bluuebunny/crossref_metadata_2025 huggingface dataset:
A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration
Authors: Skorin-Kapov Nina , Santos Ricardo , Ghazzai Hakim , Kassler Andreas
Published: February 2021

In this paper, we consider the reconfiguration of wireless backhaul networks with mechanically steerable antennas in the presence of changing traffic demands. Reconfiguration requires the scheduling and coordination of several operations, including antenna alignment and link establishment/removal, with minimal disruption to existing user traffic. Previously, we proposed a Mixed Integer Linear Program (MILP) to orchestrate such reconfiguration with minimal packet loss. While the MILP solves the problem optimally for a limited number of discrete reconfiguration time slots, it does not scale well. In this paper, we propose an iterative randomized greedy algorithm to obtain suboptimal solutions in reduced time. The algorithm schedules the reconfiguration of wireless links by ranking them according to a set of attributes with associated weights and selecting them according to a randomized greedy function. Results on six different network scenarios indicate that the proposed algorithm can achieve good quality solutions in significantly less time. Furthermore, by extending the reconfiguration time beyond the maximum number of time slots solvable by the MILP, the proposed heuristic can obtain superior solutions for some problem instances. The number of iterations of the algorithm can be tuned for its applicability in both offline and online planning scenarios.
Toward Autonomous UAV Localization via Aerial Image Registration
Authors: Wang Xuezhi , Kealy Allison , Li Wenchao , Jelfs Beth , Gilliam Christopher , Le May Samantha , Moran Bill
Published: February 2021

Absolute localization of a flying UAV on its own in a global-navigation-satellite-system (GNSS)-denied environment is always a challenge. In this paper, we present a landmark-based approach where a UAV is automatically locked into the landmark scene shown in a georeferenced image via a feedback control loop, which is driven by the output of an aerial image registration. To pursue a real-time application, we design and implement a speeded-up-robust-features (SURF)-based image registration algorithm that focuses efficiency and robustness under a 2D geometric transformation. A linear UAV controller with signals of four degrees of freedom is derived from the estimated transformation matrix. The approach is validated in a virtual simulation environment, with experimental results demonstrating the effectiveness and robustness of the proposed UAV self-localization system.
Memory Access Optimization of a Neural Network Accelerator Based on Memory Controller
Authors: Wei Rongshan , Li Chenjia , Chen Chuandong , Sun Guangyu , He Minghua
Published: February 2021

Special accelerator architecture has achieved great success in processor architecture, and it is trending in computer architecture development. However, as the memory access pattern of an accelerator is relatively complicated, the memory access performance is relatively poor, limiting the overall performance improvement of hardware accelerators. Moreover, memory controllers for hardware accelerators have been scarcely researched. We consider that a special accelerator memory controller is essential for improving the memory access performance. To this end, we propose a dynamic random access memory (DRAM) memory controller called NNAMC for neural network accelerators, which monitors the memory access stream of an accelerator and transfers it to the optimal address mapping scheme bank based on the memory access characteristics. NNAMC includes a stream access prediction unit (SAPU) that analyzes the type of data stream accessed by the accelerator via hardware, and designs the address mapping for different banks using a bank partitioning model (BPM). The image mapping method and hardware architecture were analyzed in a practical neural network accelerator. In the experiment, NNAMC achieved significantly lower access latency of the hardware accelerator than the competing address mapping schemes, increased the row buffer hit ratio by 13.68% on average (up to 26.17%), reduced the system access latency by 26.3% on average (up to 37.68%), and lowered the hardware cost. In addition, we also confirmed that NNAMC efficiently adapted to different network parameters.
Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer
Authors: Makhadmeh Sharif Naser , Al-Betar Mohammed Azmi , Alyasseri Zaid Abdi Alkareem , Abasi Ammar Kamal , Khader Ahamad Tajudin , Damaševičius Robertas , Mohammed Mazin Abed , Abdulkareem Karrar Hameed
Published: February 2021

