bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Burn Severity on Post-Fire Spectral Recovery of Three Fires in the Southern Rocky Mountains
Authors: Guz Jaclyn , Sangermano Florencia , Kulakowski Dominik
Published: March 2022

Increased wildfire activity and altered post-fire climate in the Southern Rocky Mountains has the potential to influence forest resilience. The Southern Rocky Mountains are a leading edge of climate change and have experienced record-breaking fires in recent years. The change in post-fire regeneration and forest resilience could potentially include future ecological trajectories. In this paper, we examined patterns of post-fire spectral recovery using Landsat time series. Additionally, we utilized random forest models to analyze the impact of climate and burn severity on three fire events in the Southern Rocky Mountains. Fifteen years following the fires, none of the burned stands fully recovered to their pre-fire spectral states. The results suggested that burn severity significantly impacted post-fire spectral recovery, but that influence may decrease as time since fire increases. The biggest difference in forest recovery was among fire events, indicating that post-fire climate may be influential in post-fire recovery. The mean and minimum growing-season temperatures were more significant to post-fire recovery than the variability in precipitation, which is consistent with field-based analysis. The present study indicated that, as warming continues, there may be changes in forest density where forests are not regenerating to their pre-fire spectral states. Additionally, this study emphasizes how high-elevation forests continue to regenerate after fires, but that regeneration is markedly affected by post-fire climate.
Fine Resolution Imagery and LIDAR-Derived Canopy Heights Accurately Classify Land Cover with a Focus on Shrub/Sapling Cover in a Mountainous Landscape
Authors: Bulluck Lesley , Lin Baron , Schold Elizabeth
Published: March 2022

Publicly available land cover maps do not accurately represent shrubs and saplings, an uncommon but ecologically relevant cover type represented by woody vegetation <4 m tall. This omission likely occurs because (1) the resolution is too coarse, (2) poor training data are available, and/or (3) shrub/saplings are difficult to discriminate from spectrally similar classes. We present a framework for classifying land cover, including shrub/saplings, by combining open-source fine-resolution (1 m) spectral and structural data across a large (>6000 km2) mountainous region. We hypothesized that the combination of spectral (imagery) and structural (LIDAR) data would allow for discrimination of shrub/sapling cover from other cover types. Specifically, we created training data using segmented four-band imagery from the National Agricultural Imagery Program (NAIP). In addition to spectral information from imagery, we used topographic information (elevation, slope, and aspect) and a LIDAR-derived canopy height model to classify land cover within a pixel-based random forests framework. To assess model accuracy, we used image interpretation and an independent sample of validation points. Due to the fine resolution of predictor rasters across such a large geographic region, we classified five subregions (counties) separately. We also compared the landscape metrics calculated for our custom classification at fine (1 m) and coarse resolution (resampled to 30 m) to metrics calculated with National Land Cover Data (NLCD). We achieved an overall accuracy of 89% and >80% accuracy for each land cover class. The LIDAR-derived canopy height model was consistently ranked as the most important predictor of vegetative land cover classes. Compared with our custom classification, NLCD underrepresented pasture/grassland by up to 10% and overrepresented forest up to 30%. There was no correlation between percent shrub/sapling cover in our custom classification and NLCD, suggesting that NLCD is not reliable for applications concerned with this ecologically relevant cover type.
IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization
Authors: Chen Zhipeng , Li Qingquan , Li Jiayuan , Zhang Dejin , Yu Jianwei , Yin Yu , Lv Shiwang , Liang Anbang
Published: March 2022

Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit (IMU) to assist registration and propose an MLS point cloud registration method based on an inertial trajectory error model. First, we propose an error model of inertial trajectory over a short time period to construct the constraints between trajectory points at different times. On this basis, a relationship between the point cloud registration error and the inertial trajectory error is established, then trajectory error parameters are estimated by minimizing the point cloud registration error using the least squares optimization. Finally, a reliable and concise inertial-assisted MLS registration algorithm is realized. We carried out experiments in three different scenarios: indoor, outdoor and integrated indoor–outdoor. We evaluated the overall performance, accuracy and efficiency of the proposed method. Compared with the ICP method, the accuracy and speed of the proposed method were improved by 2 and 2.8 times, respectively, which verified the effectiveness and reliability of the proposed method. Furthermore, experimental results show the significance of our method in constructing a reliable and scalable mobile 3D mapping system suitable for complex scenes.
Identification of Crop Type Based on C-AENN Using Time Series Sentinel-1A SAR Data
Authors: Guo Zhengwei , Qi Wenwen , Huang Yabo , Zhao Jianhui , Yang Huijin , Koo Voon-Chet , Li Ning
Published: March 2022

