bluuebunny/crossref_metadata_2025 huggingface dataset:
Physiological Offset Parameters of the Adult Shoulder Joint—A MRI Study of 800 Patients
Authors: Meier Marc-Pascal , Brandt Lars Erik , Saul Dominik , Roch Paul Jonathan , Klockner Friederike Sophie , Hosseini Ali Seif Amir , Lehmann Wolfgang , Hawellek Thelonius
Published: October 2022

Background: Humeral offset (HO) and glenoidal offset (GO) are important morphological parameters in diagnostics and therapy for shoulder pathologies. However, physiological reference values have not yet been sufficiently determined. The aim of the present study was to establish physiological reference values for shoulder offset parameters (SOPs). Methods: MRI images of the shoulder joints of 800 patients (mean age: 50.13 years [±16.01]) were analysed retrospectively. HO, GO, lateral glenoidal humeral offset (LGHO), humeral shaft axis offset (HAO) and cortical offset (CO) were measured. SOPs were examined for associations with age, gender, side and osteoarthritis. Results: The mean HO was 26.19 (±2.70), the mean GO was 61.79 (±5.67), the mean LGHO was 54.49 (±4.69), the mean HAO was 28.17 (±2.82) and the mean CO was 16.70 (±3.08). For all SOPs, significantly higher values were measured in male shoulders. There was a significantly (p < 0.001) higher mean value for HO, GO and LGHO in right shoulders. There was a significant correlation between age and LGHO, and HAO and CO, but not between age and HO or GO. Shoulders with osteoarthritis and non-osteoarthritis did not differ in the mean value of HO, GO, LGHO and HAO, except for CO (p = 0.049). Conclusion: Reference values for SOPs in the adult shoulder joint were determined for the first time. Significant gender-specific differences were found for all measured SOPs. In addition, it was seen that for some SOPs, the joint side and the patient’s age has to be taken into account in shoulder diagnostics and surgery.
A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks
Authors: Han Pu , Han Lin , Yuan Bo , Pan Jeng-Shyang , Shang Jiandong
Published: October 2022

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users’ historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user’s moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.
Bayesian Variable Selection and Estimation in Semiparametric Simplex Mixed-Effects Models with Longitudinal Proportional Data
Authors: Tang Anmin , Duan Xingde , Zhao Yuanying
Published: October 2022

In the development of simplex mixed-effects models, random effects in these mixed-effects models are generally distributed in normal distribution. The normality assumption may be violated in an analysis of skewed and multimodal longitudinal data. In this paper, we adopt the centered Dirichlet process mixture model (CDPMM) to specify the random effects in the simplex mixed-effects models. Combining the block Gibbs sampler and the Metropolis–Hastings algorithm, we extend a Bayesian Lasso (BLasso) to simultaneously estimate unknown parameters of interest and select important covariates with nonzero effects in semiparametric simplex mixed-effects models. Several simulation studies and a real example are employed to illustrate the proposed methodologies.
Entropy-Based Applications in Economics, Finance, and Management
Authors: Olbryś Joanna
Published: October 2022

The concept of entropy originated from physics (precisely, from thermodynamics), but it has been utilized in many research fields to characterize the complexity of a system and to investigate the information content of a probability distribution [...]
Four-Objective Optimization of an Irreversible Magnetohydrodynamic Cycle
Authors: Wu Qingkun , Chen Lingen , Ge Yanlin , Feng Huijun
Published: October 2022

Based on the existing model of an irreversible magnetohydrodynamic cycle, this paper uses finite time thermodynamic theory and multi-objective genetic algorithm (NSGA-II), introduces heat exchanger thermal conductance distribution and isentropic temperature ratio of working fluid as optimization variables, and takes power output, efficiency, ecological function, and power density as objective functions to carry out multi-objective optimization with different objective function combinations, and contrast optimization results with three decision-making approaches of LINMAP, TOPSIS, and Shannon Entropy. The results indicate that in the condition of constant gas velocity, deviation indexes are 0.1764 acquired by LINMAP and TOPSIS approaches when four-objective optimization is performed, which is less than that (0.1940) of the Shannon Entropy approach and those (0.3560, 0.7693, 0.2599, 0.1940) for four single-objective optimizations of maximum power output, efficiency, ecological function, and power density, respectively. In the condition of constant Mach number, deviation indexes are 0.1767 acquired by LINMAP and TOPSIS when four-objective optimization is performed, which is less than that (0.1950) of the Shannon Entropy approach and those (0.3600, 0.7630, 0.2637, 0.1949) for four single-objective optimizations, respectively. This indicates that the multi-objective optimization result is preferable to any single-objective optimization result.
Analysis of Longitudinal Binomial Data with Positive Association between the Number of Successes and the Number of Failures: An Application to Stock Instability Study
Authors: Zhang Xiaolei , Yan Guohua , Ma Renjun , Li Jiaxiu
Published: October 2022

