligent search algorithm capable of obtaining a global optimum solution and curtailing harmonic distortions from the BESS/PVDG-connected DSs.
Sustainability Strategies by Oil and Gas Companies, Contribution to the SDGs and Local Innovation Ecosystems
Authors: Menéndez-Sánchez Jaime , Fernández-Gómez Jorge , Araujo-de-la-Mata Andrés
Published: March 2023

Oil and gas (O&G) companies are facing increasing pressure to transform their businesses in order to contribute effectively to the transition from an economy based on fossil fuels to one driven by clean energy technologies. Understanding how O&G companies can define actions to comply with the United Nations’ Sustainable Development Goals (SDGs) and, specifically, how they can generate a positive impact in terms of sustainability through technology innovation becomes relevant in order to guarantee the success of such transformation. To explore this issue, this article analyzes, using a case study research methodology, the sustainability strategy of an O&G company in the Basque Country region (Spain) that is undergoing a profound transformation of its overall business strategy. In particular, the analysis focuses on how the company’s innovation and research and development (R&D) activities and projects related to clean technologies contribute to fulfilling the energy- and non-energy-related SDGs. The main result of the analysis is the identification and characterization of an emerging complex public–private multi-stakeholder business and innovation ecosystem surrounding the O&G company under scrutiny with a clear focus on low-carbon technologies. This ecosystem channels knowledge and innovation synergies and spillovers at the local and regional levels, encouraging green industrial growth in different value chains, and provides insights about how O&G companies can contribute effectively to the SDGs and, at the same time, increase the sustainability of their businesses.
Comparative Review of Motor Technologies for Electric Vehicles Powered by a Hybrid Energy Storage System Based on Multi-Criteria Analysis
Authors: Rimpas Dimitrios , Kaminaris Stavrοs D. , Piromalis Dimitrios D. , Vokas George , Arvanitis Konstantinos G. , Karavas Christos-Spyridon
Published: March 2023

The modern era of green transportation based on Industry 4.0 is leading the automotive industry to focus on the electrification of all vehicles. This trend is affected by the massive advantages offered by electric vehicles (EV), such as pollution-free, economical and low-maintenance cost operation. The heart of this system is the electric motor powered by lithium-ion batteries; however, due to their many limitations, a hybrid energy storage system (HESS) consisting of batteries and ultracapacitors is currently gaining increased attention. This paper aims to review the distinct motor technologies such as brushless motors, synchronous reluctance and induction motors currently used in EVs. Additionally, through eleven selected criteria, such as regenerative braking efficiency and power density at different load ranges, the motors are classified in terms of their combined ability to operate with a HESS in order to maximize efficiency and sizing. The results show that permanent magnet and induction motors are the best options when all criteria are considered, while synchronous reluctance motor outperforms the induction motor regarding only the main factors affecting the performance of the hybrid storage system.
Selective Auto-Reclosing of Mixed Circuits Based on Multi-Zone Differential Protection Principle and Distributed Sensing
Authors: Kawal Kevin , Blair Steven , Hong Qiteng , Papadopoulos Panagiotis N.
Published: March 2023

Environmental concerns and economic constraints have led to increasing installations of mixed conductor circuits comprising underground cables (UGCs) and overhead transmission lines (OHLs). Faults on the OHL sections of such circuits are usually temporary, while there is a higher probability that faults on UGC sections are permanent. To maintain power system reliability and security, auto-reclose (AR) schemes are typically implemented to minimize outage duration after temporary OHL faults while blocking AR for UGC faults to prevent equipment damage. AR of a hybrid UCG–OHL transmission line, therefore, requires effective identification of the faulty section. However, the different electrical characteristics of UGC and OHL sections present significant challenges to existing protection and fault location methods. This paper presents a selective AR scheme for mixed conductor circuits based on the evaluation of differential currents in multiple defined protection zones, using distributed current transformer (CT) measurements provided by passive optical sensing. Case studies are conducted with a number of different UGC–OHL configurations, and the results demonstrate that the proposed scheme can accurately identify the faulty section, enabling effective selective AR of a comprehensive range of mixed conductor circuit topologies. The proposed scheme is also more cost effective, with reduced hardware requirements compared to conventional solutions. This paper thereby validates the optimal solution for mixed circuit protection as described in CIGRE Working Group B5.23 report 587.
Measurement of Gaseous Exhaust Emissions of Light-Duty Vehicles in Preparation for Euro 7: A Comparison of Portable and Laboratory Instrumentation
Authors: Valverde Victor , Kondo Yosuke , Otsuki Yoshinori , Krenz Torsten , Melas Anastasios , Suarez-Bertoa Ricardo , Giechaskiel Barouch
Published: March 2023

