bluuebunny/crossref_metadata_2025 huggingface dataset:
Epistemic Disobedience and Grief in Academia
Authors: Ramirez Carla C.
Published: August 2021

Drawing on conversations with foreign women in academic positions at one major University in Norway, this article is inspired by Barad’s and Haraway’s theorizing on how matter and discourse are mutually constituted through a diffractive approach. Understanding diffraction as an embodied engagement, a becoming with the data through shared entanglements, this article argues that the researcher’s personal background cannot be separated from the data produced. Departing from the decolonial theorist Castro-Gómez concept ‘hubris of zero-point epistemology’, the existence of an abstract and transcendental western universalism, where ‘the observer observes without been observed’ (Domínguez 2020; Mignolo 2009), assemblages of foreign female academics are explored through posthuman feminism and decolonial perspectives (Jackson and Mazzei 2012; Taguchi 2012; Puwar 2004). Through immersion in assemblages of contradictions, strength, and resistance, this article contends that policymakers’ good intentions of diversity in higher education, and the existence of different bodies, are shaking the world of academia, albeit slowly. Academia is still immersed in zero-point epistemology, favoring western, upper-class, paternalist, and meritocratic thought, detached from academics’ embodied knowledge. This brings into existence ‘bodies out of place’, re/producing grief, resistance, and epistemic disobedience when some academics are not suitable of becoming real academics.
Voltage Regulation of an Isolated DC Microgrid with a Constant Power Load: A Passivity-based Control Design
Authors: Magaldi Guillermo Luciano , Serra Federico Martin , de Angelo Cristian Hernán , Montoya Oscar Danilo , Giral-Ramírez Diego Armando
Published: August 2021

Passivity-based nonlinear control for an isolated microgrid system is proposed in this paper. The microgrid consists of a photovoltaic array and a battery energy storage connected to a point of common converters, supplying a constant power load. The purpose of this control strategy is to maintain the output direct current voltage in its reference value under load variations, improving battery interaction. The system is represented by its state space averaged model and the proposed controller is designed using the interconnection and damping assignment strategy, which allows obtaining controller parameters while ensuring the closed-loop system stability. The unknown constant power load is estimated using an observer based on the energy function of the system. The behavior of the proposed control strategy is validated with simulation and experimental results.
The Spoofing Detection of Dynamic Underwater Positioning Systems (DUPS) Based on Vehicles Retrofitted with Acoustic Speakers
Authors: Lemieszewski Łukasz , Radomska-Zalas Aleksandra , Perec Andrzej , Dobryakova Larisa , Ochin Evgeny
Published: August 2021

The need of precision for underwater positioning and navigation should be considered as strict as those present at the sea surface. GNSS provides 4D positioning (XYZT). Each satellite contains two rubidium and two cesium atomic clocks. They are monitored by an atomic clock on the ground, and the entire system is constantly calibrated to a universal time standard, Coordinated Universal Time (UTC). GNSS receivers determine the time T to within 100 billionths of a second without the cost of owning, operating and maintaining an atomic clock. Of particular importance is the measurement of XYZT underwater. We assume that some surface vehicles are additionally equipped with an Acoustic Speaker, which transmits the XY coordinates of the vessel with an indication of accuracy and the time T of the vessel. Submarine vehicles determine their position by help of acoustic signals from several surface acoustic sources using the Time of Arrival (ToA) algorithm. Detection of Spoofing for the Dynamic Underwater Positioning Systems (DUPS) based on vehicles retrofitted with acoustic speakers is very actual problem. Underwater spoofing works as follows: N acoustic speaker on N ships transmit the coordinates {xi,yi,ti}, i=1,N¯. GNSS signals are susceptible to interference due to their very low power (−130 dBm) and can be easily jammed by other sources, which may be accidental or intentional. The spoofer, like an underwater vehicle, receives these signals from N vessels, distorts them and transmits with increased acoustic power. All receivers into the spoofed area will calculate the same coordinates, so the indication of the coincidence of coordinates from a pair of diversity receivers is an indication of spoofing detection.
LIHL: Design of a Novel Loop Interlocked Hardened Latch
Authors: Xu Hui , Liu Xuan , Yu Guo , Liang Huaguo , Huang Zhengfeng
Published: August 2021