Numerous methods have been developed for longitudinal binomial data in the literature. These traditional methods are reasonable for longitudinal binomial data with a negative association between the number of successes and the number of failures over time; however, a positive association may occur between the number of successes and the number of failures over time in some behaviour, economic, disease aggregation and toxicological studies as the numbers of trials are often random. In this paper, we propose a joint Poisson mixed modelling approach to longitudinal binomial data with a positive association between longitudinal counts of successes and longitudinal counts of failures. This approach can accommodate both a random and zero number of trials. It can also accommodate overdispersion and zero inflation in the number of successes and the number of failures. An optimal estimation method for our model has been developed using the orthodox best linear unbiased predictors. Our approach not only provides robust inference against misspecified random effects distributions, but also consolidates the subject-specific and population-averaged inferences. The usefulness of our approach is illustrated with an analysis of quarterly bivariate count data of stock daily limit-ups and limit-downs.
Enhanced Sample Self-Revised Network for Cross-Dataset Facial Expression Recognition
Authors: Xu Xiaolin , Zong Yuan , Lu Cheng , Jiang Xingxun
Published: October 2022

Recently, cross-dataset facial expression recognition (FER) has obtained wide attention from researchers. Thanks to the emergence of large-scale facial expression datasets, cross-dataset FER has made great progress. Nevertheless, facial images in large-scale datasets with low quality, subjective annotation, severe occlusion, and rare subject identity can lead to the existence of outlier samples in facial expression datasets. These outlier samples are usually far from the clustering center of the dataset in the feature space, thus resulting in considerable differences in feature distribution, which severely restricts the performance of most cross-dataset facial expression recognition methods. To eliminate the influence of outlier samples on cross-dataset FER, we propose the enhanced sample self-revised network (ESSRN) with a novel outlier-handling mechanism, whose aim is first to seek these outlier samples and then suppress them in dealing with cross-dataset FER. To evaluate the proposed ESSRN, we conduct extensive cross-dataset experiments across RAF-DB, JAFFE, CK+, and FER2013 datasets. Experimental results demonstrate that the proposed outlier-handling mechanism can reduce the negative impact of outlier samples on cross-dataset FER effectively and our ESSRN outperforms classic deep unsupervised domain adaptation (UDA) methods and the recent state-of-the-art cross-dataset FER results.
Investigating Students’ Learning Experiences in a Neural Engineering Integrated STEM High School Curriculum
Authors: Aldemir Tugce , Davidesco Ido , Kelly Susan Meabh , Glaser Noah , Kyle Aaron M. , Montrosse-Moorhead Bianca , Lane Katie
Published: October 2022

STEM integration has become a national and international priority, but our understanding of student learning experiences in integrated STEM courses, especially those that integrate life sciences and engineering design, is limited. Our team has designed a new high school curriculum unit that focuses on neural engineering, an emerging interdisciplinary field that brings together neuroscience, technology, and engineering. Through the implementation of the unit in a high school engineering design course, we asked how incorporating life sciences into an engineering course supported student learning and what challenges were experienced by the students and their teacher. To address these questions, we conducted an exploratory case study consisting of a student focus group, an interview with the teacher, and analysis of student journals. Our analysis suggests that students were highly engaged by the authentic and collaborative engineering design process, helping solidify their self-efficacy and interest in engineering design. We also identified some challenges, such as students’ lower interest in life sciences compared to engineering design and the teacher lacking a life sciences background. These preliminary findings suggest that neural engineering can provide an effective context to the integration of life sciences and engineering design but more scaffolding and teacher support is needed for full integration.
Higher Education Faculty Perceptions and Needs on Neuroeducation in Teaching and Learning
Authors: Fragkaki Maria , Mystakidis Stylianos , Dimitropoulos Konstantinos
Published: October 2022

