bluuebunny/crossref_metadata_2025 huggingface dataset:
A Robust Multilevel Inverter Topology for Operation under Fault Conditions
Authors: Asif Mohd , Tariq Mohd , Sarwar Adil , Hussan Md Reyaz , Ahmad Shafiq , Mihet-Popa Lucian , Shah Noor Mohamed Adamali
Published: December 2021

Multilevel inverters (MLIs) are used on a large scale because they have low total harmonic distortion (THD) and low voltage stress across the switches, making them ideal for medium- and high-power applications. The authenticity of semiconductor devices is one of the main concerns for these MLIs to operate properly. Due to the large number of switches in multilevel inverters, the possibility of a fault also arises. Hence, a reliable five-level inverter topology with fault-tolerant ability has been proposed. The proposed topology can withstand an open-circuit (OC) fault caused when any single switch fails. In comparison to typical multilevel inverters, the proposed topology is fault-tolerant and reliable. The simulation of the proposed topology is conducted in MATLAB-Simulink and PLECS software packages, and the results obtained for normal pre-fault, during-fault, and after-fault conditions are discussed. Experimental results also prove the proposed cell topology’s robustness and effectiveness in tolerating OC faults across the switches. Furthermore, a thorough comparison is provided to demonstrate the proposed topology’s superiority compared to recently published topologies with fault-tolerant features.
An Approximate Mixed-Integer Convex Model to Reduce Annual Operating Costs in Radial Distribution Networks Using STATCOMs
Authors: Montoya Oscar Danilo , Alvarado-Barrios Lázaro , Hernández Jesus C.
Published: December 2021

The problem of optimal siting and sizing of distribution static compensators (STATCOMs) is addressed in this research from the point of view of exact mathematical optimization. The exact mixed-integer nonlinear programming model (MINLP) is decoupled into two convex optimization sub-problems, named the location problem and the sizing problem. The location problem is addressed by relaxing the exact MINLP model, assuming that all the voltages are equal to 1∠0∘, which allows obtaining a mixed-integer quadratic programming model as a function of the active and reactive power flows. The solution of this model provides the best set of nodes to locate all the STATCOMs. When all the nodes are selected, it solves the optimal reactive power problem through a second-order cone programming relaxation of the exact optimal power flow problem; the solution of the SOCP model provides the optimal sizes of the STATCOMs. Finally, it refines the exact objective function value due to the intrinsic non-convexities associated with the costs of the STATCOMs that were relaxed through the application of Taylor’s series expansion in the location and sizing stages. The numerical results in the IEEE 33- and 69-bus systems demonstrate the effectiveness and robustness of the proposed optimization problem when compared with large-scale MINLP solvers in GAMS and the discrete-continuous version of the vortex search algorithm (DCVSA) recently reported in the current literature. With respect to the benchmark cases of the test feeders, the proposed approach reaches the best reductions with 14.17% and 15.79% in the annual operative costs, which improves the solutions of the DCVSA, which are 13.71% and 15.30%, respectively.
A Privacy Preserved, Trust Relationship (PTR) Model for Internet of Vehicles
Authors: Farman Haleem , Khalil Abizar , Ahmad Naveed , Albattah Waleed , Khan Muazzam A. , Islam Muhammad
Published: December 2021

The Internet of vehicles (IoV) depicts a reality where ordinary things are connected to vehicular ad-hoc networks (VANETs), allowing them to transmit and collaborate. By placing these regular objects in VANETs and making them available at any time, this network and data sharing may raise real privacy and security issues. Thus, group-based communication is mostly preferred in the literature. However, in heavy network scenarios, cluster-based communication mostly leads to additional overload in the form of the group leader that causes delay and disrupts the performance of a network. Due to the interaction of VANETs with applications that are not stable for life, privacy and security mechanism for detecting many malicious nodes is in great demand. Therefore, a multi-phantom node selection has been proposed in this paper to select trustworthy, normal, and malicious nodes. The multi-phantom node scheme is proposed to reduce the phantom node load, where the multi-lateral nodes in a cluster act as a phantom node to share the load. A multi criteria decision-making (MCDM) methodology (analytic network process) is used to optimize the phantom node to pre-serve privacy using the privacy preserved trust relationship (PTR) model. The results show checking the stability of parameters and using sensitivity analysis by considering different scenarios for the most optimal phantom node to preserve vehicle location privacy. The impact of the proposed model will be more clearly visible in its real-time implementation in urban areas vehicle networks.
A Continuous Semantic Embedding Method for Video Compact Representation
Authors: Han Tingting , Qi Yuankai , Zhu Suguo
Published: December 2021