The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of PSPSH are: (I) minimizing the cost of the power consumed by home appliances, which refers to electricity bills, (II) balance the power consumed during a time horizon, particularly at peak periods, which is known as the peak-to-average ratio, and (III) maximizing the satisfaction level of users. Several approaches have been proposed to address PSPSH optimally, including optimization and non-optimization based approaches. However, the set of restrictions inhibit the approach used to obtain the optimal solutions. In this paper, a new formulation for smart home battery (SHB) is proposed for PSPSH that reduces the effect of restrictions in obtaining the optimal/near-optimal solutions. SHB can enhance the scheduling of smart home appliances by storing power at unsuitable periods and use the stored power at suitable periods for PSPSH objectives. PSPSH is formulated as a multi-objective optimization problem to achieve all objectives simultaneously. A robust swarm-based optimization algorithm inspired by the grey wolf lifestyle called grey wolf optimizer (GWO) is adapted to address PSPSH. GWO has powerful operations managed by its dynamic parameters that maintain exploration and exploitation behavior in search space. Seven scenarios of power consumption and dynamic pricing schemes are considered in the simulation results to evaluate the proposed multi-objective PSPSH using SHB (BMO-PSPSH) approach. The proposed BMO-PSPSH approach’s performance is compared with that of other 17 state-of-the-art algorithms using their recommended datasets and four algorithms using the proposed datasets. The proposed BMO-PSPSH approach exhibits and yields better performance than the other compared algorithms in almost all scenarios.
A Fuzzy Logic Model for Hourly Electrical Power Demand Modeling
Authors: Islas Marco Antonio , Rubio José de Jesús , Muñiz Samantha , Ochoa Genaro , Pacheco Jaime , Meda-Campaña Jesus Alberto , Mujica-Vargas Dante , Aguilar-Ibañez Carlos , Gutierrez Guadalupe Juliana , Zacarias Alejandro
Published: February 2021

In this article, a fuzzy logic model is proposed for more precise hourly electrical power demand modeling in New England. The issue that exists when considering hourly electrical power demand modeling is that these types of plants have a large amount of data. In order to obtain a more precise model of plants with a large amount of data, the main characteristics of the proposed fuzzy logic model are as follows: (1) it is in accordance with the conditions under which a fuzzy logic model and a radial basis mapping model are equivalent to obtain a new scheme, (2) it uses a combination of the descending gradient and the mini-lots approach to avoid applying the descending gradient to all data.
A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
Authors: Rezwan Sifat , Choi Wooyeol
Published: February 2021

Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research.
A Low-Cost Microwave Filter with Improved Passband and Stopband Characteristics Using Stub Loaded Multiple Mode Resonator for 5G Mid-Band Applications
Authors: Alnahwi Falih M. , Al-Yasir Yasir I. A. , Abdulhameed Abdulghafor A. , Abdullah Abdulkareem S. , Abd-Alhameed Raed A.
Published: February 2021

This paper presents the design and implementation of a printed circuit microwave band-pass filter for 5G mid-band applications, using a Stub Loaded Multiple Mode Resonator (SL-MMR) technique. The objective of this article is to introduce a low-cost microstrip filter with improved passband and stopband characteristics, based on a mathematical analysis of stub loaded resonators. The filter cost is reduced by selecting the low-cost FR4 dielectric material as a substrate for the proposed filter. Based on the transmission line model of the filter, mathematical expressions are derived to predict the odd-mode and the even-mode resonant frequencies of the SL-MMR. The mathematical model also highlights the capability of controlling the position of the SL-MMR resonant frequencies, so that the 5G sub-band that extends along the range (3.7–4.2 GHz) can perfectly be covered with almost a flat passband. At the resonance frequency, a fractional bandwidth of 12.8% (500 MHz impedance bandwidth) has been obtained with a return loss of more than 18 dB and an insertion loss of less than 2.5 dB over the targeted bandwidth. Furthermore, a pair of parasitic elements is attached to the proposed filter to create an additional transmission zero in the lower stopband of the filter to enhance the suppression of the filter stopband. The measured and simulation results are well agreed, and both reveal the acceptable performance of the stopband and passband characteristics of the filter.
On the Selection of Process Mining Tools
Authors: Drakoulogkonas Panagiotis , Apostolou Dimitris
Published: February 2021

Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent years and new process mining software tools, both academic and commercial, have been developed. This paper provides a survey of process mining software tools. It identifies and describes criteria that can be useful for comparing the tools. Furthermore, it introduces a multi-criteria methodology that can be used for the comparative analysis of process mining software tools. The methodology is based on three methods, namely ontology, decision tree, and Analytic Hierarchy Process (AHP), that can be used to help users decide which software tool best suits their needs.
Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network
Authors: Sánchez-Sutil Francisco , Cano-Ortega Antonio
Published: February 2021