Being a professor at the university is associated with the acquisition of sufficient domain knowledge and skills to teach. On the other hand, there is a constant need to update and strengthen teaching skills and contribute to an effective learning process. Neuroscience research provides evidence on facilitating factors to student engagement and lasting, durable learning. Toward the improvement of the learning quality in Higher Education, the development of an innovative didactic proposal based on neuroscience was undertaken. For that reason, a mixed research method was designed with the purpose of exploring instructors’ opinions and practices. Participants were sixty academics from five Greek universities. Findings include the recognition of the validity and importance of neuroeducation training. It was also revealed that academics’ theoretical declarations are not always aligned with teaching practice. Equally important is their need to deepen the comprehension of memory function for the enhancement of students’ knowledge retention. Eventual misconceptions about the brain might be present, while not all employed pedagogical methods lead to desired student outcomes such as creativity and critical thinking. In addition, it is considered useful to apply alternative, creative and authentic assessment methods based on neuroscience evidence to increase students’ interest and engagement.
Market-Based Instruments to Promote Energy Efficiency: Insights from the Italian Case
Authors: Di Foggia Giacomo , Beccarello Massimo , Borgarello Marco , Bazzocchi Francesca , Moscarelli Stefano
Published: October 2022

Market-based instruments such as white certificates offer the potential to increase efficiency gains, reduce primary energy consumption and make energy systems more resilient. The purpose of this paper is to provide insights into the white certificate scheme through a case study analysis. In light of increasing decarbonization and energy efficiency targets, it is important to rethink the role that policy instruments play in the energy transition, including the energy efficiency obligation schemes within which white certificate schemes fall. We focus on Italy’s white certificate scheme because it is among the longest-lived and has achieved notable results. The scheme is characterized by annually increasing energy savings targets, flexibility, the opportunity to include many interventions, and the role of energy services companies. We provide evidence based on an empirical survey and fill the research gap left by the latest updates to the scheme along with an appraisal of those updates, which covers prominent scheme innovations such as its stability mechanism. Our research can serve as a baseline for policymakers in designing white certificate schemes in countries with little related previous experience and provide useful information from countries where similar schemes have been implemented to fine-tune similar market-based mechanisms to improve energy efficiency.
Research on Parameter Design Method and Motion Characteristics of a Ball Cage Flexible Joint
Authors: Zhu Xiuxing , Xu Yingpeng , Zhou Weixia , Ye Guigen , Zhou Bo
Published: October 2022

The flexible joint is an important part in ultra-short-radius drilling tools, and its structural parameters and motion characteristics are key factors affecting the success of drilling. In this work, a new type of ball cage flexible joint, which is applied in 5″ and 5.5″ cased wells, was proposed based on the working principle of the ball cage universal joint. A structural parameter design method for the ball cage flexible joint was established according to the geometric coordination relation and material strength theory. Using this new method, the length, diameter, and window size of the ball cage flexible joint were analyzed. The multi-body motion process was further analyzed using a multi-body dynamics method, and then the motion characteristics, such as impact contact force, isokinetic characteristics, transfer efficiency, deflection torque and so on, were studied. Based on the above analyses, the structural parameters of the designed joint were optimized by means of the orthogonal test method. Results demonstrate that the experimental ball cage flexible joint has excellent isokinetic transmission characteristic, which can effectively suppress vibration and shock caused by changes in rotational speed. The transmission efficiency of the structure was 89.8%, while the power loss rate was 0.102%. According to the orthogonal test analysis, the optimal structure of the flexible joint has a ball seat diameter of 80 mm, a ball head diameter of 62 mm, and a ball key diameter of 16 mm. It is important to note that the ball key diameter was the most influential factor on the flexible joint internal contact force. The ball key contact force varied periodically, and there was a significant phase difference between the contact forces of different balls. On the other hand, with an increase in the flexible joint working angle, the deflection torque increased gradually, and the vibration amplitude of the torque increased. This work can provide reference for the parameter optimization design of the new flexible joint.
Advances in Solar PV Systems; A Comprehensive Review of PV Performance, Influencing Factors, and Mitigation Techniques
Authors: Aslam Adnan , Ahmed Naseer , Qureshi Safian Ahmed , Assadi Mohsen , Ahmed Naveed
Published: October 2022

PV power plants utilizing solar energy to generate electricity on a large scale has become a trend and a new option that has been adopted by many countries; however, in actuality, it is difficult to anticipate how much electricity PV plants will generate. This analysis of existing photovoltaic (PV) power plants provides guidelines for more precise designs and performance forecasting of other upcoming PV technologies. In the literature, some authors have put their efforts into reviewing studies on PV power systems; however, those reviews are too focused on specific aspects of the topic. This study will review, from a broader perspective, recent investigations on PV power systems in the literature that were published between 1990 and 2022. The present study is divided into three main parts. Firstly, a performance assessment review of PV power plants is presented by taking different performance parameters into consideration, which were developed by the “International Electrotechnical Commission (IEC 61724-1)”. These parameters include reference yield, final yield, performance ratio, capacity utilization factor, and system efficiency. Secondly, different identifying factors that were investigated in previous studies, and which affect PV performance, were considered. These factors include solar irradiance, PV technology type, ambient temperature, cell temperature, tilt angle, dust accumulation, and shading effect. Thirdly, different methods were adopted and suggested to counter the effects of these influencing factors to enhance the performance efficiency of the PV power system. A hybrid cooling and cleaning system can use active techniques to boost efficiency during high solar irradiances and ambient temperatures while depending on passive techniques for everyday operations. This comprehensive and critical review identifies the challenges and proposed solutions when using photovoltaic technologies and it will be helpful for researchers, designers, and investors dealing with PV power systems.
A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions
Authors: Okwuosa Chibuzo Nwabufo , Hur Jang-wook
Published: October 2022