Video compact representation aims to obtain a representation that could reflect the kernel mode of video content and concisely describe the video. As most information in complex videos is either noisy or redundant, some researchers have instead focused on long-term video semantics. Recent video compact representation methods heavily rely on the segmentation accuracy of video semantics. In this paper, we propose a novel framework to address these challenges. Specifically, we designed a novel continuous video semantic embedding model to learn the actual distribution of video words. First, an embedding model based on the continuous bag of words method is proposed to learn the video embeddings, integrated with a well-designed discriminative negative sampling approach, which helps emphasize the convincing clips in the embedding while weakening the influence of the confusing ones. Second, an aggregated distribution pooling method is proposed to capture the semantic distribution of kernel modes in videos. Finally, our well-trained model can generate compact video representations by direct inference, which provides our model with a better generalization ability compared with those of previous methods. We performed extensive experiments on event detection and the mining of representative event parts. Experiments on TRECVID MED11 and CCV datasets demonstrated the effectiveness of our method. Our method could capture the semantic distribution of kernel modes in videos and shows powerful potential to discover and better describe complex video patterns.
Infrared Image Super-Resolution via Progressive Compact Distillation Network
Authors: Fan Kefeng , Hong Kai , Li Fei
Published: December 2021

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance.
Predictive Fixed Switching Maximum Power Point Tracking Algorithm with Dual Adaptive Step-Size for PV Systems
Authors: Ahmed Mostafa , Harbi Ibrahim , Kennel Ralph , Abdelrahem Mohamed
Published: December 2021

Maximum power point tracking (MPPT) is an essential and primary objective in photovoltaic (PV) systems implementation. Thus, in this article, the predictive fixed switching MPPT technique is proposed for a two-stage PV system, where the system under consideration consists of a PV source, boost converter, and two-level inverter. The MPPT design is based on dual adaptive step-size realization to limit the duty cycle oscillations at a steady state. Furthermore, the PI controller is eliminated, which simplifies the MPPT implementation. The suggested tuning procedure of the duty cycle is compared with the conventional adaptive step-size perturb and observe (P&O) method. The inverter is controlled using an efficient finite-set model predictive control (FS-MPC) algorithm with reduced computation burden, where the optimal switching state vector is identified based on the polarity of the reference voltage in the α-β reference frame and without any need for sector determination. Furthermore, the cost function of the FS-MPC algorithm is modified to include the reduction of the switching frequency as a secondary objective for the inverter control. The overall control methodology is evaluated using experimental results at different operating conditions.
Exploring the Potential for Electric Retrofit Regulations and an Accreditation Scheme for the UK
Authors: Watts Robbie , Ghosh Aritra , Hinshelwood Justin
Published: December 2021

Electric vehicles have zero tailpipe emissions and can reduce greenhouse gas emissions by up to 90% compared to internal combustion engine (ICE) vehicles. Electric retrofits involve converting an ICE vehicle to an electric drivetrain, aiding the transition to zero emission vehicles by adapting current vehicles and, thus, reducing the transport sector emissions. Other benefits include charge exemptions in major cities, reduced driving costs, and lower maintenance. The UK has a considerable retrofit market, with a large price range of services offered. There is a varying level of practice undertaken and current regulations may not adequately cover these retrofits. Industrial engagement has highlighted the varying levels and common themes of practice, such as restoration work, computer-aided design, and finite element analysis. Converting the registered fuel type of a vehicle to electricity, post-retrofit, appears to be a limited process, with few steps. Therefore, a regulatory framework, such as an accreditation scheme, could be introduced to ensure high levels of safety and good practice. Future work suggestions include further meetings with the DVLA and DVSA, and meeting the Motor Insurers’ Bureau.
Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
Authors: Corrochano Javier , Alonso-Weber Juan M. , Sesmero María Paz , Sanchis Araceli
Published: December 2021