Electrical installations represent an important part of the industry. In this sense, knowing the state of the electrical installation in real time through the readings of the installed power analyzers is of vital importance. For this purpose, the RS485 bus can be used, which most electrical installations already have. An alternative to the bus wiring and its distance limitation is the use of low-power wide area networks (LPWAN). The long range (LoRa) protocol is ideal for industries due to its low-power consumption and coverage of up to 10 km. In this research, a device is developed to control all the reading and programming functions of a power analyzer and to integrate the device into the LoRa LPWAN network. The power analyzer monitor and programming device (PAMPD) is inexpensive and small enough to be installed in electrical panels, together with the power analyzer, without additional wiring. The information collected is available in the cloud in real time, allowing a multitude of analysis be run and optimization in real time. The results support high efficiency in information transmission with average information loss rate of 3% and a low average transmission time of 30 ms.
Electrostatic Discharge Characteristics of SiGe Source/Drain PNN Tunnel FET
Authors: Wang You , Mao Yu , Ji Qizheng , Yang Ming , Yang Zhaonian , Lin Hai
Published: February 2021

Gate-grounded tunnel field effect transistors (ggTFETs) are considered as basic electrostatic discharge (ESD) protection devices in TFET-integrated circuits. ESD test method of transmission line pulse is used to deeply analyze the current characteristics and working mechanism of Conventional TFET ESD impact. On this basis, a SiGe Source/Drain PNN (P+N+N+) tunnel field effect transistors (TFET) was proposed, which was simulated by Sentaurus technology computer aided design (TCAD) software. Simulation results showed that the trigger voltage of SiGe PNN TFET was 46.3% lower, and the failure current was 13.3% higher than Conventional TFET. After analyzing the simulation results, the parameters of the SiGe PNN TFET were optimized. The single current path of the SiGe PNN TFET was analyzed and explained in the case of gate grounding.
Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers
Authors: Lee Janggoon , Park Chanhee , Roh Heejun
Published: February 2021

Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.
Fossil vs. Active Geothermal Systems: A Field and Laboratory Method to Disclose the Relationships between Geothermal Fluid Flow and Geological Structures at Depth
Authors: Liotta Domenico , Brogi Andrea , Ruggieri Giovanni , Zucchi Martina
Published: February 2021

Comparison between fossil and analogue active geothermal systems permit to obtain key-parameters to define a conceptual model of the area under exploration. The approach is based on structural, kinematic, and fluid inclusions analyses. The fossil system is investigated to describe the distribution of the hydrothermal mineralization as witness of the fluid flow through geological structures and bodies, at depth. Structural and kinematic data (to define the preferential direction of fluid flow) are collected in structural stations and by scan lines and scan boxes on key outcrops. Distribution, length, width of fractures, and hydrothermal veins bring to evaluate permeability in the fossil system and, by analogy, in the deep roots of the active system. Fluid inclusions analysis shed light on density, viscosity, and temperature of the paleo-fluids. Data integration provides the hydraulic conductivity. In active geothermal systems, fieldwork is addressed to paleo-stress analysis with data from recent faults (<2 Ma), to compare with local focal mechanisms. By this, indications on the present fluid pathways are given. The main advantage resides in obtaining parameters normally got after drilling, thus contributing to strengthen the strategy of exploration, de-risking unsuccessful boreholes.
Collapse of n Point Vortices, Formation of the Vortex Sheets and Transport of Passive Markers
Authors: Kudela Henryk
Published: February 2021

In this paper, the motion of the n-vortex system as it collapses to a point in finite time is studied. The motion of vortices is described by the set of ordinary differential equations that we are able to solve analytically. The explicit formula for the solution demands the initial location of collapsing vortices. To find the collapsing locations of vortices, the algebraic, nonlinear system of equations was built. The solution of that algebraic system was obtained using Newton’s procedure. A good initial iterate needs to be provided to succeed in the application of Newton’s procedure. An unconstrained Leverber–Marquart optimization procedure was used to find such a good initial iterate. The numerical studies were conducted, and numerical evidence was presented that if in a collapsing system n=50 point vortices include a few vortices with much greater intensities than the others in the set, the vortices with weaker intensities organize themselves onto the vortex sheet. The collapsing locations depend on the value of the Hamiltonian. By changing the Hamiltonian values in a specific interval, the collapsing curves can be obtained. All points on the collapse curves with the same Hamiltonian value represent one collapsing system of vortices. To show the properties of vortex sheets created by vortices, the passive tracers were used. Advection of tracers by the velocity induced by vortices was calculated by solving the proper differential equations. The vortex sheets are an impermeable barrier to inward and outward fluxes of tracers. Arising vortex structures are able to transport the passive tracers. In this paper, several examples showing the diversity of collapsing structures with the vortex sheet are presented. The collapsing phenomenon of many vortices, their ability to self organize and the transportation of the passive tracers are novelties in the context of point vortex dynamics.
Comparison of Cycle Reduction and Model Reduction Strategies for the Design Optimization of Hybrid Powertrains on Driving Cycles
Authors: Kaloun Adham , Brisset Stéphane , Ogier Maxime , Ahmed Mariam , Vincent Robin
Published: February 2021