The European Union’s ambition to reach climate neutrality and a toxic-free environment by 2050 entails, among other things, cleaner road vehicles. The European Commission’s proposal for the next regulatory emissions standard, Euro 7, requires the measurement of pollutants currently not regulated on the road. In this study we compared a prototype portable emissions measurement system (PEMS) measuring CO2, CO, NO, NO2, N2O, NH3, CH4, and HCHO based on infrared laser absorption modulation (IRLAM), and two Fourier transform infrared (FTIR) spectrometers with laboratory grade analyzers. To this end, one Euro 6d Diesel, one Euro 6d gasoline, and one Euro 4 gasoline vehicle were tested at −7 °C and 23 °C with various driving cycles covering traffic conditions to highway dynamic driving. The results demonstrated that the differences among the instruments were small: ±1 mg/km for HCHO, N2O, and CH4, ±2.5 mg/km for NH3, ±10–15 mg/km for NOx, ±50 mg/km or ±15% for CO (whichever was larger), and ±10–15 g/km for CO2. These values corresponded to <10–15% of the proposed Euro 7 limits or the emission levels of the tested vehicles. Our results confirm the feasibility of on-board systems to measure the conventional components including CO2 and the aforementioned additional pollutants.
Energy Self-Sufficiency of a Salmonids Breeding Facility in the Recirculating Aquaculture System
Authors: Dworak Paweł , Mrozik Andrzej , Korzelecka-Orkisz Agata , Tański Adam , Formicki Krzysztof
Published: March 2023

This study presents the results of research on energy self-sufficiency, safety and production reliability in a salmonids fish farm. For the needs of the breeding facility under construction in the Recirculating Aquaculture System, energy requirement analyses were carried out and guidelines collected for the implementation of innovative solutions, which ensure the security and continuity of energy supplies and the plant’s energy self-sufficiency. This research included the development of solutions for the reliability of power supply and ensuring the continuity of operation of devices that are key to the fish farming process, i.e., lighting and pumps. In terms of ensuring the necessary amounts of energy, the possibility to diversify electricity sources was analysed, taking into account the possibility of self-production, including primarily from a photovoltaic power plant and from average power wind farms. The profitability of using energy storage was also examined. Recommendations and a list of energy balances for selected cases of potential power sources are presented.
Comparison of Blade Aeroelastic Responses between Upwind and Downwind of 10 MW Wind Turbines under the Shear Wind Condition
Authors: Kang Haojie , Xu Bofeng , Shen Xiang , Li Zhen , Cai Xin , Hu Zhiqiang
Published: March 2023

This paper examines the potential for reducing the cost of energy for super-scale wind turbines through the use of a downwind configuration. Using nonlinear aeroelastic modeling, the responses of 10 MW upwind and downwind wind turbine blades are simulated and compared under shear wind conditions. The study evaluates the impact of both nonlinear and linear aeroelastic models on the dynamic response of different blade sizes, highlighting the need for a nonlinear approach. Results indicate that the linear model overestimates blade deformations (18.14%) and the nonlinear model is more accurate for predicting the aeroelastic response of ultra-long blades of 86.35 m. The study also finds that the downwind turbine blade experiences smaller flapwise moment (17.53%), and blade tip flapwise deformation (33.97%) than the upwind turbine blade, with increased load and deformation fluctuation as wind shear increases.
Passive Hydrogen Recombination during a Beyond Design Basis Accident in a Fusion DEMO Plant
Authors: D’Onorio Matteo , Glingler Tommaso , Mazzini Guido , Porfiri Maria Teresa , Caruso Gianfranco
Published: March 2023