A single event causing a double-node upset is likely to occur in nanometric complementary metal-oxide-semiconductor (CMOS). Contemporary hardened latch designs are insufficient in meeting high reliability, low power consumption, and low delay. This paper presents a novel soft error hardened latch, known as a loop interlocked hardened latch (LIHL). This latch consists of four modified cross-coupled elements, based on dual interlocked storage cell (DICE) latch. The use of these elements hardens the proposed LIHL to soft errors. The simulation results showed that the LIHL has single-event double upset (SEDU) self-recoverability and single-event transient (SET) pulse filterability. This latch also reduces power dissipation and propagation delay, compared to other SEDU or SET-tolerant latches.
Wild Animal Information Collection Based on Depthwise Separable Convolution in Software Defined IoT Networks
Authors: Cao Qinghua , Yu Lisu , Wang Zhen , Zhan Shanjun , Quan Hao , Yu Yan , Khan Zahid , Koubaa Anis
Published: August 2021

The wild animal information collection based on the wireless sensor network (WSN) has an enormous number of applications, as demonstrated in the literature. Yet, it has many problems, such as low information density and high energy consumption ratio. The traditional Internet of Things (IoT) system has characteristics of limited resources and task specificity. Therefore, we introduce an improved deep neural network (DNN) structure to solve task specificity. In addition, we determine a programmability idea of software-defined network (SDN) to solve the problems of high energy consumption ratio and low information density brought about by low autonomy of equipment. By introducing some advanced network structures, such as attention mechanism, residuals, depthwise (DW) convolution, pointwise (PW) convolution, spatial pyramid pooling (SPP), and feature pyramid networks (FPN), a lightweight object detection network with a fast response is designed. Meanwhile, the concept of control plane and data plane in SDN is introduced, and nodes are divided into different types to facilitate intelligent wake-up, thereby realizing high-precision detection and high information density of the detection system. The results show that the proposed scheme can improve the detection response speed and reduce the model parameters while ensuring detection accuracy in the software-defined IoT networks.
An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices
Authors: Kuo Tz-Hau , Teng Chi-Min , Wu Ming-Feng , Wen Chih-Yu
Published: August 2021

This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.
Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell
Authors: Khan Shoeb Ahmad , Ahmad Shafiq , Sarwar Adil , Tariq Mohd , Ahmad Javed , Asim Mohammed , Soliman Ahmed T. , Hossain Md. Alamgir
Published: August 2021

The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.
An Ultra-Low-Power CMOS Supercapacitor Storage Unit for Energy Harvesting Applications
Authors: Gogolou Vasiliki , Kozalakis Konstantinos , Koutroulis Eftichios , Doumenis Gregory , Siskos Stylianos
Published: August 2021

This work presents an ultra-low-power CMOS supercapacitor storage unit suitable for a plethora of low-power autonomous applications. The proposed unit exploits the unregulated voltage output of harvesting circuits (i.e., DC-DC converters) and redirects the power to the storage elements and the working loads. Being able to adapt to the input energy conditions and the connected loads’ supply demands offers extended survival to the system with the self-startup operation and voltage regulation. A low-complexity control unit is implemented which is composed of power switches, comparators and logic gates and is able to supervise two supercapacitors, a small and a larger one, as well as a backup battery. Two separate power outputs are offered for external load connection which can be controlled by a separate unit (e.g., microcontroller). Furthermore, user-controlled parameters such as charging and discharging supercapacitor voltage thresholds, provide increased versatility to the system. The storage unit was designed and fabricated in a 0.18 um standard CMOS process and operates with ultra-low current consumption of 432 nA at 2.3 V. The experimental results validate the proper operation of the overall structure.
On the Detection of Low-Rate Denial of Service Attacks at Transport and Application Layers
Authors: Vedula Vasudha , Lama Palden , Boppana Rajendra V. , Trejo Luis A.
Published: August 2021

Distributed denial of service (DDoS) attacks aim to deplete the network bandwidth and computing resources of targeted victims. Low-rate DDoS attacks exploit protocol features such as the transmission control protocol (TCP) three-way handshake mechanism for connection establishment and the TCP congestion-control induced backoffs to attack at a much lower rate and still effectively bring down the targeted network and computer systems. Most of the statistical and machine/deep learning-based detection methods proposed in the literature require keeping track of packets by flows and have high processing overheads for feature extraction. This paper presents a novel two-stage model that uses Long Short-Term Memory (LSTM) and Random Forest (RF) to detect the presence of attack flows in a group of flows. This model has a very low data processing overhead; it uses only two features and does not require keeping track of packets by flows, making it suitable for continuous monitoring of network traffic and on-the-fly detection. The paper also presents an LSTM Autoencoder to detect individual attack flows with high detection accuracy using only two features. Additionally, the paper presents an analysis of a support vector machine (SVM) model that detects attack flows in slices of network traffic collected for short durations. The low-rate attack dataset used in this study is made available to the research community through GitHub.
Adaptive Sparse Representation of Continuous Input for Tsetlin Machines Based on Stochastic Searching on the Line
Authors: Abeyrathna Kuruge Darshana , Granmo Ole-Christoffer , Goodwin Morten
Published: August 2021