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.
Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning
Authors: Aldosary Abdallah Mobark , Aldossari Saud Alhajaj , Chen Kwang-Cheng , Mohamed Ehab Mahmoud , Al-Saman Ahmed
Published: December 2021

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.
Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction
Authors: Ljubobratović Dejan , Vuković Marko , Brkić Bakarić Marija , Jemrić Tomislav , Matetić Maja
Published: December 2021

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.
Integrated Communication and Control Design for Fuel-Efficient Vehicle Platooning
Authors: Wen Qingji , Hu Bin-Jie
Published: December 2021

As a promising application for autonomous driving, vehicle platooning aims at increasing traffic throughput, improving road safety, and reducing air pollution and fuel consumption. However, frequent traffic perturbations will bring more fuel consumption because vehicles driving in a platoon require more control to ensure safe driving, especially in high-density scenes. In this paper, considering the traffic perturbations and high-density scenes, we integrate communication and control systems to reduce the fuel consumption of a platoon. By obtaining the velocities of multiple vehicles ahead through a long-term evolution-vehicle (LTE-V) network, we propose a modified distributed model predictive control (DMPC) method to smooth traffic perturbations and handle the constraints of vehicle state and control. In addition, considering a limited number of uplink channels that can be reused in the platoon and the uncertainty of wireless channels, a radio resource allocation optimization problem in the LTE-V network is modeled. This problem is solved in two steps including maximum vehicle-to-vehicle (V2V) broadcast distance and minimum weight matching. This resource allocation scheme increases the platoon-based V2V broadcast distance while ensuring the ergodic capacity requirement of the cellular user (CUE) uplink communication and the reliability of platoon-based V2V communication. Simulation results show that the proposed method improves fuel efficiency compared to the existing schemes.
Modelling Long-Term Transition from Coal-Reliant to Low-Emission Power Grid and District Heating Systems in Poland
Authors: Jaskólski Marcin , Bućko Paweł
Published: December 2021

Energy systems require technological changes towards climate neutrality. In Poland, where the power system is dominated by outdated coal-fired power plants, efforts to minimize the environmental impact are associated with high costs. Therefore, optimal paths for the development of the energy sector should be sought in order to achieve ambitious long-term strategic goals, while minimizing the negative impact on the consumers’ home budget. A methodology and a model for the development of the electricity and heat generation structure were developed and implemented in market allocation (MARKAL) modelling framework. Two scenarios were presented, i.e., business as usual (BAU) and withdrawal from coal (WFC) scenarios. The calculations showed a significant role of nuclear energy and offshore wind power in the pursuit of climate neutrality of electricity generation. In the BAU scenario, the model proposes to stay with coal technologies using carbon capture and storage systems. Withdrawal from coal (WFC scenario) makes it necessary to replace them by gas-fired power plants with CO2 sequestration. Solar energy can be used both in electricity and district heating. In order to build on the latter technological option, appropriate energy storage techniques must be developed. Geothermal energy is expected to be the key option for district heat generation in the long-term horizon. The proposed development paths guarantee a significant reduction in greenhouse gases and industrial emissions. However, complete climate neutrality is uncertain, given the current degree and dynamics of technological development.
An Attempt to Apply Canonical Analysis to Investigate the Dependencies between the Level of Organic Farming Development in Poland and the Chosen Environmental Determinants
Authors: Smoluk-Sikorska Joanna , Malinowski Mariusz
Published: December 2021

Polish organic agriculture has faced rapid growth in the recent two decades. Nevertheless, one may observe considerable discrepancies in organic agriculture development in specific regions of Poland. Therefore, it is necessary to recognize the key conditions for this development and its spatial differentiation. Since the relationship between organic farming and the natural environment has a fundamental meaning in this production system, it is crucial to study the development determinants of environmental characters. Thus the paper aims to identify the level of organic farming development in Polish districts and to investigate multidimensional relations between this level and selected environmental conditions. In order to identify the range and direction of those multidimensional relations between the discussed phenomena, canonical analysis was applied. Within the conducted study, proprietary synthetic measures were constructed (using the TOPSIS—Technique for Order of Preference by Similarity to Ideal Solution), and linear ordering of the objects described by a large number of variables was employed. To define the strength and direction of the dependencies among constructed synthetic indices of the level of organic farming development and environmental conditions, a correlation analysis was performed. All 380 districts in Poland were considered as the investigated objects. Based on the variables describing selected environmental conditions, one may explain nearly 26.7% of the variance of variables related to organic agriculture development.
Comparison of Flexibility Factors and Introduction of A Flexibility Classification Using Advanced Heat Pump Control
Authors: Hall Monika , Geissler Achim
Published: December 2021