Decision-making is a crucial and difficult step in the design process of complex systems such as the hybrid powertrain. Finding an optimal solution requires the system feedback. This can be, depending on the granularity of the models at the component level, highly time-consuming. This is even more true when the system’s performance is determined by its control. In fact, various possibilities can be selected to deliver the required torque to the wheels during a driving cycle. In this work, two different design strategies are proposed to minimize the fuel consumption and the cost of the hybrid powertrain. Both strategies adopt the iterative framework which allows for the separation of the powertrain design problem and its control while leading to system optimality. The first approach is based on model reduction, while the second approach relies on improved cycle reduction techniques. They are then applied to a parallel hybrid vehicle case study, leading to important cost reduction in reasonable delays and are compared using different metrics.
CFD Analysis of the Performance of a Double Decker Turbine for Wave Energy Conversion
Authors: García-Díaz Manuel , Pereiras Bruno , Miguel-González Celia , Rodríguez Laudino , Fernández-Oro Jesús
Published: February 2021

The Double Decker Turbine (DDT) is a recent design introduced for oscillating water column (OWC) devices. Its major contribution is the combination of two typical solutions in just one prototype: a self-rectifying performance, to deal with the bidirectional flow, and the twin-turbine concept, allowing the use of unidirectional turbines. This is achieved by a set of two concentric turbines, called external and internal turbines (ExT—InT). In this work, Computational Fluid Dynamics (CFD) numerical model is developed to study in detail the performance of a DDT, where geometrical components for both turbines have been taken from previous works of the authors. The ANSYS-Fluent code was first executed by means of a URANS simulation with a realizable k-ε turbulence model to obtain the performance curve of the turbine under steady conditions. Results obtained reveal its potential with respect to other solutions in the current state-of-the-art of OWC solutions for Wave Energy Conversion. Following a non-steady analysis, we assumed a sinusoidal input from the chamber which also resulted in promising results. Finally, the flow analysis inside the DDT allowed the authors to envisage geometric improvements that could enhance the DDT efficiency on future works.
Heat-Up Performance of Catalyst Carriers—A Parameter Study and Thermodynamic Analysis
Authors: Steiner Thomas , Neurauter Daniel , Moewius Peer , Pfeifer Christoph , Schallhart Verena , Moeltner Lukas
Published: February 2021

This study investigates geometric parameters of commercially available or recently published models of catalyst substrates for passenger vehicles and provides a numerical evaluation of their influence on heat-up behavior. Parameters considered to have a significant impact on the thermal economy of a monolith are: internal surface area, heat transfer coefficient, and mass of the converter, as well as its heat capacity. During simulation experiments, it could be determined that the primary role is played by the mass of the monolith and its internal surface area, while the heat transfer coefficient only has a secondary role. Furthermore, an optimization loop was implemented, whereby the internal surface area of a commonly used substrate was chosen as a reference. The lengths of the thin wall and high cell density monoliths investigated were adapted consecutively to obtain the reference internal surface area. The results obtained by this optimization process contribute to improving the heat-up performance while simultaneously reducing the valuable installation space required.
Solar-Powered Charging Networks for Electric Vehicles
Authors: Erickson Larry , Ma Stephanie
Published: February 2021

The cost of solar-generated electricity and battery costs have been decreasing significantly. These developments can be combined to introduce solar-powered charging networks with demand management prices (DMP) to enable electric vehicle (EV) customers to help utilities to manage renewable energy. As solar-generated electricity becomes the cheapest source of power, the need to increase demand for electricity during the day can be met by charging EVs at an attractive DMP in parking lots with solar panels and charging stations that are connected to the electrical grid. The demand for electricity can be managed and controlled by the utility with the goal of increasing demand for power as needed so that all electricity that is generated can be sold. The proposal is to introduce a new DMP rate that is only implemented when the utility wants to increase demand and sell power at this low rate in order to make full use of the supply. As utilities strive to reach 100% renewable electrical power to serve a society transported by EVs, cooperative plans to make good use of batteries in EVs for managing the electrical grid will become more important.
Autonomous Decision-Making While Drilling
Authors: Cayeux Eric , Daireaux Benoît , Ambrus Adrian , Mihai Rodica , Carlsen Liv
Published: February 2021