One of the most important environmental and safety concerns in nuclear fusion plants is the confinement of radioactive substances into the reactor buildings during both normal operations and accidental conditions. For this reason, hydrogen build-up and subsequent ignition must be avoided, since the pressure and energy generated may threaten the integrity of the confinement structures, causing the dispersion of radioactive and toxic products toward the public environment. Potentially dangerous sources of hydrogen are related to the exothermal oxidation reactions between steam and plasma-facing components or hot dust, which could occur during accidents such as the in-vessel loss of coolant or a wet bypass. The research of technical solutions to avoid the risk of a hydrogen explosion in large fusion power plants is still in progress. In the safety and environment work package of the EUROfusion consortium, activities are ongoing to study solutions to mitigate the hydrogen explosion risk. The main objective is to preclude the occurrence of flammable gas mixtures. One identified solution could deal with the installation of passive autocatalytic recombiners into the atmosphere of the vacuum vessel pressure suppression system tanks. A model to control the PARs recombination capacity as a function of thermal-hydraulic parameters of suppression tanks has been modeled in MELCOR. This paper aims to test the theoretical effectiveness of the PAR intervention during an in-vessel loss of coolant accident without the intervention of the decay heat removal system for the Water-Cooled LithiumLead concept of EU-DEMO.
Electric Vehicle Battery-Connected Parallel Distribution Generators for Intelligent Demand Management in Smart Microgrids
Authors: Jasim Ali M. , Jasim Basil H. , Neagu Bogdan-Constantin , Attila Simo
Published: March 2023

Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production’s climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller’s inputs include the time of day and the EVB’s State of Charge (SOC). After the controller detects a peak demand, it alerts the EVB to start delivering power. This decrease in peak demand enhances the load factor and benefits both SG investors and end users. In this study, the adopted SG includes five parallel Distribution Generators (DGs) powered by renewable resources, which are three solar Photovoltaics (PVs) and two Wind Turbines (WTs). Sharing power among these DGs ensures the SG’s stability and efficiency. To fulfill demand problem-free, this study dynamically alters the power flow toward equity in power sharing using virtual impedance-based adaptive primary control level. This study proposes a decentralized robust hierarchical secondary control system employing Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controller parameters with fine-grained online tuning using ANNs to restore frequency and voltage deviations. The proposed system is evidenced to be effective through MATLAB simulations and real-time data analysis on the ThingSpeak platform using internet energy technology. Our presented model not only benefits users by enhancing their utility but also reduces energy costs with robust implementation of a control structure by restoring any frequency and voltage deviations by distributing power equally among DGs regardless of demand condition variations.
New Design and Study of the Transient State and Maximum Power Point Tracking of Solid Oxide Fuel Cells Using Fuzzy Control
Authors: Zishan Farhad , Montoya Oscar Danilo , Giral-Ramírez Diego Armando
Published: March 2023

Humans have always been searching for new and efficient ways to convert fuels into usable energy. Solid oxide fuel cells, energy conversion devices capable of generating electrical energy, are widely used due to their high thermal energy production. In this research, fuzzy control was used to manage the voltage and current of solid oxide fuel cells. Simulations were conducted in two evaluation modes: checking the voltage, current, and power of the fuel cell, with and without the use of fuzzy control, and analyzing maximum power point tracking (MPPT) using fuzzy control. In the second mode, when connected to the load, the performance of the fuel cell was evaluated in the transient state, and the role of the controller was clearly visible According to the current–power characteristic of the fuel cell, which is a nonlinear curve and has a maximum point, and by using the fuzzy controller and the appropriate selection of input and output functions, this research aimed to make the system work at the maximum power point at all times. To this effect, a printer was used between the fuel cell and the load, and a fuzzy controller was used to set the cycle of activities, whose input was the slope of the current–power curve. The results show that this controller performs well and is faster when compared to the P&O control method. MATLAB software was used to design and analyze the system and, in order to validate the model, the transient behavior of the fuel cell was studied. The results were compared, and an acceptable match was observed.
Data-Driven Tools for Building Energy Consumption Prediction: A Review
Authors: Olu-Ajayi Razak , Alaka Hafiz , Owolabi Hakeem , Akanbi Lukman , Ganiyu Sikiru
Published: March 2023