This paper introduces a novel approach to representing continuous inputs in Tsetlin Machines (TMs). Instead of using one Tsetlin Automaton (TA) for every unique threshold found when Booleanizing continuous input, we employ two Stochastic Searching on the Line (SSL) automata to learn discriminative lower and upper bounds. The two resulting Boolean features are adapted to the rest of the clause by equipping each clause with its own team of SSLs, which update the bounds during the learning process. Two standard TAs finally decide whether to include the resulting features as part of the clause. In this way, only four automata altogether represent one continuous feature (instead of potentially hundreds of them). We evaluate the performance of the new scheme empirically using five datasets, along with a study of interpretability. On average, TMs with SSL feature representation use 4.3 times fewer literals than the TM with static threshold-based features. Furthermore, in terms of average memory usage and F1-Score, our approach outperforms simple Multi-Layered Artificial Neural Networks, Decision Trees, Support Vector Machines, K-Nearest Neighbor, Random Forest, Gradient Boosted Trees (XGBoost), and Explainable Boosting Machines (EBMs), as well as the standard and real-value weighted TMs. Our approach further outperforms Neural Additive Models on Fraud Detection and StructureBoost on CA-58 in terms of the Area Under Curve while performing competitively on COMPAS.
A Fully-Integrated 180 nm CMOS 1.2 V Low-Dropout Regulator for Low-Power Portable Applications
Authors: Pérez-Bailón Jorge , Calvo Belén , Medrano Nicolás
Published: August 2021

This paper presents the design and postlayout simulation results of a capacitor-less low dropout (LDO) regulator fully integrated in a low-cost standard 180 nm Complementary Metal-Oxide-Semiconductor (CMOS) technology which regulates the output voltage at 1.2 V from a 3.3 to 1.3 V battery over a −40 to 120 °C temperature range. To meet with the constraints of system-on-chip (SoC) battery-operated devices, ultralow power (Iq = 8.6 µA) and minimum area consumption (0.109 mm2) are maintained, including a reference voltage Vref = 0.4 V. It uses a high-gain dynamically biased folded-based error amplifier topology optimized for low-voltage operation that achieves an enhanced regulation-fast transient performance trade-off.
Influence of Environmental Changes Due to Altitude on Performance, Fuel Consumption and Emissions of a Naturally Aspirated Diesel Engine
Authors: Ceballos John Jairo , Melgar Andrés , Tinaut Francisco V.
Published: August 2021

The present study shows the effects of environmental conditions (atmospheric temperature, pressure and relative humidity) due to altitude changes on performance, fuel consumption and emissions in a naturally aspirated diesel engine. Due to changes in altitude, the atmospheric conditions are altered, mainly the air density, associated to hydrostatic pressure, temperature profile and humidity and relative nitrogen/oxygen ratio, thus modifying the engine intake conditions. The study considers changes in altitude from sea level to 2500 m above sea level, which are representative of the orographic conditions in Ecuador. As a main part of this research, a parametric study of variation of atmospheric temperature, pressure and relative humidity is carried out in AVL BOOST™, showing the effects on mean effective pressure, fuel consumption and specific pollutant emissions (CO2, NOx, CO and soot). The study considers effects at regional level (change from an altitude to another) and local level (changes in the atmospheric conditions due to local anticyclone or storm, temperature and humidity). The quantitative effects are expressed in the form of sensitivity coefficients, e.g., relative change in an engine output variable due to the change in atmospheric pressure, temperature or humidity. In addition, several global correlations have been obtained to provide analytical expressions to summarize all results obtained, showing the separate effect of pressure and temperature on each engine performance variable.
Experimental Characterization of an Alkaline Electrolyser and a Compression System for Hydrogen Production and Storage
Authors: Pietra Andrea , Gianni Marco , Zuliani Nicola , Malabotti Stefano , Taccani Rodolfo
Published: August 2021