The drilling process is complex because unexpected situations may occur at any time. Furthermore, the drilling system is extremely long and slender, therefore prone to vibrations and often being dominated by long transient periods. Adding the fact that measurements are not well distributed along the drilling system, with the majority of real-time measurements only available at the top side and having only access to very sparse data from downhole, the drilling process is poorly observed therefore making it difficult to use standard control methods. Therefore, to achieve completely autonomous drilling operations, it is necessary to utilize a method that is capable of estimating the internal state of the drilling system from parsimonious information while being able to make decisions that will keep the operation safe but effective. A solution enabling autonomous decision-making while drilling has been developed. It relies on an optimization of the time to reach the section total depth (TD). The estimated time to reach the section TD is decomposed into the effective time spent in conducting the drilling operation and the likely time lost to solve unexpected drilling events. This optimization problem is solved by using a Markov decision process method. Several example scenarios have been run in a virtual rig environment to test the validity of the concept. It is found that the system is capable to adapt itself to various drilling conditions, as for example being aggressive when the operation runs smoothly and the estimated uncertainty of the internal states is low, but also more cautious when the downhole drilling conditions deteriorate or when observations tend to indicate more erratic behavior, which is often observed prior to a drilling event.
Spatiotemporal Simulation of Green Space by Considering Socioeconomic Impacts Based on A SD-CA Model
Authors: Li Fangzheng , Wang Rongfang , Lu Shasha , Shao Ming , Ding Jingyi , Sun Qianxiang
Published: February 2021

Green space is an important part of composite urban spatial systems. Therefore, reasonable planning strategies based on scientifically sound predictions of temporal and spatial changes in green space are critical for maintaining urban ecological environments, ensuring the health of residents, and maintaining social stability. However, existing forecasting models discount the impacts of urban social economy on green space. To address this gap, we constructed a system dynamics and cellular automata (SD-CA) coupling model that integrated the socioeconomic system and generated multiple scenarios. The results showed that at the current pace of socioeconomic development, Beijing’s central district will experience an overall reduction in green space and a decline in its integrity and diversity by 2035. If the population of this area reaches 9.29 million by 2035 and the GDP maintains an average growth rate of 6.1%, the areas of various land types will exhibit little change by 2035, and green space will be optimized to a certain extent. However, if the study area’s population decreases to 8.59 million by 2035 and the average GDP growth rate drops to 4.9%, the fragmentation, connectivity, and diversity index of green space will all increase significantly by 2035, and green space will be clearly optimized. We propose scientifically grounded strategies for maximizing the ecological functions and economic benefits of green space through optimized green space patterns, considered from a policy-oriented perspective of promoting socioeconomic development.
Responses of Swamp Cypress (Taxodium distichum) and Chinese Willow (Salix matsudana) Roots to Periodic Submergence in Mega-Reservoir: Changes in Organic Acid Concentration
Authors: He Xinrui , Wang Ting , Wu Kejun , Wang Peng , Qi Yuancai , Arif Muhammad , Wei Hong
Published: February 2021

Organic acids are critical as secondary metabolites for plant adaption in a stressful situation. Oxalic acid, tartaric acid, and malic acid can improve plant tolerance under waterlogged conditions. Two prominent woody species (Taxodium distichum-Swamp cypress and Salix matsudana-Chinese willow) have been experiencing long-term winter submergence and summer drought in the Three Gorges Reservoir. The objectives of the present study were to explore the responses of the roots of two woody species during flooding as reflected by root tissue concentrations of organic acids. Potted sample plants were randomly divided into three treatment groups: control, moderate submergence, and deep submergence. The concentrations of oxalic acid, tartaric acid, and malic acid in the main root and lateral roots of the two species were determined at four stages. The results showed that T. distichum and S. matsudana adapted well to the water regimes of the reservoir, with a survival rate of 100% during the experiment period. After experiencing a cycle of submergence and emergence, the height and base diameter of the two species showed increasing trends. Changes in base diameter showed insignificant differences between submergence treatments, and only height was significant under deep submergence. The concentrations of three organic acids in the roots of two species were influenced by winter submergence. After emergence in spring, two species could adjust their organic acid metabolisms to the normal level. Among three organic acids, tartaric acid showed the most sensitive response to water submergence, which deserved more studies in the future. The exotic species, T. distichum, had a more stable metabolism of organic acids to winter flooding. However, the native species, S. matsudana, responded more actively to long-term winter flooding. Both species can be considered in vegetation restoration, but it needs more observations for planting around 165 m above sea level, where winter submergence is more than 200 days.
Vegetation Height and Diurnal Period Influenced the Landscape-Use Pattern of Small Ruminants in Woodlands around Summer
Authors: Bhattrai Shailes , Karki Uma , Poudel Sanjok
Published: February 2021