Crop type identification is the initial stage and an important part of the agricultural monitoring system. It is well known that synthetic aperture radar (SAR) Sentinel-1A imagery provides a reliable data source for crop type identification. However, a single-temporal SAR image does not contain enough features, and the unique physical characteristics of radar images are relatively lacking, which limits its potential in crop mapping. In addition, current methods may not be applicable for time-series SAR data. To address the above issues, a new crop type identification method was proposed. Specifically, a farmland mask was firstly generated by the object Markov random field (OMRF) model to remove the interference of non-farmland factors. Then, the features of the standard backscatter coefficient, Sigma-naught (σ0), and the normalized backscatter coefficient by the incident angle, Gamma-naught (γ0), were extracted for each type of crop, and the optimal feature combination was found from time-series SAR images by means of Jeffries-Matusita (J-M) distance analysis. Finally, to make efficient utilization of optimal multi-temporal feature combination, a new network, the convolutional-autoencoder neural network (C-AENN), was developed for the crop type identification task. In order to prove the effectiveness of the method, several classical machine learning methods such as support vector machine (SVM), random forest (RF), etc., and deep learning methods such as one dimensional convolutional neural network (1D-CNN) and stacked auto-encoder (SAE), etc., were used for comparison. In terms of quantitative assessment, the proposed method achieved the highest accuracy, with a macro-F1 score of 0.9825, an overall accuracy (OA) score of 0.9794, and a Kappa coefficient (Kappa) score of 0.9705. In terms of qualitative assessment, four typical regions were chosen for intuitive comparison with the sample maps, and the identification result covering the study area was compared with a contemporaneous optical image, which indicated the high accuracy of the proposed method. In short, this study enables the effective identification of crop types, which demonstrates the importance of multi-temporal radar images in feature combination and the necessity of deep learning networks to extract complex features.
Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
Authors: Geng Tao , Jiang Rui , Lv Yifei , Xie Xin
Published: March 2022

The characteristics and performance of satellite clocks are important to the positioning, navigation, and timing (PNT) services of Global Navigation Satellite System (GNSS) users. Although China’s BeiDou-3 Navigation Satellite System (BDS-3) has been fully operational for more than one year, there is still a lack of comprehensive research on the onboard clocks of the entire BDS-3 constellation. In this study, the precise clock products of GeoForschungsZentrum (GFZ) from day-of-year (DOY) 1, 2021 to DOY 300, 2021 were used to analyze the characteristics and performance of BDS-3 onboard clocks from the following aspects: clock bias, frequency, drift rate, fitting residuals, periodicity, and frequency stability. Compared with BDS-2, the clock quality of BDS-3 satellites has been greatly improved, but there are still jumps in the clock offsets and frequency series of BDS-3 clocks. The drift rate of BDS-3 clocks varies within the range between −2×10−18 and 2×10−18 s/s2. The daily model fitting residuals of passive hydrogen masers (PHM) on BDS-3 medium Earth orbit (MEO), inclined geosynchronous orbit (IGSO), and geostationary (GEO) satellites are 0.15, 0.28, and 0.46 ns, respectively. The overlapping Allan deviation (OADEV) of BDS-3 MEO clocks is 4.0 × 10−14 s/s at a time interval of 1000 s. The PHMs on BDS-3 MEO satellites exhibit fewer periodic signals than those of Rb clocks. In addition, the precise clock offsets of the BDS-3 PHMs carried on the MEO, IGSO, and GEO satellites show different periodicities, which are similar to those of the corresponding types of BDS-2 satellites.
An Efficient High-Resolution Global–Local Network to Detect Lunar Features for Space Energy Discovery
Authors: Jia Yutong , Liu Lei , Peng Siqing , Feng Mingyang , Wan Gang
Published: March 2022