In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the loop-hole of the Fourier Transform at minor-load conditions by harnessing the uniqueness of the Hilbert Transform (HT) to present a methodology that combines different feature engineering technologies to excite, extract, and select 10 discriminant information using a filter-based approach as the selection tool for fault classification. With the selected features, the SVC performed exceptionally well, with a significant diagnostic performance accuracy of 97.32%. Further testing with other well-known robust classifiers such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), gradient boost classifier (GBC), stochastic gradient descent (SGD), and global assessment metrics revealed that the SVC is reliable in terms of accuracy and computation speeds.
Improving the Dynamic Behavior of a Hybrid Electric Rotorcraft for Urban Air Mobility
Authors: Donateo Teresa , Spada Chiodo Ludovica , Ficarella Antonio , Lunaro Andrea
Published: October 2022

A rising number of aerospace manufacturers are working on the development of new solutions in the field of Urban Air Mobility with increasing attention addressing electric and hybrid electric propulsive systems. Hybrid electric propulsive systems potentially offer performance improvements during transient maneuvers, as well as sustaining the engine during flight phases characterized by high power demands. Among the challenges of hybridization in rotorcraft, there is the necessity to predict the dynamic behavior and its effect on the control of rotor shaft speed. In the present study, the dynamic behavior of a parallel hybrid electric propulsive system for a coaxial-rotor air taxi is analyzed in response to a typical sequence of pilot commands that encompasses the range of operations from hover to forward flight. The system is modeled with a dynamic approach and includes sub-models for the coaxial rotors, the turboshaft engine, the electric machine, and the battery. The results of the investigation show a better performance during transients of the hybrid system than a conventional turboshaft configuration, especially if the electric contribution to the power request is coordinated to account for the lag due to slower engine dynamic response.
The Use of Solar Energy by Households and Energy Cooperatives in Post-War Ukraine: Lessons Learned from Austria
Authors: Trypolska Galyna , Rosner Andrzej
Published: October 2022

The Russia-Ukraine war of 2022 showed the danger of reliance on autocratic regimes for energy supply. The use of renewables is a viable opportunity to substitute energy imports. Prosumption is one of the essential pillars of the Fourth Energy Package of the EU. After the war, Ukraine will require recovery packages, among other policies, aimed at increased energy prosumption. It will spur energy generation and foster employment, as well as being one of the means to avoid CO2 emissions. Following the example of Austria, the investment grant sizes for households and energy cooperatives in Ukraine were assessed. Potential job growth was estimated using the employment factor method and the size of CO2 emissions reduction. Should investment grants of EUR 50–200/kW for households and energy cooperatives be introduced, up to EUR 359.4 million would be required in 2022–2030. Households and energy cooperatives in Ukraine will still need to invest up to EUR 988 million until 2030. About 11 thousand full-time jobs may appear by 2030. Energy generation by households in 2020–2030 would enable the avoidance of 3.39–5.94 million tons of CO2 emissions.
Offline Handwritten Signature Verification Using Deep Neural Networks
Authors: Lopes José , Baptista Bernardo , Lavado Nuno , Mendes Mateus
Published: October 2022

Prior to the implementation of digitisation processes, the handwritten signature in an attendance sheet was the preferred way to prove the presence of each student in a classroom. The method is still preferred, for example, for short courses or places where other methods are not implemented. However, human verification of handwritten signatures is a tedious process. The present work describes two methods for classifying signatures in an attendance sheet as valid or not. One method based on Optical Mark Recognition is general but determines only the presence or absence of a signature. The other method uses a multiclass convolutional neural network inspired by the AlexNet architecture and, after training with a few pieces of genuine training data, shows over 85% of precision and recall recognizing the author of the signatures. The use of data augmentation and a larger number of genuine signatures ensures higher accuracy in validating the signatures.
Identifying Promising Technologies of Electric Vehicles from the Perspective of Market and Technical Attributes
Authors: Feng Lijie , Liu Kehui , Wang Jinfeng , Lin Kuo-Yi , Zhang Ke , Zhang Luyao
Published: October 2022