Storing renewable energy in chemicals, like hydrogen, can bring various benefits like high energy density, seasonal storability, possible cost reduction of the final product, and the potential to let renewable power penetrate other markets and to overcome their intermittent availability. In the last year’s production of this gas from renewable energy sources via electrolysis has grown its reputation as one feasible solution to satisfy future zero-emission energy demand. To extend the exploitation of Renewable Energy Source (RES), small-scale conversion plants seem to be an interesting option. In view of a possible widespread adoption of these types of plants, the authors intend to present the experimental characterization of a small-scale hydrogen production and storage plant. The considered experimental plant is based on an alkaline electrolyser and an air-driven hydrogen compression and storage system. The results show that the hydrogen production-specific consumption is, on average, 77 kWh/kgH2. The hydrogen compressor energy requirement is, on average, 15 kWh/kgH2 (data referred to the driving compressed air). The value is higher than data found in literature (4.4–9.3 kWh/kgH2), but the difference can be attributed to the small size of the considered compressor and the choice to limit the compression stages.
Analyzing the Driving Forces behind CO2 Emissions in Energy-Resource-Poor and Fossil-Fuel-Centered Economies: Case Studies from Taiwan, Japan, and South Korea
Authors: Huang Yun-Hsun , Wu Jung-Hua , Huang Hao-Syuan
Published: August 2021

Based on the strong similarities between energy-resource-poor and fossil-fuel-centered economies (e.g., Taiwan, Japan, and South Korea) in terms of economy, culture, and energy usage characteristics, they should be analyzed collectively. This study adopted two-tier input-output structural decomposition analysis to identify the driving forces behind CO2 emissions to these countries to the formulation of effective environmental policy. Data from the World Input-Output Database was used to decompose relative changes in CO2 emissions into a range of technological advances, factor substitution, and final demand effects. Technological advances in energy (direct) contributed to a 77% reduction in Taiwan and a 34% reduction in South Korea. This is a clear indication that improving energy efficiency via technological advances should be a priority. In Japan in particular, there was a 22% reduction in CO2 emissions attributable to technological advances in materials; hence, it is recommended that Taiwan and South Korea work to extensively develop eco-industrial parks to create industry clusters to promote resource/energy efficiency and reductions in CO2 emissions. Decomposition results based on factor substitution revealed that a variety of strategies will be required, such as switching to fuels that are less carbon intensive, promoting the adoption of renewable energies, and implementing clean-coal technologies.
Analyzing Regional and Local Changes in Irradiance during the 2019 Total Solar Eclipse in Chile, Using Field Observations and Analytical Modeling
Authors: Castillejo-Cuberos Armando , Cardemil José Miguel , Escobar Rodrigo
Published: August 2021

Solar eclipses are astronomic phenomena in which the Earth’s moon transits between the planet and the Sun, projecting a shadow onto the planet’s surface. As solar power installed capacity increases, detailed studies of this region-wide phenomenon’s effect in irradiance is of interest; however, the literature mainly reports its effects on localized scales. A measurement campaign spanning over 1400 km was pursued for the 2 July 2019 total solar eclipse in Chile, to register the event and establish a modeling framework to assess solar eclipse effects in irradiance over wide regional scales. This work describes the event and presents an estimation framework to decompose atmospheric and eclipse effects on irradiance. An analytical model was applied to study irradiance attenuation throughout the Chilean mainland territory, using satellite-derived and astronomical data as inputs compared to ground measurements in eight stations. Results showed good agreement between model and observations, with Mean Bias Errors of −0.008 to 0.98 W/m2 for Global Horizontal Irradiance and −0.004 to −4.664 W/m2 for Direct Normal Irradiance, with Normalized Root Mean Squared Errors of 0.7–5.8% and 1.4–12.2%, respectively. Energy losses due to obscuration corresponded between 20–40% for Global Horizontal Irradiance and 25–50% for Direct Normal Irradiance over Chilean territory.
Effect of Operating Conditions on the Performance of Gas–Liquid Mixture Roots Pumps
Authors: Guo Qing , Luo Kai , Li Daijin , Huang Chuang , Qin Kan
Published: August 2021

The performance of the gas–liquid mixture Roots pump at different operating conditions is investigated in this paper. The pump efficiency was first increased from 48% to 64%, and then decreased to 59% with the increased inlet CO2 volume fraction (from 0.8 to 1). The increased rotational speed (from 1000 rpm to 4000 rpm) and pressure ratio (from 2 to 10) can lead to a reduction in the pump’s efficiency from 67% to 43% and from 48% to 33%, respectively. The variation in the pump’s efficiency is affected by the volumetric efficiency and the flow efficiency simultaneously. The high pressure and the CO2 volume fraction in the outlet zone can increase the leakage, leading to a reduction in the volumetric efficiency. The flow efficiency decreases with the increase in the local pressure at the outlet zone and the backflow density. The outlet zone pressure can also affect the fluid properties by changing the density of the gas phase. Therefore, the combined effect of the outlet zone pressure and the working fluid properties is considered to be the main factor affecting the performance. This paper further explores the suitability of Roots pumps for compressing gas–liquid mixtures.
Adaptive Online Sequential Extreme Learning Machine with Kernels for Online Ship Power Prediction
Authors: Peng Xiuyan , Wang Bo , Zhang Lanyong , Su Peng
Published: August 2021

