bluuebunny/crossref_metadata_2025 huggingface dataset:
Underwater Optical Wireless Communications: Overview
Authors: Schirripa Spagnolo Giuseppe , Cozzella Lorenzo , Leccese Fabio
Published: April 2020

Underwater Optical Wireless Communication (UOWC) is not a new idea, but it has recently attracted renewed interest since seawater presents a reduced absorption window for blue-green light. Due to its higher bandwidth, underwater optical wireless communications can support higher data rates at low latency levels compared to acoustic and RF counterparts. The paper is aimed at those who want to undertake studies on UOWC. It offers an overview on the current technologies and those potentially available soon. Particular attention has been given to offering a recent bibliography, especially on the use of single-photon receivers.
Combination of Smartphone MEMS Sensors and Environmental Prior Information for Pedestrian Indoor Positioning
Authors: Huang Lu , Li Hongsheng , Yu Baoguo , Gan Xingli , Wang Boyuan , Li Yaning , Zhu Ruihui
Published: April 2020

In view of the inability of Global Navigation Satellite System (GNSS) to provide accurate indoor positioning services and the growing demand for location-based services, indoor positioning has become one of the most attractive research areas. Moreover, with the improvement of the smartphone hardware level, the rapid development of deep learning applications on mobile terminals has been promoted. Therefore, this paper borrows relevant ideas to transform indoor positioning problems into problems that can be solved by artificial intelligence algorithms. First, this article reviews the current mainstream pedestrian dead reckoning (PDR) optimization and improvement methods, and based on this, uses the micro-electromechanical systems (MEMS) sensor on a smartphone to achieve better step detection, stride length estimation, and heading estimation modules. In the real environment, an indoor continuous positioning system based on a smartphone is implemented. Then, in order to solve the problem that the PDR algorithm has accumulated errors for a long time, a calibration method is proposed without the need to deploy any additional equipment. An indoor turning point feature detection model based on deep neural network is designed, and the accuracy of turning point detection is 98%. Then, the particle filter algorithm is used to fuse the detected turning point and the PDR positioning result, thereby realizing lightweight cumulative error calibration. In two different experimental environments, the performance of the proposed algorithm and the commonly used localization algorithm are compared through a large number of experiments. In a small-scale indoor office environment, the average positioning accuracy of the algorithm is 0.14 m, and the error less than 1 m is 100%. In a large-scale conference hall environment, the average positioning accuracy of the algorithm is 1.29 m, and 65% of the positioning errors are less than 1.50 m which verifies the effectiveness of the proposed algorithm. The simple and lightweight indoor positioning design scheme proposed in this article is not only easy to popularize, but also provides new ideas for subsequent scientific research in the field of indoor positioning.
Wireless Motion Sensors—Useful in Assessing the Effectiveness of Physiotherapeutic Methods Used in Patients with Knee Osteoarthritis—Preliminary Report
Authors: Goślińska Jagoda , Wareńczak Agnieszka , Miedzyblocki Margaret , Hejdysz Krystyna , Adamczyk Ewa , Sip Paweł , Chlebuś Ewa , Gośliński Jarosław , Owczarek Piotr , Woźniak Adam , Lisiński Przemysław
Published: April 2020

Osteoarthritis of the knee (OAK) is characterized by pain, limitation of joint mobility, and significant deterioration of proprioception resulting in functional decline. This study assessed proprioception in OAK patients following two ten-day rehabilitation programs using the Orthyo® system. Fifty-four study participants with clinical symptoms and radiological signs of OAK were randomly divided into an exercise group (n = 27) or a manual therapy group (n = 27). The control group consisted of 27 volunteers with radiological signs of OAK, but with no clinical symptoms or prior history of rehabilitation. The following parameters were assessed: knee proprioception using inertial sensors and a mobile application, patients’ function using Western Ontario and McMaster Universities osteoarthritis index (WOMAC), and pain intensity using the visual analog scale (VAS). Following rehabilitation, knee proprioception tests did not improve in either study group. Both study groups showed significant improvement of the WOMAC-assessed function (exercise group: p < 0.01, manual therapy group: p = 0.01) and a significant decrease (p < 0.01) of VAS-assessed pain following rehabilitation, but the post-therapy results did not differ significantly between the aforementioned groups. The Orthyo® system provided a quick and accurate assessment of the knee joint position sense. There was no direct relationship between functionality, pain, and proprioception threshold in the knee joint.
Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
Authors: Ran Maoli , Bai Xiangyu
Published: April 2020

In this paper, we propose an optimization framework of vehicular fog computing and a cooperation vehicular network model. We aim to improve the performance of vehicular fog computing and solve the problem that the data of the vehicle collaborative network is difficult to obtain. This paper applies the hypergraph theory to study the underlying structure, considering the social characteristics of the vehicles and vehicle communication. Since the vehicles join the network in accordance with the Poisson process law, the model is analyzed by using Poisson stochastic process and mean field theory. This paper uses MATLAB to simulate the evolution process of cooperative networks. The results show that the vehicle’s super-degree in vehicular fog computing has scale-free characteristics. Through this model, the vehicle cooperation situation can be analyzed, and the vehicle dynamics can be accurately predicted to further improve the performance of vehicular fog computing. The model can be transformed into some complex network models by adjusting the parameters. It has strong universality and has certain reference significance for the research on the related characteristics of VANETs and the theoretical research of the cooperative network.
Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review
Authors: Khan Faisal , Salahuddin Saqib , Javidnia Hossein
Published: April 2020