The vigorous development of electric vehicles (EVs) can promote the green and low-carbon development of society and the environment. However, the research and development of EVs technology in China started late, and there are some problems such as relatively backward technology. In order to promote the decarbonization process of transportation systems, there is an urgent need for appropriate methods to identify promising technologies in the EVs field to guide the efficient development of innovation activities. This study proposes a novel approach to integrate the perspective of market and technical attributes to identify promising EVs technologies. Firstly, text mining tools are applied to extract review and technical keywords from online reviews and patents, and technical topics are summarized. Secondly, sentiment analysis is conducted to calculate user satisfaction based on online reviews, and then market demand of technical topics is obtained. Thirdly, social network centrality analysis, DEA–Malmquist model, and CRITIC method are employed to obtain technical features of technical topics based on patents. Finally, a portfolio map is constructed to analyze technical topics and identify promising EVs technologies. As the main driving force for the development and transformation of the automotive industry, the efficient identification of promising technologies in this field can provide strategic decision support for the development of EVs. This study aims to provide objective data and scientific guidance for related enterprises to carry out technological innovation activities.
Queuing Theory-Based Design Methods for the Definition of Power Requirements in Manufacturing Systems
Authors: Ferretti Ivan , Camparada Matteo , Zavanella Lucio Enrico
Published: October 2022

The attention paid to energy consumption is growing steadily due to the costs associated with energy usage as well as the resulting environmental impacts. This work proposes an analytical method to assess the energy consumption and the power requirements of a productive system. By exploiting queuing theory, it is possible to achieve a probabilistic view of energy consumption. This method is useful to define the contractual power level and calculate the service level associated with it, so it is applicable as a decision-support tool during the design of productive systems when it is not possible to obtain field data (green-field design). Three different models characterised by an increasing degree of complexity were exploited. The three models share the feature of an infinite number of servers, while the increasing complexity is due to the introduction of batch arrivals and the variability of the size of the arrival lot. A connection is made between production variables and power used by machines to consider energy consumption. A numerical example shows the applicability of the method and highlights the different results obtained through the three models. In addition, analytical formulations are available for all three proposed models; thus, no simulation process is needed.
Research on the Soot Generation of Diesel Surrogate Mechanisms of Different Carbon Chain Length
Authors: Liang Xingyu , Zhu Zhijie , Cao Xinyi , Wang Kun , Wang Yuesen
Published: October 2022

This paper studies the generation process and emission characteristics of soot from Marine diesel engine. On the basis of Particulate size mimic (PSM) detailed soot model, the parameters related to soot generation obtained from the reaction mechanism calculation of 3 surrogate of different carbon chain length: n-heptane, n-tetradecane and n-tetradecane-toluene were compared and analyzed including precursor of soot, quality and density of soot, particle size distribution of empirical soot model. The results show that the soot nucleation stage of n-tetradecane-toluene mechanism was slightly more consistent than that of n-tetradecane mechanism with the experimental results, far more consistent than that of n-heptane mechanism. The intensity of surface growth and aggregation stage is greater, which is reflected in the soot precursor: acetylene and A4, and leads to that the soot produced by the mechanism of n-tetradecane and n-tetradecane-toluene is small in quality but large in quantity. The particle size distribution calculated by n-tetradecane and n-tetradecane-toluene mechanism is closer to the experimental data than that calculated by n-heptane mechanism. Then combustion process of n-tetradecane and n-tetradecane-toluene mechanism is more suitable for marine engine, which is reflected in the higher temperature and the smaller equivalent ratio in the cylinder.
Decomposition Methods for the Network Optimization Problem of Simultaneous Routing and Bandwidth Allocation Based on Lagrangian Relaxation
Authors: Ruksha Ihnat , Karbowski Andrzej
Published: October 2022

The main purpose of the work was examining various methods of decomposition of a network optimization problem of simultaneous routing and bandwidth allocation based on Lagrangian relaxation. The problem studied is an NP-hard mixed-integer nonlinear optimization problem. Multiple formulations of the optimization problem are proposed for the problem decomposition. The decomposition methods used several problem formulations and different choices of the dualized constraints. A simple gradient coordination algorithm, cutting-plane coordination algorithm, and their more sophisticated variants were used to solve dual problems. The performance of the proposed decomposition methods was compared to the commercial solver CPLEX and a heuristic algorithm.
Turning Trash to Treasure: Reusable Glucose Kit as a Cell Using ZnO Derived from Metal Organic Framework (MOF) Electrode for Redox Flow Battery
Authors: Lakshmanan Priya , Rengapillai Subadevi , Marimuthu Sivakumar , Vembu Suryanarayanan
Published: October 2022