With the in-depth penetration of renewable energy in the shipboard power system, the uncertainty of its output power and the variability of sea conditions have brought severe challenges to the control of shipboard integrated power system. In order to provide additional accurate signals to the power control system to eliminate the influence of uncertain factors, this study proposed an adaptive kernel based online sequential extreme learning machine to accurately predict shipboard electric power fluctuation online. Three adaptive factors are introduced, which control the kernel function scale adaptively to ensure the accuracy and speed of the algorithm. The electric power fluctuation data of real-ship under two different sea conditions are used to verify the effectiveness of the algorithm. The simulation results clearly demonstrate that in the case of ship power fluctuation prediction, the proposed method can not only meet the rapidity demand of real-time control system, but also provide accurate prediction results.
Geospatial Analysis and Environmental Impact Assessment of a Holistic and Interdisciplinary Approach to the Biogas Sector
Authors: Bedoić Robert , Smoljanić Goran , Pukšec Tomislav , Čuček Lidija , Ljubas Davor , Duić Neven
Published: August 2021

Crop-based biogas energy production, in combination with electricity generation under subsidy schemes, is no longer considered a favourable business model for biogas plants. Switching to low-cost or gate fee feedstocks and utilising biogas via alternative pathways could contribute to making existing plants fit for future operations and could open up new space for further expansion of the biogas sector. The aim of this study was to combine a holistic and interdisciplinary approach for both the biogas production side and the utilisation side to evaluate the impact of integrating the biogas sector with waste management systems and energy systems operating with a high share of renewable energy sources. The geospatial availability of residue materials from agriculture, industry and municipalities was assessed using QGIS software for the case of Northern Croatia with the goal of replacing maize silage in the operation of existing biogas plants. Furthermore, the analysis included positioning new biogas plants, which would produce renewable gas. The overall approach was evaluated through life cycle assessment using SimaPro software to quantify the environmental benefits and identify the bottlenecks of the implemented actions. The results showed that the given feedstocks could replace 212 GWh of biogas from maize silage in the relevant region and create an additional 191 GWh of biomethane in new plants. The LCA revealed that the proposed measures would contribute to the decarbonisation of natural gas by creating environmental benefits that are 36 times greater compared to a business-as-usual concept. The presented approach could be of interest to stakeholders in the biogas sector anywhere in the world to encourage further integration of biogas technologies into energy and environmental transitions.
Internet of Things and Other E-Solutions in Supply Chain Management May Generate Threats in the Energy Sector—The Quest for Preventive Measures
Authors: Dobrowolski Zbysław
Published: August 2021

Energy firms are the beneficiaries and initiators of innovation, and energy investments are a crucial area of business activity that is specially protected in any country. This is no wonder, as energy security is the basis for the functioning of states and economies. The Internet of Things and Big Data create both new challenges and new threats. This study aimed to identify the potential threats and determine preventive measures, as well as to establish the agile principles related to energy firms’ logistics. The method of the narrative summary in combination with the literature searching method was used. Two conclusions emerged: first, research serves to develop the discipline of management science; second, the identification of risks associated with innovation serves practitioners. In addition, the study defined further research directions.
Operation Cycle of Diesel CR Injection Pump via Pressure Measurement in Piston Working Chamber
Authors: Chiavola Ornella , Frattini Edoardo , Lancione Simone , Palmieri Fulvio
Published: August 2021

The paper is devoted to the analysis of the operating cycle of a high-pressure injection pump used in common rail systems. The investigation is based on experimental activities, and it is carried out in a novel pump set-up that allows measurements of the instantaneous pressure in the piston working chamber. A single plunger pump has been equipped with a piezo-resistive pressure transducer which allows for the measurement of the pressure signal during pump operation on a test rig. The paper describes the experimental set-up, the modified injection pump equipped with the pressure transducer, and the experimental tests carried out. Main results obtained using a standard commercial diesel fuel are discussed at first; secondly, the focus moves on to the use of an alternative fuel (biodiesel) whose features in terms of bulk modulus, viscosity, and density significantly differ from the reference fuel. Based on the characteristics of the pump operating cycle, the fuel suction and delivery processes are analyzed, pointing out how the used fuel type is reflected on them. The investigations are aimed at describing the operating characteristics of the pump, focusing the attention on those features playing a fundamental role on the global efficiency of the pump. The amplitudes of the pump-work phases, the ranges of pressure fluctuations, and the pressure-rise rates are quantified and reported, providing crucial indications for lumped parameter modeling and design activities in the field of current generation high-pressure injection pumps.
Charging for Collaboration: Exploring the Dynamics of Temporal Fit in Interdependent Constellations for Innovation
Authors: van Galen Wouter P. L. , Walrave Bob , Dolmans Sharon A. M. , Romme A. Georges L.
Published: August 2021