Lunar craters and rilles are significant topographic features on the lunar surface that will play an essential role in future research on space energy resources and geological evolution. However, previous studies have shown low efficiency in detecting lunar impact craters and poor accuracy in detecting lunar rilles. There is no complete automated identification method for lunar features to explore space energy resources further. In this paper, we propose a new specific deep-learning method called high-resolution global–local networks (HR-GLNet) to explore craters and rilles and to discover space energy simultaneously. Based on the GLNet network, the ResNet structure in the global branch is replaced by HRNet, and the residual network and FPN are the local branches. Principal loss function and auxiliary loss function are used to aggregate global and local branches. In experiments, the model, combined with transfer learning methods, can accurately detect lunar craters, Mars craters, and lunar rilles. Compared with other networks, such as UNet, ERU-Net, HRNet, and GLNet, GL-HRNet has a higher accuracy (88.7 ± 8.9) and recall rate (80.1 ± 2.7) in lunar impact crater detection. In addition, the mean absolute error (MAE) of the GL-HRNet on global and local branches is 0.0612 and 0.0429, which are better than the GLNet in terms of segmentation accuracy and MAE. Finally, by analyzing the density distribution of lunar impact craters with a diameter of less than 5 km, it was found that: (i) small impact craters in a local area of the lunar north pole and highland (5°–85°E, 25°–50°S) show apparent high density, and (ii) the density of impact craters in the Orientale Basin is not significantly different from that in the surrounding areas, which is the direction for future geological research.
Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions
Authors: Mohan Pilla Vaishno , Dixit Shriniket , Gyaneshwar Amogh , Chadha Utkarsh , Srinivasan Kathiravan , Seo Jung Taek
Published: March 2022

With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.
Laboratory Investigation of Tomography-Controlled Continuous Steel Casting
Authors: Glavinić Ivan , Muttakin Imamul , Abouelazayem Shereen , Blishchik Artem , Stefani Frank , Eckert Sven , Soleimani Manuchehr , Saidani Iheb , Hlava Jaroslav , Kenjereš Saša , Wondrak Thomas
Published: March 2022

More than 96% of steel in the world is produced via the method of continuous casting. The flow condition in the mould, where the initial solidification occurs, has a significant impact on the quality of steel products. It is important to have timely, and perhaps automated, control of the flow during casting. This work presents a new concept of using contactless inductive flow tomography (CIFT) as a sensor for a novel controller, which alters the strength of an electromagnetic brake (EMBr) of ruler type based on the reconstructed flow structure in the mould. The method was developed for the small-scale Liquid Metal Model for Continuous Casting (mini-LIMMCAST) facility available at the Helmholtz-Zentrum Dresden-Rossendorf. As an example of an undesired flow condition, clogging of the submerged entry nozzle (SEN) was modelled by partly closing one of the side ports of the SEN; in combination with an active EMBr, the jet penetrates deeper into the mould than when the EMBr is switched off. Corresponding flow patterns are detected by extracting the impingement position of the jets at the narrow faces of the mould from the CIFT reconstruction. The controller is designed to detect to undesired flow condition and switch off the EMBr. The temporal resolution of CIFT is 0.5 s.
Fault Handling in Industry 4.0: Definition, Process and Applications
Authors: Webert Heiko , Döß Tamara , Kaupp Lukas , Simons Stephan
Published: March 2022

The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.
Analytical and Experimental Investigation of a Curved Piezoelectric Energy Harvester
Authors: Pourashraf Talieh , Bonello Philip , Truong Jason
Published: March 2022

Piezoelectric energy harvesters have traditionally taken the form of base excited cantilevers. However, there is a growing body of research into the use of curved piezoelectric transducers for energy harvesting. The novel contribution of this paper is an analytical model of a piezoelectric energy harvesting curved beam based on the dynamic stiffness method (DSM) and its application to predict the measured output of a novel design of energy harvester that uses commercial curved transducers (THUNDER TH-7R). The DSM predictions are also verified against results from commercial finite element (FE) software. The validated results illustrate the resonance shift and shunt damping arising from the electrical effect. The magnitude, phase, Nyquist plots, and resonance frequency shift estimates from DSM and FE are all in satisfactory agreement. However, DSM has the advantage of having significantly fewer elements and is sufficiently accurate for commercial curved transducers used in applications where beam-like vibration is the predominant mode of vibration.
Cluster Analysis and Discriminant Analysis for Determining Post-Earthquake Road Recovery Patterns
Authors: Wu Jieling , Saito Mitsugu , Endo Noriaki
Published: March 2022

The transport network in eastern Japan was severely damaged by the 2011 Tohoku earthquake. To understand the road recovery conditions after a large earthquake, a large amount of time is needed to collect information on the extent of the damage and road usage. In our previous study, we applied cluster analysis to analyze the data on driving vehicles in Fukushima prefecture to classify the road recovery conditions among municipalities within the first six months after the earthquake. However, the results of the cluster analysis and relevant factors affecting road recovery from that study were not validated. In this study, we proposed a framework for determining post-earthquake road recovery patterns and validated the cluster analysis results by using discriminant analysis and observing them on a map to identify their common characteristics. In addition, our analysis of objective data reflecting regional characteristics showed that the road recovery conditions were similar according to the topography and the importance of roads.
Automatic Extrinsic Calibration of 3D LIDAR and Multi-Cameras Based on Graph Optimization
Authors: Ou Jinshun , Huang Panling , Zhou Jun , Zhao Yifan , Lin Lebin
Published: March 2022