Redox flow batteries (RFBs) are a promising candidate that are capable of meeting the energy storage applications to fulfill the needs of renewable resources. Herein, we prepare an electrochemical device that holds higher energy density. In this work, a reusable glucose kit used as a flow cell which in turn helps to minimize the cost and also balance the pump losses in electrochemical systems. For fabricating RFB, ZnO, from the metal organic framework (Zn-MOF/ZnO), uses an electrode material: ZnCl2 in aqueous KOH used as both anolyte and catholyte solution. Upon the new cell fabricating in this investigation, we demonstrated the voltage efficiency of 92% at 5 mA cm−2, which reduces the cost of the cell upon being implemented in the flow battery application.
Sorption Characteristic of Thermally Modified Wood at Varying Relative Humidity
Authors: Cai Chenyang , Zhou Fanding
Published: October 2022

Thermal modification (TM) is commonly used for improving the performance of wood under varying environmental conditions. The effect of TM on the hygroscopic properties of wood has been studied extensively; however, the sorption mechanism and the states of water of thermally modified wood (TMW) at varying relative humidity (RH) is limited. In this work, Douglas fir was modified at 180 °C, 200 °C and 220 °C for a duration of 2 h and the Hailwood–Horrobin model and NMR relaxometry were used after specimens were conditioned at eight different RHs. The results showed that TM inhibited monolayer and polylayer moisture sorption with increasing modification temperatures in all RHs. The lower slope of the sorption isotherms in TMW decreased compared with the control, indicating that the TM increased the hygroscopical stability of wood. The T2 distribution indicated that no free water was observed in the hygroscopic moisture range. The mobility of water molecules in the cell walls was decreased by TM intensity and increased by increasing RH.
Antioxidant Responses and Adaptation Mechanisms of Tilia tomentosa Moench, Fraxinus excelsior L. and Pinus nigra J. F. Arnold towards Urban Air Pollution
Authors: Petrova Slaveya , Velcheva Iliana , Nikolov Bogdan , Vasileva Tonka , Bivolarski Veselin
Published: October 2022

The aim of this study was to assess the antioxidant response towards urban air pollution of three widespread ornamental species—Tilia tomentosa, Fraxinus excelsior and Pinus nigra. Saplings were planted in four urban plots with different anthropogenic impacts, and periodic observations were performed on their development. Three types of biochemical markers, representing plant responses by three different mechanisms, were analyzed: photosynthetic pigments, free proline and guaiacol peroxidase activity. Our study confirmed that plant responses and adaptation to the environment are complex biological processes including physiological and biochemical changes. As a whole, these experiments revealed that the studied trees react by specific mechanisms towards urban air pollution, and antioxidant responses are significantly correlated with the enhancement of traffic (p < 0.05). Fraxinus excelsior was assessed as being very suitable for urban landscaping due to the significant tolerance to environmentally stressful conditions. Tilia tomentosa was also evaluated as a suitable ornamental species as it demonstrated good development in the urban environment. Pinus nigra was proven as more sensitive to the urban air pollution versus the other two studied trees. These findings could be very useful as a scientific basis for the landscaping practice in terms of the sustainable development and management of urban forestry.
Microenvironment Impact on Survival Rate, Growth and Stability Traits, in a Half-Sib Test of Pendula and Pyramidalis Varieties of Norway Spruce
Authors: Besliu Emanuel , Budeanu Marius , Apostol Ecaterina Nicoleta , Radu Raul Gheorghe
Published: October 2022

Norway spruce (Picea abies) is a tree species with low resistance to wind storms and breakage from snow. In this study, we analyzed the microenvironmental impact on the survival rate (Sr), growth, and stability traits of 25-year-old narrow (pendula)- and normal-crowned (pyramidalis) spruce varieties in the Măneciu half-sib trial. The replication factor had a highly significant influence (p < 0.001) on the growth and stability traits and a significant influence (p < 0.05) on the Sr, which confirms the microenvironmental impact. The average Sr was 85% and was 5% higher for the pendula variety (p < 0.05). For normal-crowned trees, a negative correlation (r = −0.26 ***) between the crown diameter and Sr was found, while for pendula trees, the correlations were 31% lower, suggesting the pendula variety’s high adaptation potential to a denser planting scheme. The edge effect affected the Sr of both varieties almost equally, with a 3% superiority for pendula. The Sr variations in relation to the slope position indicated that the narrow-crowned variety was less affected by the slope position, while the normal variety showed significant slope variation. The pendula variety of Norway spruce could be promoted in afforestation because of its high adaptation plasticity to a denser planting scheme.
Social Media as Support Channels in Communication with Society on Sustainable Forest Management
Authors: Słupińska Kamila , Wieruszewski Marek , Szczypa Piotr , Kożuch Anna , Adamowicz Krzysztof
Published: October 2022