The development of data-driven building energy consumption prediction models has gained more attention in research due to its relevance for energy planning and conservation. However, many studies have conducted the inappropriate application of data-driven tools for energy consumption prediction in the wrong conditions. For example, employing a data-driven tool to develop a model using a small sample size, despite the recognition of the tool for producing good results in large data conditions. This study delivers a review of 63 studies with a precise focus on evaluating the performance of data-driven tools based on certain conditions; i.e., data properties, the type of energy considered, and the type of building explored. This review identifies gaps in research and proposes future directions in the field of data-driven building energy consumption prediction. Based on the studies reviewed, the outcome of the evaluation of the data-driven tools performance shows that Support Vector Machine (SVM) produced better performance than other data-driven tools in the majority of the review studies. SVM, Artificial Neural Network (ANN), and Random Forest (RF) produced better performances in more studies than statistical tools such as Linear Regression (LR) and Autoregressive Integrated Moving Average (ARIMA). However, it is deduced that none of the reviewed tools are predominantly better than the other tools in all conditions. It is clear that data-driven tools have their strengths and weaknesses, and tend to elicit distinctive results in different conditions. Hence, this study provides a proposed guideline for the selection tool based on strengths and weaknesses in different conditions.
The Strike-Slip Fault Effects on Tight Ordovician Reef-Shoal Reservoirs in the Central Tarim Basin (NW China)
Authors: Zhao Yawen , Wu Guanghui , Zhang Yintao , Scarselli Nicola , Yan Wei , Sun Chong , Han Jianfa
Published: March 2023

The largest carbonate condensate field in China has been found in the central Tarim Basin. Ordovician carbonate reservoirs are generally attributed to reef-shoal microfacies along a platform margin. However, recent production success has been achieved along the NE-trending strike-slip fault zones that intersect at the platform margin. For this contribution, we analyzed the strike-slip fault effects on the reef-shoal reservoirs by using new geological, geophysical, and production data. Seismic data shows that some NE-trending strike-slip faults intersected the NW-trending platform margin in multiple segments. The research indicated that the development of strike-slip faults has affected prepositional landforms and the subsequent segmentation of varied microfacies along the platform margin. In addition, the strike-slip fault compartmentalized the reef-shoal reservoirs into multiple segments along the extent of the platform margin. We show that fractured reef-shoal complexes are favorable for the development of dissolution porosity along strike-slip fault damage zones. In the tight matrix reservoirs (porosity < 6%, permeability < 0.5 mD), the porosity and permeability could be increased by more than 2–5 times and to 1–2 orders of magnitude in the fault damage zone, respectively. This suggests that high production wells are correlated with “sweet spots” of fractured reservoirs along the strike-slip fault damage zones, and that the fractured reservoirs in the proximity of strike-slip fault activity might be a major target for commercial exploitation of the deep Ordovician tight carbonates.
Analysis and Evaluation of Photovoltaic Cell Defects and Their Impact on Electricity Generation
Authors: Pavlík Marek , Beňa L’ubomír , Medved’ Dušan , Čonka Zsolt , Kolcun Michal
Published: March 2023

Many problems arise in the operation of photovoltaic systems. Each of these problems affects the operation of photovoltaic systems by reducing the power of the entire system. Some problems can be avoided during the design of photovoltaic systems. For example, when designing photovoltaic systems, it is possible to eliminate the shading of photovoltaic panels from surrounding objects. It is also necessary to look at the shading from neighboring photovoltaic panels when designing photovoltaic systems. It is necessary to calculate the inclination of the sunlight based on the position of the sun and calculate the distance between the two panels accordingly. However, some problems appear during the operation of photovoltaic systems. These problems can be partially eliminated. The magnitude of the series resistance can be eliminated by reducing the transition resistance throughout the system. The paper describes these problems and their possible solutions in practice. In the practical part, we focused on simulations and experiments in the field of photovoltaic systems (PV). The simulations focused on both the influence of temperature and the series resistance of photovoltaic panels on the production of electricity. The experiments were focused on the influence of various faults/defects on the power and V-A characteristics of photovoltaic panels connected in strings. The paper also discusses the impact of bypass diodes on the operation of photovoltaic systems and determines the need to use bypass diodes.
A Literature Review on Methods and Metrics for the Analysis of Outdoor Air Displacement Conditions in the Urban Environment
Authors: Wankhade Ritesh , Pernigotto Giovanni , Larcher Michele
Published: March 2023