In recent years, multi-sensor fusion technology has made enormous progress in 3D reconstruction, surveying and mapping, autonomous driving, and other related fields, and extrinsic calibration is a necessary condition for multi-sensor fusion applications. This paper proposes a 3D LIDAR-to-camera automatic calibration framework based on graph optimization. The system can automatically identify the position of the pattern and build a set of virtual feature point clouds, and can simultaneously complete the calibration of the LIDAR and multiple cameras. To test this framework, a multi-sensor system is formed using a mobile robot equipped with LIDAR, monocular and binocular cameras, and the pairwise calibration of LIDAR with two cameras is evaluated quantitatively and qualitatively. The results show that this method can produce more accurate calibration results than the state-of-the-art method. The average error on the camera normalization plane is 0.161 mm, which outperforms existing calibration methods. Due to the introduction of graph optimization, the original point cloud is also optimized while optimizing the external parameters between the sensors, which can effectively correct the errors caused during data collection, so it is also robust to bad data.
Towards a Better Understanding of Motivation and Constraints for Domestic Geotourists: The Case of the Middle and Lower Danube Region in Serbia
Authors: Tomić Nemanja , Marjanović Miloš
Published: March 2022

The main goal of this study is to explore the motivation and constraints among domestic geotourists in Serbia. For this purpose, a survey was conducted on 303 respondents from Serbia who have visited geosites within the Middle and Lower Danube region in Serbia during the past three years. The initial results were obtained through an exploratory factor analysis which revealed five motivating factors (Visiting attractions, Research and prestige, Rest and relaxation, Acquiring new knowledge and Friends) and four constraints factors (Structural, Lack of time, Lack of information and recommendation and Inter/Intrapersonal). Ranking the factors further revealed that “Acquiring new knowledge” motivates geotourists the most while Structural constraints are the main demotivator. Further analysis by using ANOVA and T-test for independent samples revealed significant differences between gender and educational groups, and the results show that women are more motivated than men by the factor related to acquiring new knowledge, while those with master’s or doctoral degrees consider the factor related to friends less important than those with lower degrees.
The Analysis of Trade Liberalization and Nutrition Intake for Improving Food Security across Districts in Indonesia
Authors: Montolalu Maya H. , Ekananda Mahjus , Dartanto Teguh , Widyawati Diah , Panennungi Maddaremmeng
Published: March 2022

The debate on the effect of trade liberalization on food security poses solid arguments, both in favor as well as against the issue. This study aims to analyze the linkages between trade liberalization (measured using food import tariff exposure) and food security (measured using nutrition intake) in the case of Indonesia. The national food import tariff is decomposed into district-level import tariff exposure and is analyzed based on sectoral tariffs such as agriculture tariffs and food manufacture import tariffs. The analysis employs panel data of 496 Indonesian districts and postulates an association between trade and food security by using fixed-effect regression. By analyzing the effects of tariff exposure towards food consumption in all districts and grouping the districts into 5 (five) islands, we can contribute to the literature on trade liberalization and food security. First, it is found that import tariff exposure is negatively impacting nutrition intake and each sector has a different effect on each nutrition intake. Furthermore, the impact of manufacturing tariffs on calorie and protein intake is slightly higher than that of agriculture tariffs. Second, it is shown that both sectoral import tariffs’ effects vary across islands in Indonesia. Furthermore, the research is expected to contribute to and become a reference for the government in regulating tariffs and other trade liberalization schemes to support households to be food secure.
Political Dilemmas in the Making of a Sustainable City-Region: The Case of Istanbul
Authors: Enlil Zeynep , Dinçer İclal
Published: March 2022

This article aims to explore the political dilemmas of sustainable metropolitan development marked by intense tensions between ecology and economy within the context of neoliberal urban policies over the Case of Istanbul, Turkey. It investigates the re-scaling and centralization of the state in directing the investment capital and focuses on the ways in which it reregulates and loosens the institutions to create exceptionalities in order to realize mega projects. It examines Canal Istanbul and the “New City” or the Yenişehir Project, the so-called “crazy project” imposed upon the city by the central government, which presents a crucial case demonstrating the processes of creating exceptionalities and the erosion of public norms. Empirically, drawing from the Turkish experience through an in-depth analysis of policy documents, plans and reports prepared by a variety of agents, the article demonstrates and discusses different modalities of creating exceptions to capitalize on the lucrative real estate markets through mega projects in an increasingly authoritarian neoliberal context, its ramifications on the existing norms and the oppositions it raised. The article concludes with a discussion on how the new political climate that moved away from subsidiarity, transparency and democratic participation, and became increasingly centralized, created an impasse for planning and that neither the ecology nor the economy could be protected and enhanced. Although economic development discourse is used to legitimize these mega projects, it is obvious that they lead to an ecocide.
Can China’s Policy of Carbon Emissions Trading Optimize Manufacturing Structure? Evidence from Guangdong Based on a Synthetic Control Approach
Authors: Lei Yutao , Zhang Xuan , Peng Wenxiang
Published: March 2022