This article deals with the issue of using social media to promote sustainable forest management. Major forest managers see significant value in combining various social media channels and implementing a communication campaign. With the help of social media, it is possible to raise public awareness of rational forest management. The purpose of this research was to conduct an in-depth theoretical and research analysis of the use of increasingly common communication channels such as social media. The research and analysis period was set from 1–26 July 2018. In order to evaluate the effectiveness of communication activities, the social media survey was repeated from 1–21 January 2019 and from 1–21 July 2020. The control analysis focused on the use of the main social media channels. The article assumed the hypothesis of conscious actions in the communication of forest management institutions in social media to communicate the rational use of forest resources. The hypothesis was positively verified on the basis of quantitative and qualitative studies of nonparticipatory observation for selected social media (Facebook, Instagram) and analysis of data contained in social media channels. The pilot study was conducted on 45 forestry entities from Poland. The results of the survey indicate the growing use of social media to communicate with the public about forest management. The dominant share of respondents consider social media as effective channels for forest education. Facebook is the most popular channel for conducting dialogue with the public. The results of the qualitative survey confirm the superiority of a graphic or mixed form of information presentation over the written form alone. This confirms the observed trends of increasing use of infographics, especially in communication with younger generations.
Experimenting with Routing Protocols in the Data Center: An ns-3 Simulation Approach
Authors: Alberro Leonardo , Velázquez Felipe , Azpiroz Sara , Grampin Eduardo , Richart Matías
Published: October 2022

Massive scale data centers (MSDC) have become a key component of current content-centric Internet architecture. With scales of up to hundreds of thousands servers, conveying traffic inside these infrastructures requires much greater connectivity resources than traditional broadband Internet transit networks. MSDCs use Fat-Tree type topologies, which ensure multipath connectivity and constant bisection bandwidth between servers. To properly use the potential advantages of these topologies, specific routing protocols are needed, with multipath support and low control messaging load. These infrastructures are enormously expensive, and therefore it is not possible to use them to experiment with new protocols; that is why scalable and realistic emulation/simulation environments are needed. Based on previous experiences, in this paper we present extensions to the ns-3 network simulator that allow executing the Free Range Routing (FRR) protocol suite, which support some of the specific MSDC routing protocols. Focused on the Border Gateway Protocol (BGP), we run a comprehensive set of control plane experiments over Fat-Tree topologies, achieving competitive scalability running on a single-host environment, which demonstrates that the modified ns-3 simulator can be effectively used for experimenting in the MSDC. Moreover, the validation was complemented with a theoretical analysis of BGP behavior over selected scenarios. The whole project is available to the community and fully reproducible.
A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
Authors: Alghamdi Taghreed , Mostafi Sifatul , Abdelkader Ghadeer , Elgazzar Khalid
Published: October 2022

The significant advancements in intelligent transportation systems (ITS) have contributed to the increased development in traffic modeling. These advancements include prediction and simulation models that are used to simulate and predict traffic behaviors on highway roads and urban networks. These models are capable of precise modeling of the current traffic status and accurate predictions of the future status based on varying traffic conditions. However, selecting the appropriate traffic model for a specific environmental setting is challenging and expensive due to the different requirements that need to be considered, such as accuracy, performance, and efficiency. In this research, we present a comprehensive literature review of the research related to traffic prediction and simulation models. We start by highlighting the challenges in the long-term and short-term prediction of traffic modeling. Then, we review the most common nonparametric prediction models. Lastly, we look into the existing literature on traffic simulation tools and traffic simulation algorithms. We summarize the available traffic models, define the required parameters, and discuss the limitations of each model. We hope that this survey serves as a useful resource for traffic management engineers, researchers, and practitioners in this domain.
A Unique Crustacean-Based Chitin Platform to Reduce Self-Aggregation of Polysaccharide Nanofibers
Authors: Londoño-Zuluaga Carolina , Jameel Hasan , Gonzalez Ronalds , Nellenbach Kimberly , Brown Ashley , Yang Guihua , Lucia Lucian
Published: October 2022