The ongoing pandemic has driven the attention of both policy makers and professionals of the building sector towards the need for proper ventilation of the indoor environment. Despite accurate ventilation control only being available with mechanical ventilation systems, in several countries worldwide the renovation of indoor air relies mainly on natural solutions. In this context, in the design of new or renovated buildings, conventional natural ventilation rates are typically assumed to be in agreement with available technical standards, sometimes regardless of the actual external conditions. For instance, local wind speed and direction, as well as buoyancy-driven air displacements, are not considered, even if they can significantly affect the ventilation efficacy for the designed buildings. Moreover, the local outdoor temperature and the presence of pollutants are rarely accounted for, even though they can represent interesting inputs not only for naturally ventilated buildings but also for mechanical ventilation systems. In the framework described above, this review paper aims to provide an overview of the current state-of-the-art of the research regarding air displacement and conditions in the urban context, focusing on the main methods, parameters and metrics to consider in order to ensure a deeper and more accurate modelling of natural ventilation potential in the urban built environment. The analysis of the literature includes both experimental and numerical studies. As regards the latter ones, the features of the chosen urban areas—real or parametric ones—the adopted turbulence models and the indexes calculated as simulation outputs were analysed, with the purpose of defining a common framework to support future extensive numerical studies.
Development and Applications of 3D Printing-Processed Auxetic Structures for High-Velocity Impact Protection: A Review
Authors: Junio Raí Felipe Pereira , da Silveira Pedro Henrique Poubel Mendonça , Neuba Lucas de Mendonça , Monteiro Sergio Neves , Nascimento Lucio Fabio Cassiano
Published: March 2023

Auxetic structures (AXSs) are a novel class of materials with unique mechanical deformation behavior associated with negative Poisson ratio. The combination of AXS configurations with various types of materials has unveiled a wide field of applications, including military high-velocity protection against explosions and ballistic projectiles. However, the characteristic geometric re-entrant model of AXSs imposes limitations and difficulties when using conventional manufacturing methods to assemble the structure lattice. Additive manufacturing (AM) has recently been explored as a more efficient and cost-effective method to fabricate AXSs, regardless of the type of material. This review paper focuses on the development and applications of AM processed AXSs. The review highlights the significance and great potential for this class of materials that can be produced relatively fast and at a low cost. The advantages of AXS/AM are expected to extend to important industrial sectors, particularly for military ballistic armor, where the feasibility for products with improved properties is critical. The use of AM offers a viable solution to overcome the difficulties associated with the conventional manufacturing methods, and thus offers greater design flexibility, cost efficiency, and reduced material waste. This review paper aims to contribute to the understanding of the current state-of-the-art and future research prospects for the production and applications of AXS/AM.
A Review of Coastal Protection Using Artificial and Natural Countermeasures—Mangrove Vegetation and Polymers
Authors: Amos Deborah , Akib Shatirah
Published: March 2023

Any stretch of coastline requires protection when the rate of erosion exceeds a certain threshold and seasonal coastal drift fluctuations fail to restore balance. Coast erosion can be caused by natural, synthetic, or a combination of the two. Severe storm occurrences, onshore interventions liable for sedimentation, wave action on the coastlines, and rising sea levels caused by climate change are instances of natural factors. The protective methods used to counteract or prevent coastal flooding are categorized as hard and soft engineering techniques. This review paper is based on extensive reviews and analyses of scientific publications. In order to establish a foundation for the selection of appropriate adaptation measures for coastal protection, this research compiles literature on a combination of both natural and artificial models using mangrove trees and polymer-based models’ configurations and their efficiency in coastal flooding. Mangrove roots occur naturally and cannot be manipulated unlike artificial model configuration which can be structurally configured with different hydrodynamic properties. Artificial models may lack the real structural features and hydrodynamic resistance of the mangrove root it depicts, and this can reduce its real-life application and accuracy. Further research is required on the integration of hybrid configuration to fully optimize the functionality of mangrove trees for coastal protection.
Preparation and Physicochemical Characterization of Biodiesel from Recycled Vegetable Oil in Cuenca, Ecuador by Transesterification Catalyzed by KOH and NaOH
Authors: Ramírez Jennifer , Buestán Lourdes , López-Maldonado Eduardo Alberto , Pinos-Vélez Verónica
Published: March 2023