On 16 July 2021, the national carbon emissions trading market opened, and the national carbon market officially started online trading. However, it is still unclear whether the carbon emissions trading policy can effectively optimize the manufacturing structure. We studied the experiment of the carbon emissions trading policy that has been ongoing in Guangdong, China, since 2013 to assess the impact of this policy on the manufacturing structure in Guangdong Province compared to other provinces in mainland China that have not implemented a carbon trading pilot policy. The methodology uses a synthetic control method. Using this method, a “synthetic Guangdong” was constructed using data from 23 provinces (municipalities and autonomous regions) in mainland China that did not implement carbon trading policies from 2009 to 2019. The synthetic province had similar observed characteristics to Guangdong before the carbon emissions trading experiment in 2013. Therefore, manufacturing structure differences between Guangdong and the synthetic province after 2013 could be attributed only to the carbon emissions trading policy. The conclusion indicates that in the short term, the carbon emissions trading policy implemented in 2013 can significantly promote manufacturing upgrading and manufacturing greening in Guangdong Province. This policy can optimize the manufacturing structure of Guangdong Province through improving the technological innovation of enterprises and increasing foreign direct investment. Therefore, in regions whose manufacturing structure is similar to Guangdong Province, implementing a carbon emissions trading policy can promote manufacturing upgrading and manufacturing greening.
Meta-Analysis of Tourism Sustainability Research: 2019–2021
Authors: Roberts Chris , Reynolds Joel , Dolasinski Mary Jo
Published: March 2022

Sustainability in tourism, hospitality, and leisure is a long-standing topic of growing interest. Research in the field of tourism has accelerated over the past decade. The goal of this meta-analysis is to categorize recent research in tourism sustainability to identify patterns and trends, which could help us understand where the field is currently conducting research and where more work may be needed. Over 800 relevant articles published in major tourism journals during a three-year period (2019–2021) were included in the analysis; however, despite the increase in volume of published tourism sustainability research, this quantity was less than 4% of all publications in the same journals. Four over-arching pillars of sustainability research themes emerged (social, economic, ecological, and institutional) with numerous sub-categories within each. The majority of tourism sustainability research occurred in the social and economic arenas. Much more research is needed in the other three categories. The greater part of the recent advances in tourism sustainability appears to be driven by small- and medium-sized tourism firms. The impact of the COVID-19 pandemic on tourism sustainability is only beginning to be understood. Tourism business leaders have an opportunity to strengthen and integrate their use of sustainability as they work to rebuild consumer trust in travel services.
Analysis of Influencing Factors of Embodied Carbon in China’s Export Trade in the Background of “Carbon Peak” and “Carbon Neutrality”
Authors: Yang Weixin , Gao Hao , Yang Yunpeng
Published: March 2022

Since China’s reform and opening up, especially after its accession to the World Trade Organization, its foreign trade has achieved fruitful results. However, at the same time, the extensive foreign trade growth model with high energy consumption and high pollution has also caused a rapid increase in carbon emissions. There is a large amount of embodied carbon emissions in the export trade. In order to achieve the strategic goals of “Carbon Peak” and “Carbon Neutrality’, and at the same time build a green trading system to achieve coordinated development of trade and the environment, it is of great significance to study embodied carbon emissions and how to decouple them with China’s foreign trade. This paper uses the Logarithmic Mean Divisia Index method to decompose the influencing factors of the embodied carbon in China’s export trade in order to study the impact of three factors: export scale, export structure, and carbon emission intensity. The results show that the change in export scale is the most important factor affecting the embodied carbon of China’s export trade, and the expansion of export scale has caused the growth of trade embodied carbon. Carbon emission intensity is the second influential factor, and the decline in carbon intensity would slow down the growth of trade embodied carbon, while changes in the export structure have the smallest impact on trade embodied carbon. The high carbonization of the overall export structure will cause growth of trade embodied carbon, but the tertiary industry has seen some improvement in the export structure, which could facilitate the decline of trade embodied carbon.
Optimal Placement and Sizing of PV Sources in Distribution Grids Using a Modified Gradient-Based Metaheuristic Optimizer
Authors: Montoya Oscar Danilo , Grisales-Noreña Luis Fernando , Giral-Ramírez Diego Armando
Published: March 2022