The development of a suitable public charging system for electric vehicles relies on inputs from many complementary organizations that need to synchronize interdependencies across different activities, organizations, and industries. Research on temporal fit has focused on synchronizing activities within or external to the organization, rather than exploring synchronization across multiple organizations with highly interdependent yet colliding temporal structures and multiple time-givers. Drawing on a case study of a collaborative effort to create a national charging infrastructure for electric vehicles, we theorize the interplay between various highly interdependent actors. The resulting theory posits that actors combine and shift between different innovation practices to organize time and explains how multiple, yet interdependent actors engaging in temporal work attempt to accomplish temporal fit. Three entrainment dynamics are identified: (1) temporal tug-of-war through ecosystem configuration; (2) temporal dictating through group politics; and (3) ecosystem navigation through temporal ambivalence. These dynamics arise both between and within groups of actors when they coordinate innovation practices across multiple temporal structures and time-givers. Together, the simultaneous pursuit of synchronization within and across these different coalitions appears to constrain the realization of the collective goal.
Assessing Tree Drought Resistance and Climate-Growth Relationships under Different Tree Age Classes in a Pinus nigra Arn. ssp. salzmannii Forest
Authors: Lucas-Borja Manuel Esteban , Bose Arun K. , Andivia Enrique , Candel-Pérez David , Plaza-Álvarez Pedro A. , Linares Juan C.
Published: August 2021

The magnitude of drought impact in forest ecosystems depends on which group of trees are more severely affected; greater mortality of smaller trees can modulate the trajectories of succession, while the mortality of larger trees can disproportionately offset the ecosystem’s carbon balance. Several studies have documented a greater vulnerability of large trees to extreme droughts while some other studies reported a greater growth reduction in smaller trees during droughts. We tested these hypotheses by comparing tree basal area increment (BAI), drought resistance (i.e., magnitude of growth decline during drought), and resilience (i.e., magnitude of growth recovery after drought) across five different age-classes in black pine (Pinus nigra Arn. ssp. salzmannii) forests in Spain. Our results showed that the BAI patterns, drought resistance, and resilience were strongly influenced by tree age-classes. In addition, the effect of climatic water balance (precipitation minus potential evapotranspiration) on BAI significantly varied among age-classes. The effect of water balance on BAI was lower for younger age-classes (1–39 years of age) compared to older age-classes. We observed a greater growth reduction (i.e., lower resistance) in older trees (>40 years of age) during droughts compared to younger trees (<40 years of age). However, all trees, irrespective of their ages, were able to recover the growth rates after the drought. In general, younger trees showed a greater capacity in recovering the growth rate (i.e., more resilient) than older trees. We detected no significant effects of stand basal area and stand density on BAI, drought resistance, and resilience. Overall, our results indicated that growth of older trees was more negatively affected during drought. Therefore, these older/larger trees can be selected for commercial thinning, or can be released from competition, which can minimize the potential impacts of future droughts in black pine forests in Spain.
Genetic Differentiation and Demographic History of Three Cerris Oak Species in China Based on Nuclear Microsatellite Makers
Authors: Chen Xiao-Dan , Zhang Xiao , Zhang Hao , Zhou Tao , Zhao Yue-Mei , Yang Jia , Zhao Gui-Fang
Published: August 2021