With the increasing use of renewable energy, the energy flexibility of buildings becomes increasingly important regarding grid support. Therefore, there is a need to describe this flexibility in a concise manner. For the characterization of building energy flexibility, flexibility factors can be used. The comparison of a selection of existing flexibility factors shows that they are not easy to use or understand for designers and users. A simplification is necessary. The aim of this study is to introduce a flexibility classification that is easy to understand and shows in an easy way if a building already uses the lowest energy cost level or if further improvement is possible. The classification expresses the annual energy costs in colored classes: green (class A) for lowest up to red (class D) for highest level. Basically, the flexibility classes can be derived for any metric of interest, in this paper examples are shown for energy costs and CO2eq emissions. The results given are based on the simulation of load management scenarios with different penalty signals applied for the heat pump operation of a residential building.
Theoretical and Experimental Studies of Uneven Dust Suction from a Multi-Cyclone Settling Tank in a Two-Stage Air Filter
Authors: Dziubak Tadeusz
Published: December 2021

In this paper, the uneven air stream distribution problem of individual cyclones is studied in the multi-cyclones of intake air filters in special vehicles’ engines. This problem increases in multi-cyclones, in which several dozen cyclones have a common dust trap from which the collected dust is continuously removed by ejection suction. The aim of this study is the recognition of the theoretical and experimental possibility of reducing the streams’ unevenness, which should result in an efficiency increase in multi-cyclone separation. The methods that led to obtaining a relative stream uniformity from the suction of individual cyclones was analyzed. The method for creating equal pressure drops between the suction streams in the channels was used to achieve this goal. For this purpose, the internal structure of the multi-cyclone settler was changed. The multi-cyclone settling tank space was divided by vertical partitions into independent segments. The settling tank segment was then divided with horizontal shelves into suction channels of different heights, which were assigned a specific number of individual cyclones. The suction channels’ height was theoretically selected in terms of the equal resistance to air stream flow through the channels. For this purpose, the multi-cyclone dust settler segment model was developed. The theoretically determined suction channel’s height was verified by performing experimental flow tests in four (A, B, C, D) dust settler variants. Suction streams of satisfactory uniformity from the cyclones of the variant D settling tank were obtained at a level of 5%. In the next stage, experimental tests of the segment cyclones were carried out with dust before and after the division into suction channels of variant D for the settling tank. A significant increase was achieved from 93.73% to 96.08% in the cyclones’ separation efficiency, which were located as far away from the suction stub as possible and led to a reduction in the non-uniformity of cyclone efficiency in the segment. It follows that the multi-cyclone dust settling segment’s internal structure change gave the expected results.
Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach
Authors: Majewski Grzegorz , Szeląg Bartosz , Białek Anita , Stachura Michał , Wodecka Barbara , Anioł Ewa , Wdowiak Tomasz , Brandyk Andrzej , Rogula-Kozłowska Wioletta , Łagód Grzegorz
Published: December 2021

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices’ influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its’ real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects.
Conceptual Control Method of Drilling Rig’s Torsional Vibrations Frequencies
Authors: Trawiński Tomasz , Szczygieł Marcin , Polnik Bartosz , Deja Przemysław
Published: December 2021