Every year, over 8 million tons of crustacean shells are discarded. However, there exists an opportunity for valorizing the chitin and calcium carbonate part of the composition of the shells. Our study revealed crustacean chitin reduces self-aggregation effects. It was shown that crustacean-based nanofibers alone or added to cellulose offer unprecedented reductions in viscosity even after drying to produce foams impossible for cellulose. Polysaccharide nanofibers suffer from increased viscosity from strong hydrogen bonding addressed by the incorporation of crustacean-based nanofibers. The ability of the nanocomposite to overcome self-aggregation and collapse was attributed to organized chitin nanofiber morphology in the crustacean matrix. As a result of enhanced surface area from reduced fiber aggregation, the chitin/crustacean-cellulose blend was tested for a biomedical application requiring a high surface area: coagulation. Preliminary experiments showed the crustacean matrices, especially those containing calcium carbonate, induced blood clotting when 35 s. A materials platform is proposed for bio-based nanofiber production overcoming intractable and difficult-to-address self-aggregation effects associated with polysaccharides.
Inverse Multiquadric Function to Price Financial Options under the Fractional Black–Scholes Model
Authors: Song Yanlai , Shateyi Stanford
Published: October 2022

The inverse multiquadric radial basis function (RBF), which is one of the most important functions in the theory of RBFs, is employed on an adaptive mesh of points for pricing a fractional Black–Scholes partial differential equation (PDE) based on the modified RL derivative. To solve this problem, discretization along space is carried out on a non-uniform grid in order to focus on the hot area, at which the initial condition of the pricing model, i.e., the payoff, has discontinuity. The L1 scheme having the convergence order 2−α is used along the time fractional variable. Then, our proposed numerical method is built by matrices of differentiations to be as efficient as possible. Computational pieces of evidence are brought forward to uphold the theoretical discussions and show how the presented method is efficient in contrast to the exiting solvers.
Groundwater Usage and Strategic Complements: Part II (Revealed Preferences)
Authors: Koch Caleb M. , Nax Heinrich H.
Published: October 2022

We investigate the strategic nature of farmers’ groundwater usage with a rich dataset from the American Midwest. We propose a new revealed preference test for the groundwater interaction as a dynamic game. We reject a view of groundwater usage decisions as strategic substitutes in favor of strategic complements as a better description.
Genomic Signatures of Freshwater Adaptation in Pacific Herring (Clupea pallasii)
Authors: Nedoluzhko Artem , Orlova Svetlana , Kurnosov Denis , Orlov Alexei , Galindo-Villegas Jorge , Rastorguev Sergey
Published: October 2022

Pacific herring (Clupea pallasii) is an essential target of commercial fishing in the North Pacific Ocean. Previous studies have suggested the existence of marine and lake ecological forms of this species within its range. The lake ecological form of herring has a shortened life cycle, spending the winter and spawning in brackish waters near the shoreline without long migrations for feeding; it also has a relatively smaller body size than the marine form. Genetic-based studies have shown that brackish water Pacific herring not only can be distinguished as a separate lake ecological form but possibly has its genetic legacy. Here, as part of an ongoing study, using ddRAD-sequencing data for marine and lake ecological forms from a total of 54 individuals and methods of comparative bioinformatics, we describe genomic signatures of freshwater adaptivity in Pacific herring. In total, 253 genes containing discriminating SNPs were found, and part of those genes was organized into genome clusters, also known as “genomic islands of divergence”. Moreover, the Tajima’s D test showed that these loci are under directional selection in the lake populations of the Pacific herring. Yet, most discriminating loci between the lake and marine ecological forms of Pacific herring do not intersect (by gene name) with those in other known marine fish species with known freshwater/brackish populations. However, some are associated with the same physiological trait—osmoregulation.
The Genetic Factors of the Airway Epithelium Associated with the Pathology of Asthma
Authors: Ranjbar Maral , Whetstone Christiane E. , Omer Hafsa , Power Lucy , Cusack Ruth P. , Gauvreau Gail M.
Published: October 2022

Asthma is a chronic disease of the airways characterized by inflammation, tightened muscles, and thickened airway walls leading to symptoms such as shortness of breath, chest tightness, and cough in patients. The increased risk of asthma in children of asthmatics parents supports the existence of genetic factors involved in the pathogenesis of this disease. Genome-wide association studies have discovered several single nucleotide polymorphisms associated with asthma. These polymorphisms occur within several genes and can contribute to different asthma phenotypes, affect disease severity, and clinical response to different therapies. The complexity in the etiology of asthma also results from interactions between environmental and genetic factors. Environmental exposures have been shown to increase the prevalence of asthma in individuals who are genetically susceptible. This review summarizes what is currently known about the genetics of asthma in relation to risk, response to common treatments, and gene-environmental interactions.