Knowledge of interspecific divergence and population expansions/contractions of dominant forest trees in response to geological events and climatic oscillations is of major importance to understand their evolution and demography. However, the interspecific patterns of genetic differentiation and spatiotemporal population dynamics of three deciduous Cerris oak species (Q. acutissima, Q. variabilis and Q. chenii) that are widely distributed in China remain poorly understood. In this study, we genotyped 16 nuclear loci in 759 individuals sampled from 44 natural populations of these three sibling species to evaluate the plausible demographical scenarios of the closely related species. We also tested the hypothesis that macro- and microevolutionary processes of the three species had been triggered and molded by Miocene–Pliocene geological events and Quaternary climatic change. The Bayesian cluster analysis showed that Q. acutissima and Q. chenii were clustered in the same group, whereas Q. variabilis formed a different genetic cluster. An approximate Bayesian computation (ABC) analyses suggested that Q. variabilis and Q. acutissima diverged from their most common ancestor around 19.84 Ma, and subsequently Q. chenii diverged from Q. acutissima at about 9.6 Ma, which was significantly associated with the episodes of the Qinghai–Tibetan Plateau (QTP). In addition, ecological niche modeling and population history analysis showed that these three Cerris oak species repeatedly underwent considerable ‘expansion–contraction’ during the interglacial and glacial periods of the Pleistocene, although they have varying degrees of tolerance for the climatic change. Overall, these findings indicated geological and climatic changes during the Miocene–Pliocene and Pleistocene as causes of species divergence and range shifts of dominant tree species in the subtropical and warm temperature areas in China.
Potential Factors behind the Decline of Pinus pinea Nut Production in Mediterranean Pine Forests
Authors: El Khoury Yara , Noujeim Elise , Bubici Giovanni , Tarasco Eustachio , Al Khoury Charbel , Nemer Nabil
Published: August 2021

Mediterranean stone pine nut is appreciated for its high economic and nutritional value. Starting in 2012, Pinus pinea nut production declined throughout the Mediterranean area. The dry cone syndrome associated with this decline and the introduction of Leptoglossus occidentalis occurred simultaneously. This study aims to evaluate potential reasons behind the decline in pine nut production in Lebanon, considering climatic factors and the invasion of L. occidentalis. Correlation analysis was used to examine a potential relationship between cone yield and the percentage of damaged seeds per cone. Climatic variables were also tested. Two time periods were considered for analysis: before and after 2012. Cone production and the percentage of damaged seeds were negatively correlated (r = −0.42). From 2012 to 2017, cone production declined by 50% and the percentage of damaged seeds increased on average from 3% in 2012 up to 60% in 2017. Correlations were detected between cone production and the temperature of the hottest three months of the year of harvesting, and between cone production and average temperatures during the year of cone initiation. A conjunction of factors that include L. occidentalis and climatic factors might have affected the pine nut production in Lebanon.
Machine Learning Modeling of Forest Road Construction Costs
Authors: Jaafari Abolfazl , Pazhouhan Iman , Bettinger Pete
Published: August 2021

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.
Between Pocket Forest Wilderness and Restored Rural Arcadia: Optimizing the Use of a Feral Woodland Enclave in Urban Environment
Authors: Bobiec Andrzej , Ćwik Agata , Gajdek Agata , Wójcik Tomasz , Ziaja Maria
Published: August 2021

An eight-hectare forest reserve in the centre of the metropolitan area in SE Poland was investigated for the effects of its management in relation to its purpose—protection of old Quercus robur L. trees. This local issue corresponds to a wider debate on the role of urban forests in contemporary societies. The study embraced (1) oak stand history—stand mapping and dendrochronology, historical maps’ analysis; (2) vegetation trends—undergrowth and ground layer analysis; and (3) visitors’ opinions on the reserve’s use—interview. The dendroecological analysis corroborates the landscape’s history emerging from the 1700s–1900s maps. The reserve is a feral park established in the early 1900s on the abandoned agricultural, partly wooded landscape. Under the current regime, shade-tolerant trees continue gaining advantage over older oaks, preventing their regeneration, whilst the herb layer, due to the reserve’s isolation remains poor and polluted by alien species. The non-intervention approach does not allow the reserve’s objective to be met. We recommend the restoration of features of semi-open silvopastoral landscape. This would correspond with both local eco-history and the dominating preference of the interviewed visitors. Such “bio-cultural refugia” should become key knowledge centres of the natural, cultural, and economic importance of the “working rural landscape”, fostering urban care for the countryside.
Stiffness Analysis to Predict the Spread Out of Fake Information
Authors: D’Ambrosio Raffaele , Giordano Giuseppe , Mottola Serena , Paternoster Beatrice
Published: August 2021

This work highlights how the stiffness index, which is often used as a measure of stiffness for differential problems, can be employed to model the spread of fake news. In particular, we show that the higher the stiffness index is, the more rapid the transit of fake news in a given population. The illustration of our idea is presented through the stiffness analysis of the classical SIR model, commonly used to model the spread of epidemics in a given population. Numerical experiments, performed on real data, support the effectiveness of the approach.
Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling
Authors: Alghamdi Taghreed , Elgazzar Khalid , Sharaf Taysseer
Published: August 2021

Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.
Approximate Analytical Models of Shock-Wave Structure at Steady Mach Reflection
Authors: Chernyshov Mikhail V. , Savelova Karina E. , Kapralova Anna S.
Published: August 2021