Due to the environmental impact of using fossil fuels, alternatives for the generation of biofuels are being studied. An option for this problem is to obtain biodiesel from recycled vegetable oil. Studies show that basic homogeneous catalysis has advantages such as speed over other types of catalysis. However, most of these studies are conducted with unused or little-used oils. Therefore, this study aims to obtain biodiesel from recycled vegetable oil collected from municipal oil collection centers by transesterification applying NaOH or KOH as catalysts. The used oil was filtered, washed, and dried to remove impurities. The transesterification reaction catalyzed with NaOH and KOH was carried out; each catalyst was tested at two concentrations: 0.5% and 1% w/w. All reactions were carried out at 55 °C, 350 rpm, methanol, and alcohol/oil ratio of 6/1 for 1.5 h. The best yield was found with the KOH with a concentration of 0.5%. The biodiesel obtained presented the following properties: density of 0.8807 g/mL, a viscosity of 4.694 mm2/s, an acid number of 0.355 mg KOH/g, and corrosion 1a, a calorific value of 39,726 J/g, and a FAME of 93%.
Relation between Water Storage and Photoassimilate Accumulation of Neosinocalamus affinis with Phenology
Authors: Shi Wanli , Li Juan , Zhan Hui , Yu Lixia , Wang Changming , Wang Shuguang
Published: March 2023

Reasonable management could produce the good growth of bamboo plants. There are few studies in bamboo that integrate phenology and ecophysiological traits; in this case, water relations, photosynthetic rates and carbohydrate content in different organs of Neosinocalamus affinis were determined. The moisture content was easily affected by the local precipitation and showed a similar trend in leaves and branches, with the highest values in July and August, but did not vary significantly between vegetative phenological stages in culms. The emergence and growth of shoots caused an apparent decrease in the moisture content of 1-year culms. NSC content showed a similar trend in culms and branches in classes of different ages and decreased progressively from March through November and then increased in the following months. Net photosynthetic rate and transpiration pull showed the highest values in July and then decreased constantly, which implied that a great deal of water was consumed for photoassimilate synthesis. The net photosynthetic rates of leaves were significantly affected by the water status of culms in the dry season, but correlated significantly only with the leaf water content in the wet season for the sufficient water supply. The transpiration pulls, water potential and sap flow rates revealed the water status of culms better than the water content of culms. The shoot germination and growth of N. affinis were more dependent on the photoassimilate accumulation in the early stage from May to July. There was no real dormancy period for N. affinis due to its apparent photoassimilate accumulation and water flow in January. This suggested that proper irrigation was essential during leaf and branch extension during the dry season.
Tree Genetics: Molecular and Functional Characterization of Genes
Authors: Emiliani Giovanni , Giovannelli Alessio
Published: March 2023

Forests represent one of the most important ecosystems on Earth, covering approximately 30% of the total global land area [...]
Modelling and Mapping Urban Vulnerability Index against Potential Structural Fire-Related Risks: An Integrated GIS-MCDM Approach
Authors: Noori Sepideh , Mohammadi Alireza , Miguel Ferreira Tiago , Ghaffari Gilandeh Ata , Mirahmadzadeh Ardabili Seyed Jamal
Published: March 2023

Identifying the regions with urban vulnerability to potential fire hazards is crucial for designing effective risk mitigation and fire prevention strategies. The present study aims to identify urban areas at risk of fire using 19 evaluation factors across economic, social, and built environment-infrastructure, and prior fire rates dimensions. The methods for “multi-criteria decision making” (MCDM) include the Analytic Hierarchy Process for determining the criteria’s importance and weight of the criteria. To demonstrate the applicability of this approach, an urban vulnerability index map of Ardabil city in Iran was created using the Fuzzy-VIKOR approach in a Geographic Information System (GIS). According to the findings, about 9.37 km2 (31%) of the city, involving roughly 179,000 people, presents a high or very high level of risk. Together with some neighbourhoods with low socioeconomic and environmental conditions, the city centre is the area where the level of risk is more significant. These findings are potentially very meaningful for decision-makers and authorities, providing information that can be used to support decision-making and the implementation of fire risk mitigation strategies in Ardabil city. The results of this research can be used to improve policy, allocate resources, and renew urban areas, including the reconstruction of old, worn-out, and low-income urban areas.
Fortification of Crude Protein Extract from Sung Yod and Hom Rajinee Rice Brans in the Development of Functional Jelly Products
Authors: Junsara Kanthaporn , Yupanqui Chutha Takahashi , Kawee-ai Arthitaya , Samakradhamrongthai Rajnibhas Sukeaw
Published: March 2023