Despite the huge potential of using woodlands for small ruminant grazing in the southeast US, unmanaged understory shrubs grown beyond animals’ access minimize the utilization of such vegetation. This study aimed to determine the effect of vegetation height and diurnal period on the behavior and distribution patterns of goats and sheep in woodlands around summer. The study was conducted in six woodland plots (0.4 ha each) comprising southern pines and non-pine (non-target) plant species. Non-pine plants in each study plot were assigned to four treatments: cut to 0 m, 0.9 m, or 1.5 m from the ground level or left uncut (control). Cut plant stubs were allowed to regrow to full canopy before stocking animals. Eight Kiko wethers and five Katahdin rams were rotationally stocked in separate plots, and their diurnal (dawn–dusk) behaviors and distribution patterns were monitored when they were in each plot (three plots per animal species) around the summer of 2018. Animal behavior data were analyzed using the general linear model (GLM) procedure with multivariate analysis of variance (MANOVA) in SAS, while animal distribution pattern and weather data were analyzed in SAS using a GLM procedure and the distribution evenness index (DEI) using the Kruskal–Wallis rank-sum test in R. Level of significance was set at 5%. Both animal species visited the control area the least. Wethers browsed predominantly in areas where non-pine plants were cut to 0.9 m from the ground level, and rams grazed mostly in areas where non-pine plants were cut to the ground level, mostly during the post-midday period. Browsing was the dominant feeding behavior of wethers (39% browsing vs. 4% grazing), while rams’ feeding behavior was dominated with grazing (24% grazing vs. 12% browsing). Lying was a predominant diurnal behavior in both wethers (46%) and rams (35%), mostly during the midday period. Wethers had a higher value for DEI than rams during the morning and post-midday periods. This study established that (1) the utilization of woodland understory foliage by small ruminants can be increased by lowering plant height, and (2) both vegetation characteristics and diurnal period are important factors for influencing small ruminants’ behavior while stocked in woodlands around summer.
Physical and Mechanical Properties of Poplar Clones and Rapid Prediction of the Properties by Near Infrared Spectroscopy
Authors: Jia Ru , Wang Yurong , Wang Rui , Chen Xu
Published: February 2021

In order to understand the physical and mechanical properties of poplar clones, and explore a method for their quick evaluation, the air dry density, modulus of rupture (MOR), modulus of elasticity (MOE), and compressive strength parallel to grains of three new bred poplar clones were explored and the prediction models with the highest accuracy were established by near infrared spectroscopy (NIRs). Clone 50 (Populus deltoides CL. ‘55/65′) had the highest air dry density, MOR, MOE, and compressive strength parallel to grains in the three clones. For clone 50 and 108 (Populus euramericana cv. ‘Guariento’), the mechanical properties of sapwood were better than those of heartwood, and the sapwood of clone 50 also had a better air dry density than that of heartwood. There were significant positive correlations between the air dry density and mechanical properties, with correlation coefficients above 0.68. Prediction models with better effects could be obtained by using information on the cross section for the air dry density and mechanical properties. First derivative+ Savitzky–Golay (S-G) smoothing methods were employed for the air dry density and MOR, and multiple scattering correction (MSC)+ S-G smoothing methods were used when establishing prediction models of MOE and compressive strength parallel to grains.
Effects of Large-Scale Nitrogen Fertilization on Insect–Plant Interactions in the Canopy of Tall Alder Trees with N2-Fixing Traits in a Cool Temperate Forest
Authors: Lee Jin , Nakamura Masahiro , Hiura Tsutom
Published: February 2021

Nitrogen (N) deposition is expected to influence forests. The effects of large-scale N fertilization on canopy layer insect–plant interactions in stands of tall, atmospheric nitrogen (N2)-fixing tree species have never been assessed. We conducted a large-scale fertilization experiment (100 kg N ha−1 year−1 applied to approximately 9 ha) over three years (2012–2014) in a cool temperate forest in northern Japan. Our goal was to evaluate relational responses between alder (Alnus hirsuta [Turcz.]) and their insect herbivores to N deposition. Specifically, we assessed leaf traits (N concentration, C:N ratio, condensed tannin concentration, and leaf mass per unit area (LMA)) and herbivory by three feeding guilds (leaf damage by chewers and the densities of gallers and miners) between the fertilized site and an unfertilized control. Fertilization led to increased galler density in spring 2013 and increased leaf damage by chewers in late summer 2014. For leaf traits, the LMA decreased in spring 2013 and late summer 2014, and the C:N ratio decreased in late summer 2013. The N and condensed tannin concentrations remained unchanged throughout the study period. There was a negative correlation between LMA and leaf damage by chewers, but LMA was not correlated with galler density. These results show that large-scale N fertilization had a positive plant-mediated (i.e., indirect) effect on leaf damage by chewers via a decrease in LMA in the canopy layer. Changes in physical defenses in canopy leaves may be a mechanism by which N fertilization affects the herbivory in tall N2-fixing trees.
A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects
Authors: Gao Mingyu , Qi Dawei , Mu Hongbo , Chen Jianfeng
Published: February 2021