This article focuses on the possibility of using an innovative drilling method for the implementation of underground works, especially where there is no physical possibility of using large working machines. Work on a model carried out under the INDIRES project is discussed. A design of a drilling tool equipped with the proposed technology is presented. The solution in question makes it possible to increase the efficiency of the drilling process, which is confirmed by computer simulations. Also, introductory tests of a drilling process supported by torsional vibration generated by an electromagnetic torque generator provided in the KOMAG laboratory facility show the reduction of the drilling time by almost two-fold. In our opinion, adding torsional vibration acting on the plane of a drilled wall that equals natural frequencies of the drilled material represents a promising new technology for drilling. The presented work constitutes the basis for the development of the proposed technology and allows us to conclude that the developed method will be of great interest to manufacturers of drilling machines and devices.
The Role of Industrial Revival in Untapping the Bioeconomy’s Potential in Central and Eastern Europe
Authors: Lovec Marko , Juvančič Luka
Published: December 2021

The bioeconomy occupies the centre of the Green Deal, the EU’s plan to support transformative growth following the COVID-19 episode. However, parts of the EU, such as countries in Central and Eastern Europe (CEE) continue to lag behind in harnessing the potential held by the bioeconomy. This article argues that in CEE countries, where the primary and conventional bioeconomy sectors play a more important role, ‘early’ transition pathways such as improvements in productivity and practice- as well as commercialisation-oriented innovation (the do–use–interact model: DUI) are just as important as approaches based on (generally publicly supported) R&D, innovation adoption, and technology transfer (science–technology–innovation model: STI), typically associated with high-value bioindustrial applications. The argument is tested by conducting a survey of 352 experts in the region that gives an insight into the CEE macro-region’s assets with respect to deploying the bioeconomy’s potential and assessing the transition pathways relevant to the better performance of bioeconomy (primary, manufacturing, and other related) sectors. The results show the particular relevance of consolidating the primary and traditional sectors to support improvements in productivity based on the vertical and horizontal interaction typically associated with DUI, while the relevance of STI is mostly linked to advanced sectors, which are narrowly distributed across the region. The findings are relevant to policy given that the EU’s bioeconomy policy has thus far chiefly focused on STI support that better corresponds to the needs of countries at more advanced stages of developing the bioeconomy, but is less appropriate for the specific context and needs of CEE.
Flow and Heat Transfer Characteristics of a Swirling Impinging Jet Issuing from a Threaded Nozzle of 45 Degrees
Authors: Xu Liang , Yang Tao , Sun Yanhua , Xi Lei , Gao Jianmin , Li Yunlong
Published: December 2021

In order to achieve uniform and effective impingement cooling, a swirling jet with a swirling angle of 45° (SIJ 45°) is put forward in this paper. Namely, there are four 45° spiral grooves equipped on the inner wall of the circular hole. The difference in the flow field and heat transfer characteristics between the conventional impinging jet (CIJ) and SIJ 45° is compared and analyzed. The spiral channels can increase the heat transfer rate and cooling uniformity because of the action of superimposed airflow. In addition, the thread nozzle brings lower pressure loss, which can reduce the airflow friction while effectively ensuring high heat transfer in the center area of the jet. An experimental system is built to investigate the heat transfer and flow characteristics of the impingement surface. Smoke flow visualization technology is used to explore the complex flow field of the CIJ and SIJ 45°, and the heat transfer rate of the target surface is analyzed based on thermocouple data. When 6000≤Re≤30,000, and 1≤h/dj≤8, the averaged Nusselt number (Nu) correlation for SIJ 45° is established, which is in good agreement with the experimental results. SIJ 45° is an effective measure to replace the CIJ, and the research herein provides some reference for designing the structure of new jets.
Chloroplast DNA Diversity in Populations of P. sylvestris L. from Middle Siberia and the Romanian Carpathians
Authors: Sheller Marina , Ciocîrlan Elena , Mikhaylov Pavel , Kulakov Sergey , Kulakova Nadezhda , Ibe Aleksey , Sukhikh Tatyana , Curtu Alexandru
Published: December 2021