Rice bran protein (RBP) has shown good nutritional and biological values. The present study aimed to determine the functional properties of rice bran crude protein (RBCP) and apply RBCP to a rice jelly recipe to improve the jelly quality and make it an acceptable product for consumers. The design used in the jelly formulation was a central composite design. The freeze-dried crude protein of Sung Yod (SY; 0.00–0.50%) and Hom Rajinee (HR; 0.00–0.50%) rice brans were applied to the rice jelly recipe. The crude protein extract significantly influenced the physicochemical, sensory, and angiotensin I converting enzyme (ACE)-inhibitory activity of the developed jellies (p < 0.05). The optimized jelly contained 0.11% SY and 0.50% HR crude protein extract. The rice jelly fortified with lyophilized RBCP presented a high content of bioactive compounds (phenolic and flavonoids) with antioxidant activity and ACE-inhibitory activity. Therefore, the crude protein extract of rice brans is a potential raw material that can be used in jelly products as a cheap material to improve the jelly’s nutritional quality without affecting consumer acceptability. The outcome of the present investigation confirms that rice bran extracts may have the potential to be further exploited as ingredients in foods.
Application of Whey Protein-Based Emulsion Coating Treatment in Fresh-Cut Apple Preservation
Authors: Xin Ying , Yang Chenhao , Zhang Jiahao , Xiong Lei
Published: March 2023

Fresh-cut fruit requires an edible and water-resistant coating to remain fresh. This article investigated the effects of transglutaminase (TGase) and sunflower oil on the water-resistant characteristics, mechanical properties, and microstructure of a whey protein-based film. The whey protein-based emulsion coating’s preservation effect on fresh-cut apples was confirmed. According to the findings, sunflower oil (added at 1.5% w/w) could interact with β-lactoglobulin, α-lactoglobulin dimer, and β-lactoglobulin dimer to form emulsion droplets that are evenly dispersed throughout the protein film. This effect, combined with the covalent cross-linking of TGase, significantly improves the films’ microstructure, mechanical properties, and water resistance. However, too much and unevenly distributed sunflower oil (add 3% w/w) partially prevented the covalent cross-linking of TGase, reducing the elongation at the break of the composite film. In the fresh-cut apple storage experiment, the whey protein-based emulsion coating treatment significantly reduced the weight loss rate and browning index of fresh-cut apples by 26.55% and 46.39%, respectively. This was accomplished by the coating treatment significantly inhibiting the respiration rate increase, PPO and CAT activity enhancement, H2O2 production, and MDA accumulation. This research provides practical, technical, and theoretical guidance for the preservation of fresh-cut fruit.
Metabolite Fingerprinting for Identification of Panax ginseng Metabolites Using Internal Extractive Electrospray Ionization Mass Spectrometry
Authors: Yuan Xueyan , Zhang Xiaoping , Xu Jiaquan , Ye Jianhua , Yu Zhendong , Zhang Xinglei
Published: March 2023

Ginseng, a kind of functional food and medicine with high nutritional value, contains various pharmacological metabolites that influence human metabolic functions. Therefore, it is very important to analyze the composition and metabolites of ginseng. However, the analysis of active metabolites in ginseng samples usually involves various experimental steps, such as extraction, chromatographic separation, and characterization, which may be time-consuming and laborious. In this study, an internal extractive electrospray ionization mass spectrometry (iEESI-MS) method was developed to analyze active metabolites in ginseng samples with sequential sampling and no pretreatment. A total of 44 metabolites, with 32 ginsenosides, 6 sugars, and 6 organic acids, were identified in the ginseng samples. The orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot showed a clear separation of ginseng samples from different origins, indicating that metabolic changes occurred under different growing conditions. This study demonstrated that different cultivation conditions of ginseng can be successfully discriminated when using iEESI-MS-based metabolite fingerprints, which provide an alternative solution for the quality identification of plant drugs.