The problem of the optimal placement and sizing of renewable generation sources based on photovoltaic (PV) technology in electrical distribution grids operated in medium-voltage levels was studied in this research. This optimization problem is from the mixed-integer nonlinear programming (MINLP) model family. Solving this model was achieved by implementing a master–slave optimization approach, where the master–slave corresponded to the application of the modified gradient-based metaheuristic optimizer (MGbMO) and the slave stage corresponded to the application of the successive approximation power flow method. In the master stage, the problem of the optimal placement and sizing of the PV sources was solved using a discrete–continuous codification, while the slave stage was used to calculate the objective function value regarding the energy purchasing costs in terminals of the substation, as well as to verify that the voltage profiles and the power generations were within their allowed bounds. The numerical results of the proposed MGbMO in the IEEE 34-bus system demonstrated its efficiency when compared with different metaheuristic optimizers such as the Chu and Beasley genetic algorithm, the Newton metaheuristic algorithm, the original gradient-based metaheuristic optimizer, and the exact solution of the MINLP model using the general algebraic modeling system. In addition, the possibility of including meshed distribution topologies was tested with excellent numerical results.
The Mechanism of Digital Environment Influencing Organizational Performance: An Empirical Analysis Based on Construction Data
Authors: Shen Zhifeng , Liang Xingnan , Lv Jinze , Liu Chunlu , Li Junjie
Published: March 2022

The new system formed by digital technology and digital system is different from the traditional environment, and this environment has a sharp impact, either by transformation or replacement, of the traditional environment. However, at present, organizational strategy formulation, structural design, resource allocation, and culture formation are based on the traditional environment, which leads to a declining trend in organizational performance. Therefore, to identify and adapt to the characteristics of the digital environment, the improvement and design of organizational characteristics that highly fit with this environment has become an urgent task for all kinds of enterprises to maintain and improve organizational performance. Based on the analysis of the characteristics of digital environment, organizational change, digital organizational culture, organizational performance, and the data of the construction industry, this study constructs a mediating effect structure model to explain the path and mechanism of organizational performance improvement, and puts forward corresponding countermeasures and suggestions according to the empirical results. The research puts forward the analysis model of “Digital Environment-Organizational Behavior-Organizational Performance”. The results reveal how to improve organizational performance in the digital environment and enrich the research of organizational performance and the practice of organizational performance improvement.
Accessibility Challenges in OER and MOOC: MLR Analysis Considering the Pandemic Years
Authors: Ingavélez-Guerra Paola , Robles-Bykbaev Vladimir , Teixeira António , Otón-Tortosa Salvador , Hilera José Ramón
Published: March 2022

The review of state of the art on creating and managing learning resources and accessible Open Educational Resources (OER) and Massive Open Online Courses (MOOC) is a topic that cannot only consider formal literature. The evidence and lack of a measurement consensus require the inclusion of contextual information, corroborating scientific results with practical experiences. For this reason, this article presents a review of accessibility models, OER and MOOC, considering the gray literature to capture experiences and trying to establish a shared understanding of the terminology commonly used in research on virtual accessibility and its impact on higher education. The bibliographic review relies on analyzing articles and scientific publications related to the topic following the Multivocal Literature Review (MLR) format. The results of this review establish that it is possible to apply accessibility review methodologies with transversal actions in the creation and management of learning resources and MOOCs. The research is related to one of the seventeen sustainable development goals defined by the United Nations to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.
Banking Sector Profitability: Does Household Income Matter?
Authors: Miroshnichenko Olga , Iakovleva Elena , Voronova Natalia
Published: March 2022