In recent years, due to the shortage of timber resources, it has become necessary to reduce the excessive consumption of forest resources. Non-destructive testing technology can quickly find wood defects and effectively improve wood utilization. Deep learning has achieved significant results as one of the most commonly used methods in the detection of wood knots. However, compared with convolutional neural networks in other fields, the depth of deep learning models for the detection of wood knots is still very shallow. This is because the number of samples marked in the wood detection is too small, which limits the accuracy of the final prediction of the results. In this paper, ResNet-34 is combined with transfer learning, and a new TL-ResNet34 deep learning model with 35 convolution depths is proposed to detect wood knot defects. Among them, ResNet-34 is used as a feature extractor for wood knot defects. At the same time, a new method TL-ResNet34 is proposed, which combines ResNet-34 with transfer learning. After that, the wood knot defect dataset was applied to TL-ResNet34 for testing. The results show that the detection accuracy of the dataset trained by TL-ResNet34 is significantly higher than that of other methods. This shows that the final prediction accuracy of the detection of wood knot defects can be improved by TL-ResNet34.
Seasonal Variations in Carbon, Nitrogen, and Phosphorus Stoichiometry of a Robinia pseudoacacia Plantation on the Loess Hilly Region, China
Authors: Dong Congguo , Qiao Yuning , Cao Yang , Chen Yunming , Wu Xu , Xue Wenyan
Published: February 2021

Seasonal variations in stoichiometry are a crucial regulatory mechanism for plant communities that respond to environmental changes. However, the seasonal characteristics of stoichiometry in plants, litter, and soil are poorly understood, especially in plantation ecosystems. Therefore, we explored the seasonal variations of C, N, and P contents and ratios between plants, litter, and soil of a Robinia pseudoacacia plantation on the Loess Plateau in China in 2017. The results indicate that the C, N, P contents and ratios in plants, litter and soil showed different seasonal patterns. The N and P contents of tree and shrub leaves substantially decreased over the growing season, while the C:N, C:P, and N:P ratios exhibited the opposite trend. The utilization efficiency of the N and P elements by trees and shrubs gradually increased with the change of the growing season. These results suggest that the C:N:P stoichiometry of plants was more sensitive to seasonal changes than the litter and soil; therefore, the potential impacts of time should be considered when using stoichiometry to explore the utilization of plant nutrients. Additionally, the P content between tree leaves and soil and the N content between herb leaves and soil were significantly positively correlated, indicating that the growth of the tree and herb layer in the R. pseudoacacia plantation in the area was restricted by P and N, respectively. Meanwhile, the N content in the leaves between trees and herbs showed a significant negative correlation, indicating that N competition existed between R. pseudoacacia and understory herbs, which was not conducive to the effective use of environmental resources by the R. pseudoacacia plantation ecosystem. This study contributes to vegetation restoration and plantation management on the Loess Plateau and provides basic information for global stoichiometric analyses.
Use of Social Media Data in Disaster Management: A Survey
Authors: Phengsuwan Jedsada , Shah Tejal , Thekkummal Nipun Balan , Wen Zhenyu , Sun Rui , Pullarkatt Divya , Thirugnanam Hemalatha , Ramesh Maneesha Vinodini , Morgan Graham , James Philip , Ranjan Rajiv
Published: February 2021

Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.
Effect of Ripening on the Phenolic Composition and Mineral Content of Three Varieties of Olive Fruits
Authors: Fernández-Poyatos María del Pilar , Llorent-Martínez Eulogio J. , Ruiz-Medina Antonio
Published: February 2021

The phenolic composition and mineral content of Cornezuelo, Cornicabra and Picual olive fruit varieties were investigated during olive ripening in two different harvesting seasons (2017/2018 and 2018/2019). Phytochemical profiles were evaluated by high-performance liquid chromatography (HPLC) with diode-array and mass spectrometry detection. Mineral contents were determined by inductively coupled plasma-mass spectrometry (ICP-MS). Twenty-five compounds were characterized and the main ones quantified. These compounds corresponded mostly to secoiridoids, the main ones being oleuropein, oleoside/secologanoside, oleoside-11-methylester, and oleuropein and comselogoside isomers. Total phenolic contents reached the highest values between December and January, coinciding with the usual harvesting date. This trend was observed in both harvesting seasons, although higher phenolic contents were recorded in season 2018/2019. This was due to the different weather conditions, which caused a lower olive production in season 2017/2018. No clear tendency was observed between mineral content and harvest time in any of the studied seasons. The highest concentration of total phenolics was obtained in Cornezuelo variety (840 mg/100 g) in January 2019 (season 2018/2019). Picual and Cornicabra varieties reached concentrations of 670 mg/100 g and 530 mg/100 g, respectively, also in the last harvesting dates of season 2018/2019.
Biological Effect of Different Spinach Extracts in Comparison with the Individual Components of the Phytocomplex
Authors: Arru Laura , Mussi Francesca , Forti Luca , Buschini Annamaria
Published: February 2021