Scots pine is one of the dominant conifer species in forest ecosystems of the boreal zone in Eurasia. Knowledge of the genetic structure and the level of genetic variability of Scots pine populations is relevant for the development of measures aimed at conservation of species’ diversity. In this study, we used ten paternally inherited chloroplast microsatellite loci to investigate the genetic diversity of nineteen Scots pine populations from Middle Siberia and the Romanian Carpathians. The results of the study showed high genetic diversity (HCP = 0.91–1.00) in all of the investigated populations. The cpSSR analysis yielded a total of 158 haplotypes. The majority of the haplotypes (85%) were detected only once (unique haplotypes). Three common haplotypes were found between the Carpathian and the Siberian populations of Scots pine. Analysis of molecular variance (AMOVA) showed that only 3% of the variation occurred among populations from Middle Siberia and 6% of the variation existed among populations from the Carpathian Mountains. Overall, we found a weak geographic population structure in Scots pine from Middle Siberia and the Romanian Carpathians. The present study on genetic diversity in the Siberian and the Carpathian populations of Scots pine may contribute to the sustainable management and conservation of Scots pine genetic resources in Middle Siberia and the Romanian Carpathians.
Impregnation of Wood with Waste Engine Oil to Increase Water- and Bio-Resistance
Authors: Belchinskaya Larisa , Zhuzhukin Konstantin Viktorovich , Ishchenko Tatiana , Platonov Aleksey
Published: December 2021

Impregnation is a common method of protecting wood from external influences. This study proposes the use of spent engine oil as an impregnating composition for modifying birch wood to make it resistant to biological degradation and water. The indicators of water resistance and dimensional stability of wood such as wetting contact angle, thermogravimetric analysis, Fourier transform infrared spectroscopy (FTIR), and biodegradation tests have been determined. It has been found that treatment with spent engine oil significantly increases the dimensional stability (56.8% and 45.7% in tangential and radial directions) and water-resistant indicators of wood. Thermogravimetric analysis has showed that the curves for the impregnated specimens were different from the control group and had two sharp peaks at 302 and 357 °C. However, FTIR indicated that no clear chemical reactions occur between spent engine oil and wood. A study on wood resistance to biological degradation has showed a significant increase in resistance against brown rot (Poria placenta fungi) in the treated specimens, in contrast to the control group. Thus, impregnation of wood with spent engine oil makes it possible to increase wood resistance to water and biological degradation.
Tree Rings Reveal the Impact of Soil Temperature on Larch Growth in the Forest-Steppe of Siberia
Authors: Belokopytova Liliana V. , Zhirnova Dina F. , Meko David M. , Babushkina Elena A. , Vaganov Eugene A. , Krutovsky Konstantin V.
Published: December 2021

Dendroclimatology has focused mainly on the tree growth response to atmospheric variables. However, the roots of trees directly sense the “underground climate,” which can be expected to be no less important to tree growth. Data from two meteorological stations approximately 140 km apart in southern Siberia were applied to characterize the spatiotemporal dynamics of soil temperature and the statistical relationships of soil temperature to the aboveground climate and tree-ring width (TRW) chronologies of Larix sibirica Ledeb. from three forest–steppe stands. Correlation analysis revealed a depth-dependent delay in the maximum correlation of TRW with soil temperature. Temperatures of both the air and soil (depths 20–80 cm) were shown to have strong and temporally stable correlations between stations. The maximum air temperature is inferred to have the most substantial impact during July–September (R = −0.46–−0.64) and early winter (R = 0.39–0.52). Tree-ring indices reached a maximum correlation with soil temperature at a depth of 40 cm (R = −0.49–−0.59 at 40 cm) during April–August. High correlations are favored by similar soil characteristics at meteorological stations and tree-ring sites. Cluster analysis of climate correlations for individual trees based on the K-means revealed groupings of trees driven by microsite conditions, competition, and age. The results support a possible advantage of soil temperature over air temperature for dendroclimatic analysis of larch growth in semiarid conditions during specific seasons.
Mycobiota of Fine Roots of Pseudotsuga menziesii Introduced to the Native Forest Environment
Authors: Damszel Marta , Szmidla Hanna , Sikora Katarzyna , Młodzińska Agata , Piętka Sławomir , Sierota Zbigniew
Published: December 2021