Household incomes, their level and dynamics are one of the factors that ensure the achievement of the Sustainable Development Goals. At the same time, stable development of the banking sector, which is impossible without steady earnings, determines economic growth, which also positively affects reaching the Sustainable Development Goals. The paper examines the impact of household income on the return on assets of the banking sector in Russia using annual time series from 2003 to 2019. The study was conducted using formalized economic and mathematical methods of analysis by linear regression with least squares tests on the significance of the model, with tests for redundancy of insignificant variables (Wald test), Ramsey test on the functionality of the model, White and Breusch Pagan test for heteroscedasticity (heterogeneity of observations) and multicollinearity by method of inflation factors, graphic method. The Multiple Linear Regression (MLR) model was used. The results show that (1) an increase in the growth rate of household income and deposits in the non-financial sector has a positive effect on the return on assets of the banking sector; (2) an increase in the growth rate of the price of Brent crude oil and non-performing loans negatively affect the dependent variable; and (3) the regions that have the greatest (positive and negative) impact on profitability of whole bank sector in Russia were identified. Increasing household incomes and eliminating inequality in the incomes of the population of different regions will have a positive impact not only on social well-being but will also provide the banking sector with the opportunity for profitable operation and create conditions for sustainable growth. Our conclusions are useful for the regulator and individual banks and can be taken into account when developing and implementing policies aimed at sustainable development.
Ecosystem Services Multifunctionality: An Analytical Framework to Support Sustainable Spatial Planning in Italy
Authors: Pilogallo Angela , Scorza Francesco
Published: March 2022

A growing demand at several levels of territorial government concerns the need for tools to support policy-making oriented towards sustainable planning. That of Ecosystem Services (ES) represents a well-structured and robust methodological framework for developing tools to assess environmental performances and territorial transformations linked to different development needs. The paper fits into this frame by proposing an analytical framework based on the ES multifunctionality approach, i.e., the joint provision of multiple ES, and applying it to the Italian national context. The methodology defines a spatial model based on three aggregate indices (abundance, diversity and richness) assessed considering the Provinces as reference territorial units. Derived from ecological disciplines, these three dimensions of ES multifunctionality describe the variability with which territorial units deliver multiple services for community well-being and support the analysis of the relationships between anthropic components of territorial systems and the ecosystems’ multifunctionality. The evaluation of how the three indices’ spatial distribution varied as a result of land use changes in the period 2000–2018 allows us to highlight specific aspects of territorial units useful to improve the knowledge framework from a sustainable planning perspective. The results highlight its potential to support decision-making processes and formulate recommendations for sustainable spatial planning.
Market Competition, Infrastructure Sharing, and Network Investment in China’s Mobile Telecommunications Industry
Authors: Wang Liang , Sun Qiming
Published: March 2022

The relationship between market competition and network investment in the mobile telecommunications industry has always been one of the focuses for scholars and regulatory agencies. The conclusions of previous studies on this topic remain ambiguous. Infrastructure sharing has become a noticeable trend in the global mobile telecommunications industry, but its impact on operators’ investment and innovation behaviors is controversial. This paper uses fixed effects and dynamic panel data models to empirically examine the relationship among the market competition, infrastructure sharing, and network investment in China’s mobile telecommunications industry. The results show that market competition has a significant positive impact on the total industry network investment, but the interaction of market competition and infrastructure sharing has undermined network investment, which indicates that the competitive strategy of rational operators will shift from facility-based competition to service-based competition when both deep infrastructure sharing and fierce market competition appear at the same time, and this is likely to cause insufficient incentives for investment in advanced technology. This paper suggests that China’s regulators should improve the market structure, enhance market competitiveness continuously, and support infrastructure sharing, but at the same time they should exercise caution when conducting in-depth infrastructure sharing. In addition, they should accelerate the development of 5G vertical industry applications to expand the market space for industry development.
Evaluation on Seismic Performance of Beam-Column Joints of Fabricated Steel Structure with Replaceable Energy-Dissipating Elements
Authors: Li Yuanqi , Huang Binhui
Published: March 2022

This research proposes a beam-column hinged joint with additional replaceable energy-dissipating elements, which is highly industrialized and fully fabricated. In this structure, steel beam and steel column are hinged with pins, at which corners a replaceable energy dissipation element is added. The energy dissipation element is rigidly connected to the steel column through a section of H-beam and high-strength bolts and is hinged to the steel beam using high-strength bolts. The main materials, such as energy dissipation elements, steel columns, and steel beams, are all steel with a design yield strength of 345 MPa. Under the condition that the vertical clear distance between the energy dissipation element and the steel beam is constant at 0.2 m, and the size and section of the beams and columns remain unchanged, six groups of different test samples are constructed by changing the thickness and the horizontal length of the energy-dissipating element. Through the experimental research and numerical simulation of 6 groups of specimens, the strength, stiffness, ductility, hysteresis curve, energy dissipation coefficient, equivalent viscous damping coefficient, and failure mechanism of the joints are obtained, and the horizontal section of the energy dissipation element is mainly analyzed. The effects of parameters such as the ratio of length to span and its ratio to the linear stiffness of steel beams on the seismic performance of the joints were compared with those of traditional welded steel frame beam-column joints. The research results show that the joints can be fully assembled, the energy-consuming components can be replaced, and the beam-column connection joints can be controlled in practical applications. The deviation between the experimental results and the numerical simulation results is less than 10%, which is in good agreement. The failure mode of the node conforms to the seismic performance concept of “energy-dissipating elements are destroyed first and easily replaced after earthquakes”; when the ratio of the horizontal length to the span of the energy-consuming components is 0.225, and the ratio to the linear stiffness of the steel beam is 0.7, the seismic performance is close to or superior to that of traditional welded steel frame beam-column joints, that is, equal to or better than traditional welded steel frame beam-column joints.
The Measurement of High-Quality Development Level of Tourism: Based on the Perspective of Industrial Integration
Authors: Lu Yi
Published: March 2022