The Mediterranean-style diet is rich in fruit and vegetables and has a great impact on the prevention of major chronic diseases, such as cardiovascular diseases and cancer. In this work we investigated the ability of spinach extracts obtained by different extraction methods and of the single main components of the phytocomplex, alone or mixed, to modulate proliferation, antioxidant defense, and genotoxicity of HT29 human colorectal cells. Spinach extracts show dose-dependent activity, increasing the level of intracellular endogenous reactive oxygen species (ROS) when tested at higher doses. In the presence of oxidative stress, the activity is related to the oxidizing agent involved (H2O2 or menadione) and by the extraction method. The single components of the phytocomplex, alone or mixed, do not alter the intracellular endogenous level of ROS but again, in the presence of an oxidative insult, the modulation of antioxidant defense depends on the oxidizing agent used. The application of the phytocomplex extracts seem to be more effective than the application of the single phytocomplex components.
Modelling and Classification of Apple Textural Attributes Using Sensory, Instrumental and Compositional Analyses
Authors: Bejaei Masoumeh , Stanich Kareen , Cliff Margaret A.
Published: February 2021

Textural characteristics of fruit are important for their quality, storability, and consumer acceptance. While texture can be evaluated instrumentally or sensorially, instrumental measurements are preferred if they can be reliably related to human perception. The objectives of this research were to validate instrumental measurements with sensory determinations, develop a classification scheme to group apples by their textural characteristics, and create models to predict sensory attributes from instrumental and compositional analyses. The textural characteristics (crispness, hardness, juiciness, and skin toughness) of 12 apple cultivars were evaluated on new and established cultivars. Fruit was also evaluated using five instrumental measurements from TA.XTplus Texture Analyzer, and three compositional determinations. The experiment was repeated for analysis and validation purposes. Principal component (PC) analysis revealed that 95.88% of the variation in the instrumental determinations could be explained by two components (PC 1 and PC 2); which were highly correlated with flesh firmness and skin strength, respectively. Four textural groups of apples were identified, and the accuracy of classification was established at 94.44% by using linear discriminant analysis. The predictive models that were developed between the sensory and instrumental-compositional data explained more than 85% of the variation in the data for hardness and crispness, while models for juiciness and skin toughness were more complex. The work should assist industry personnel to reduce time-consuming and costly sensory testing, yet have an appreciation of the textural traits as perceived by the consumer.
Assessing Suitability of Sorghum to Alleviate Sub-Saharan Nutritional Deficiencies through the Nutritional Water Productivity Index in Semi-Arid Regions
Authors: Hadebe Sandile T. , Modi Albert T. , Mabhaudhi Tafadzwanashe
Published: February 2021

Lack of cereal nutritional water productivity (NWP) information disadvantages linkages of nutrition to water–food nexus as staple food crops in Sub-Saharan Africa (SSA). This study determined the suitability of sorghum (Sorghum bicolor L. Moench) genotypes to alleviate protein, Zn and Fe deficiency under water-scarce dryland conditions through evaluation of NWP. Sorghum genotypes (Macia, Ujiba, PAN8816, IsiZulu) NWP was quantified from three planting seasons for various sorghum seed nutrients under dryland semi-arid conditions. Seasons by genotypes interaction highly and significantly affected NWPStarch, Ca, Cu, Fe, and significantly affected NWPMg, K, Na, P, Zn. Genotypic variations highly and significantly affected sorghum NWPProtein, Mn. Macia exhibited statistically superior NWPprotein (13.2–14.6 kg·m−3) and NWPZn (2.0–2.6 g·m−3) compared to other tested genotypes, while Macia NWPFe (2.6–2.7 g·m−3) was considerably inferior to that of Ujiba and IsiZulu landraces under increased water scarcity. Excellent overall NWPprotein, Fe and Zn under water scarcity make Macia a well-rounded genotype suitable to alleviating food and nutritional insecurity challenges in semi-arid SSA; however, landraces are viable alternatives with limited NWPprotein and Zn penalty under water-limited conditions. These results underline genotype selection as a vital tool in improving “nutrition per drop” in semi-arid regions.