In this study, we obtain the comparative analysis of methods of quick approximate analytical prediction of Mach shock height in planar steady supersonic flows (for example, in supersonic jet flow and in narrowing channel between two wedges), that are developed since the 1980s and being actively modernized now. A new analytical model based on flow averaging downstream curved Mach shock is proposed, which seems more accurate than preceding models, comparing with numerical and experimental data.
Analytical Estimates of Critical Taylor Number for Motion between Rotating Coaxial Cylinders Based on Theory of Stochastic Equations and Equivalence of Measures
Authors: Dmitrenko Artur V.
Published: August 2021

The purpose of this article was to present the solution for the critical Taylor number in the case of the motion between rotating coaxial cylinders based on the theory of stochastic equations of continuum laws and the equivalence of measures between random and deterministic motions. Analytical solutions are currently of special value, as the solutions obtained by modern numerical methods require verification. At present, in the scientific literature, there are no mathematical relationships connecting the critical Taylor number with the parameters of the initial disturbances in the flow. The result of the solution shows a satisfactory correspondence of the obtained analytical dependence for the critical Taylor number to the experimental data.
Identification of the Specific Spoilage Organism in Farmed Sturgeon (Acipenser baerii) Fillets and Its Associated Quality and Flavour Change during Ice Storage
Authors: Zhang Zhichao , Wu Ruiyun , Gui Meng , Jiang Zhijie , Li Pinglan
Published: August 2021

Hybrid sturgeon, a popular commercial fish, plays important role in the aquaculture in China, while its spoilage during storage significantly limits the commercial value. In this study, the specific spoilage organisms (SSOs) from ice stored-sturgeon fillet were isolated and identified by analyzing their spoilage related on sensory change, microbial growth, and biochemical properties, including total volatile base nitrogen (TVBN), thiobarbituric acid reactive substances (TBARS), and proteolytic degradation. In addition, the effect of the SSOs on the change of volatile flavor compounds was evaluated by solid phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS). The results showed that the Pseudomonas fluorescens, Pseudomonas mandelii, and Shewanella putrefaciens were the main SSOs in the ice stored-sturgeon fillet, and significantly affect the odors by changing the volatile compounds in the sturgeon. Compared with the fresh sturgeon, the appreciable increase of polycyclic aromatic hydrocarbons and tetramethyl-pyrazine might be the spoilage indicators of the sturgeon contaminated by P. fluorescens; the appreciable increase of 1-octen-3-ol and (z)-2-penten-1-o might be the potential marker of the sturgeon contaminated by P. mandelii; and the appreciable increase of 1-(3,3-dimethylbicyclo [2.2.1] hept-2-yl)-ethanon and butylated hydroxytoluene were associated with S. putrefaciens. This study reveals the relationship between the SSOs and flavor changes in sturgeon fillets, which will contribute to the sturgeon preservation and shelf-life extension.
Chemical Changes of Hydroperoxy-, Epoxy-, Keto- and Hydroxy-Model Lipids under Simulated Gastric Conditions
Authors: Márquez-Ruiz Gloria , Holgado Francisca , Ruiz-Méndez María Victoria , Velasco Joaquín
Published: August 2021

Chemical changes occurring in dietary lipid oxidation compounds throughout the gastrointestinal tract are practically unknown. The first site for potential chemical modifications is the stomach due to the strong acidic conditions. In this study, model lipids representative of the most abundant groups of dietary oxidation compounds were subjected to in vitro gastric conditions. Thus, methyl linoleate hydroperoxides were used as representative of the major oxidation compounds formed in food storage at low and moderate temperatures. Methyl 9,10-epoxystearate, 12-oxostearate and 12-hydroxystearate were selected as model compounds bearing the oxygenated functional groups predominantly found in oxidation compounds formed at the high temperatures of frying. Analyses were performed using gas-liquid chromatography/flame ionization detection/mass spectrometry and high performance-liquid chromatography/ultraviolet detection. Losses of methyl 9,10-epoxystearate and linoleate hydroperoxides in the ranges 17.8–58.8% and 42.3–61.7% were found, respectively, whereas methyl 12-oxostearate and methyl 12-hydroxystearate remained unaltered. Although quantitative data of the compounds formed after digestion were not obtained, methyl 9,10-dihydroxystearate was detected after digestion of methyl 9,10-epoxystearate, and some major volatiles were detected after digestion of linoleate hydroperoxides. Overall, the results showed that significant modifications of dietary oxidized lipids occurred during gastric digestion and supported that the low pH of the gastric fluid played an important role.