The integration of the tourism industry is an important motivation in promoting high-quality development. Based on the analysis of the relationship between tourism industry integration and high-quality development, this paper constructs an evaluation index system to evaluate the high-quality development level of the tourism industry for 2011–2018 in China by using a combination of fuzzy mathematics and a clustering model, which includes 4 dimensions, 11 secondary indicators and 28 tertiary indicators. The results show that the level of high-quality development in China’s tourism industry has been greatly improved, but the gap between regions is large. The performance level of the tourism industry economy is relatively high, and the tourism industry structure and industrial integration level need to be improved urgently. There are differences in the overall level and degrees of change of the high-quality development level of the tourism industry in 31 provincial regions, and there are also differences in the main influencing factors.
China’s River Chief Policy and the Sustainable Development Goals: Prefecture-Level Evidence from the Yangtze River Economic Belt
Authors: Wang Yufeng , Wu Tao , Huang Mengke
Published: March 2022

The River Chief Policy (RCP), an institutional innovation in China by which top party and government officials assume responsibility for water management, shapes the incentive structure of local governments and may have a huge influence on the Sustainable Development Goals (SDGs). Using a staggered difference-in-difference approach and panel data from 91 cities in the Yangtze River Economic Belt, we estimate the impact of the RCP on an SDG index with eight local-specific indicators. The estimation results show that the RCP has improved the overall SDG index and significantly improved the levels of innovation, education, and consumption. Heterogeneity tests show that more affluent regions are more committed to investing in education, raising consumption, and increasing wages under the RCP. These results suggest that local governments in China have responded rationally and strategically to the RCP. In general, economic growth remains the central goal of local governments, while the strengthening of other responsibilities such as environmental protection will lead to more effort being made to achieve the SDGs during and after the COVID-19 pandemic.
Adaptive Retrofit for Adaptive Reuse: Converting an Industrial Chimney into a Ventilation Duct to Improve Internal Comfort in a Historic Environment
Authors: De Vita Mariangela , Duronio Francesco , De Vita Angelo , De Berardinis Pierluigi
Published: March 2022

The reuse of architectural heritage is a topic of great interest for scientific research, involving aspects ranging from the architectural compatibility of the interventions to the performance updating of the artefacts, from the point of view of both energy consumption and internal comfort suitable for the new use. Compatible technological solutions exploit the passive cooling activating latent physical mechanisms of the building, of the envelope or its parts, such as openings and disused shafts. This work concerns the conversion of an old chimney, completely integrated into the historical envelope, into a ventilation duct for the air exchange and the internal comfort improvement of an old factory, proposing an adaptive retrofit solution during adaptive reuse intervention. Thermo-fluid dynamics analyses, performed with an ad hoc CFD solver for flows with flotation effects, verified the effective functionality of the device in summer and winter conditions. The results show that, in summer, the activation of passive ventilation improves the indoor comfort of the environment, while, in winter, it worsens them. This study demonstrates the usefulness of activating passive cooling phenomena in preserving historical architecture. Finally, the future potential of the application is presented by integrating the ventilation chimney with a mechanical control system to optimize its operation even in winter conditions.
Modified Harmony Search Algorithm-Based Optimization for Eco-Friendly Reinforced Concrete Frames
Authors: Bekdaş Gebrail , Nigdeli Sinan Melih , Kim Sanghun , Geem Zong Woo
Published: March 2022

Cost and CO2 are two factors in the optimum design of structures. This study proposes a modified harmony search methodology for optimization of reinforced concrete beams with minimum CO2 emissions. The optimum design was developed in detail by considering all possible combinations of variable loads, including dynamic force resulting from earthquake motion. Moreover, time-history analyses were performed, and requirements of the ACI-318 building code were considered in the reinforced concrete design. The results show that the optimum design based on CO2 emission minimization is greatly different from the optimum cost design results. According to these results, using recycled members provides a sustainable design.