The mycobiota of the fine roots of Pseudotsuga menziesii were studied as a measure of the adaptation of this alien species to new soil and climatic conditions. We hypothesized that after approximately 130 years of growth in a given habitat, the fungal community colonizing the fine roots of introduced trees would resemble the biota of Pinus sylvestris and Fagus sylvatica in surrounding stands of similar age and site conditions. The genetic material isolated from the fine roots was subjected to metagenomic analysis. We recorded 33, 97 and 95 OTUs exclusively from root samples of Douglas fir, beech and pine, respectively; 124 were common to all sample types. The biota from the roots of P. menziesii featured a less diverse taxonomic composition and were characterized by the highest proportion of symbiotrophs (71.8%) versus saprothrophs (5.6%) and pathogens (0.24%). Some fungal taxa (19) in the roots of P. menziesii were common with the biota in the roots of other adjacent trees, while some (7) were unique to Douglas fir. Our results indicate a locally differentiated strategy of naturalness of fungi inhabiting soil and roots of P. menziesii, although 130 years have passed since the introduction of the species.
Allometric Models for Estimating Aboveground Biomass in Short Rotation Crops of Acacia Species in Two Different Sites in Chile
Authors: Cabrera-Ariza Antonio , Valdés Sara , Gilabert Horacio , Santelices-Moya Rómulo Eduardo , Alonso-Valdés Máximo
Published: December 2021

We evaluated the ability of different allometric models to estimate the biomass production of short-rotation woody crops of Acacia dealbata, A. mearnsii and A. melanoxylon. Models considered the adjustment and validation of biomass functions and biological restrictions, such as the use of additive components of the biomass (stem, branches, and leaves). Adjustments of linear and nonlinear models of the three acacia species—established in two locations and of three densities in southern Chile—were utilized. Systems of equations were adjusted to guarantee the addition of the biomass components and the trees’ total biomass. The selection of models was performed based on their goodness of fit and predictive quality. Methods that accounted for the correlation between biomass components granted an additively consistent equations system with efficient estimates and reliable prediction intervals.
Identification of Suitable Sites for Jatropha curcas L. Bioenergy Plantation Using the AquaCrop Model
Authors: Khalid Faisal , Ullah Sami , Rehman Fariha , Hadi Rana , Khan Nasreen , Ibrahim Farzana , Khan Tariq , Aziz Farha , Feroz Dania Aeema , Nadeem Syeda Ghufrana , Hussain Majid
Published: December 2021

Jatropha curcas (JC), as a biofuel plant, has been reported to have various desired characteristics such as high oil content seeds (27–40%), fast-growth, easy cultivation, drought tolerance, and can be grown on marginal soil and wasteland, requiring fewer nutrients and management and does not interfere with existing food crops, insects, and pest resistance. This investigation was the first study of its type to use climatological data, blue/green water footprints, and JC seed production to identify suitable sites for JC bioenergy plantation using the AquaCrop FAO model across the Khyber Pakhtunkhwa province in northwest Pakistan. The JC seed yield (10 ton/ha) was at a maximum in the districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, and Dera Ismail Khan, in addition to its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, Kohat, Charsadda, Mardan, Swabi, and Nowshera, respectively. Green water footprint (264 m3/ton of JC seed) and blue water footprint (825 m3/ton) was less in these areas. Furthermore, the results revealed that, depending on climatological circumstances, the southern part of the Khyber Pakhtunkhwa province is more appropriate for JC bioenergy plantation than the northern region. The districts of Bannu, Karak, Hangu, Kurram, North Waziristan, Lakki Marwat, South Waziristan, Dera Ismail Khan, and its frontier regions, Tank, Peshawar, Mohmand, Orakzai, Khyber, and Kohat, in Khyber Pakhtunkhwa province were identified to be the most ideal places for JC bioenergy plantation. As a result, under the Billion Tree Afforestation Project (BTAP) and the Green Pakistan Project, the Forest Department of Khyber Pakhtunkhwa should consider planting JC species in the province’s southern region. Furthermore, this research will provide scientific information to government and private sector officials for better management and optimum yield of the JC biofuel crop, as well as for the promotion of energy forestry in Pakistan.
An Experimental Performance Evaluation of Cloud-API-Based Applications
Authors: Abuzrieq Yara , Ahmad Amro Al-Said , Younes Maram Bani
Published: December 2021

Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.
Efficient Solar-Driven Water Purification Based on Biochar with Multi-Level Pore Bundle Structure for Preparation of Drinking Water
Authors: Zhang Zhen , Jiang Shizheng , Chen Haonan , Qi Hao , Chen Yali , Chen Yujie , Deng Qiliang , Wang Shuo
Published: December 2021

Water is an important source for humankind. However, the amount of available clean water has rapidly reduced worldwide. To combat this issue, the solar-energy-driven evaporation technique is newly proposed to produce clean water. Here, biochar derived from sorghum stalk with a multi-level pore bundle structure is utilized to fabricate a solar-driven evaporator for the first time. The biochar displays rapid water transfer and low thermal conductivity (ca. 0.0405 W m−1 K−1), which is vitally important for such an application purpose. The evaporation rate and energy conversion efficiency of the solar evaporator based on carbonized sorghum stalk can achieve up to 3.173 kg m−2 h−1 and 100%, respectively, which are better than most of the previously reported biomass materials. Furthermore, the carbonized sorghum stalk also displays good resistance to salt crystallization, anti-acidic/basic, and organic pollutants by producing drinking water using seawater, acidic/basic waste water, and organic polluted water, respectively. The direct application of processed water in food production was also investigated. The present solar steam evaporator based on the carbonized sorghum stalk has the potential to create practical drinking water production by using various water sources.
The Physicochemical Properties of Starch Are Affected by Wxlv in Indica Rice
Authors: Feng Linhao , Lu Chenya , Yang Yong , Lu Yan , Li Qianfeng , Huang Lichun , Fan Xiaolei , Liu Qiaoquan , Zhang Changquan
Published: December 2021

Amylose largely determines rice grain quality profiles. The process of rice amylose biosynthesis is mainly driven by the waxy (Wx) gene, which also affects the diversity of amylose content. The present study assessed the grain quality profiles, starch fine structure, and crystallinity characteristics of the near-isogenic lines Q11(Wxlv), NIL(Wxa), and NIL(Wxb) in the indica rice Q11 background containing different Wx alleles. Q11(Wxlv) rice contained a relatively higher amylose level but very soft gel consistency and low starch viscosity, compared with rice lines carrying Wxa and Wxb. In addition, starch fine structure analysis revealed a remarkable decrease in the relative area ratio of the short amylopectin fraction but an increased amylose fraction in Q11(Wxlv) rice. Chain length distribution analysis showed that Q11(Wxlv) rice contained less amylopectin short chains but more intermediate chains, which decreased the crystallinity and lamellar peak intensity, compared with those of NIL(Wxa) and NIL(Wxb) rice. Additionally, the starches in developing grains showed different accumulation profiles among the three rice lines. Moreover, significant differences in starch gelatinization and retrogradation characteristics were observed between near-isogenic lines, which were caused by variation in starch fine structure. These findings revealed the effects of Wxlv on rice grain quality and the fine structure of starch in indica rice.
Detection of Species Substitution in the Meat Value Chain by High-Resolution Melting Analysis of Mitochondrial PCR Products
Authors: Njaramba Jane Kagure , Wambua Lillian , Mukiama Titus , Amugune Nelson Onzere , Villinger Jandouwe
Published: December 2021

Substituting high commercial-value meats with similar cheaper or undesirable species is a common form of food fraud that raises ethical, religious, and dietary concerns. Measures to monitor meat substitution are being put in place in many developed countries. However, information about similar efforts in sub-Saharan Africa is sparse. We used PCR coupled with high-resolution melting (PCR-HRM) analysis targeting three mitochondrial genes—cytochrome oxidase 1 (CO1), cytochrome b (cyt b), and 16S rRNA—to detect species substitution in meat sold to consumers in Nairobi, Kenya. Out of 107 meat samples representing seven livestock animals, 11 (10.3%) had been substituted, with the highest rate being observed in samples sold as goat. Our results indicate that PCR-HRM analysis is a cost- and time-effective technique that can be employed to detect species substitution. The combined use of the three mitochondrial markers produced PCR-HRM profiles that successfully allowed for the consistent distinction of species in the analysis of raw, cooked, dried, and rotten meat samples, as well as of meat admixtures. We propose that this approach has broad applications in the protection of consumers against food fraud in the meat industry in low- and middle-income countries such as Kenya, as well as in developed countries.