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.
Fabrication and Characterization of a Flexible Fluxgate Sensor with Pad-Printed Solenoid Coils
Authors: Schoinas Spyridon , El Guamra Adyl-Michaël , Moreillon Fabien , Passeraub Philippe
Published: April 2020

This paper presents the fabrication and characterization of a flexible, flat, miniaturized fluxgate sensor with a thin amorphous rectangular magnetic core fabricated by the pad/printing technique. Both the design and the various printing steps of the sensor are presented. The fluxgate sensor comprises of solenoid coils, and to the best of our knowledge, is the first to be printed with a conventional micro-printing technique. The magnetic core is a non-printed component, placed between the printed layers. The sensor’s linear measuring range is ±40 µT with 2% full-scale linearity error, at 100 kHz excitation frequency. The highest measured sensitivity reaches 14,620 V/T at 200 kHz, while the noise of the sensor was found to be 10 nT/ Hz at 1 Hz.
Efficiency Analysis of the Progress of Orange Farms in Tuyen Quang Province, Vietnam towards Sustainable Development
Authors: Tran Nguyen Thu Trang , Le Hai Ha , Ho Thi Minh Hop , Dogot Thomas , Burny Philippe , Bui Thi Nga , Lebailly Philippe
Published: April 2020

Nowadays, Vietnam is known as a developing country with a fast-growing economy. Agriculture is the main traditional sector which plays an important role in Vietnamese economic growth and development. Improving the efficiency of agricultural production towards sustainable development is one of the country’s national economic development goals. This study aims to measure the efficiency of the orange farms which have created jobs and improved living standards for people in Tuyen Quang province, Vietnam. A comprehensive literature review, key informant interviews, and a structured questionnaire were applied in this research for data collection. The data envelopment analysis (DEA) model was applied to evaluate the technical, allocative, cost, and scale efficiencies. The main results of this analysis show that the orange farms have high scores for technical and scale efficiencies. On the other hand, the research reveals low levels of cost and allocative efficiencies. This is in line with the fact that though the farms’ owners have extensive experience in orange cultivation and receive periodic technical training, they still have low levels of education and a lack of economic management expertise. The study results also propose optimal input allocation for the orange farmers. The research could provide crucial information to farms’ owners, the local government, and agricultural planners for formulating effective strategies to improve agricultural sustainability.
Park and Pool Lots’ Impact on Promoting Shared Mobility and Carpooling on Highways: The Case of Slovenia
Authors: Rijavec Robert , Dadashzadeh Nima , Žura Marijan , Marsetič Rok
Published: April 2020

Dispersed settlement areas (e.g., Slovenia) result in lower development of fixed public transport lines and thereby car-dependent lifestyles. To avoid congestion inside the cities and on highways in these areas, shared mobility modes e.g., carpooling, is one of the effective solutions, increasing the occupancy of personal vehicles. However, passenger pick-up and drop-off locations still remain an important challenge for carpool users and transport officials. As a collection point for carpooling, we can consider “park and pool (P+P)” lots near highways’ interchanges. This study aims to examine the impacts of P+P lots near interchanges on carpooling behavior of users and on improving sustainable mobility on highways in such dispersed settlement areas. To do so, we employed a field survey, incorporated the P+P lots into the mode choice model, and examined different scenarios using the macroscopic transport model. It is found that factors such as travel cost, public transport service limitations, and improved parking facilities impact highway users’ mode choice. Sixty percent of respondents are willing to leave their car in P+P lots near interchanges. The results also show that P+P lots can increase the number of carpool users. It causes remarkable savings in terms of operating and external costs because of the reduction in total distance and time travelled by personal vehicles as well as parking demand reduction in cities. At the moment, especially in Central Europe, it is easier to invest in a “demand reduction” infrastructure than to increase the capacity of infrastructure. Therefore, P+P lots could be a “win-win” situation for both users and operators.
Historical Suitability and Sustainability of Sicani Mountains Landscape (Western Sicily): An Integrated Approach of Phytosociology and Archaeobotany
Authors: Bazan Giuseppe , Speciale Claudia , Castrorao Barba Angelo , Cambria Salvatore , Miccichè Roberto , Marino Pasquale
Published: April 2020

Since 2015, the ongoing project “Harvesting Memories” has been focused on long-term landscape dynamics in Sicani Mountains (Western Sicily). Archaeological excavations in the case study site of Contrada Castro (Corleone) have investigated a settlement which was mainly occupied during the Early Middle Ages (late 8th–11th century AD). This paper aims to understand the historical suitability and sustainability of this area analysing the correlation between the current dynamics of plant communities and the historical use of woods detected by the archaeobotanical record. An integrated approach between phytosociology and archaeobotany has been applied. The vegetation series of the study area has been used as a model to understand the ecological meaning and spatial distribution of archaeobotanical data on charcoals from the Medieval layers of the Contrada Castro site. The intersection between the frequency data of the archaeobotanical record and the phytosociological analysis have confirmed the maintenance of the same plant communities during the last millennium due to the sustainable exploitation of wood resources. An integrated comparison between the structure and composition of current phytocoenoses with archaeobotanical data allowed us to confirm that this landscape is High Nature Value (HNV) farmland and to interpret the historical vegetation dynamics linked to the activities and economy of a rural community.
Energy-Efficient and Integrated Allocation of Berths, Quay Cranes, and Internal Trucks in Container Terminals
Authors: Karam Ahmed , Eltawil Amr , Hegner Reinau Kristian
Published: April 2020

Despite a significant number of studies that have focused on the operational efficiency of container terminals, existing literature has paid little attention to improving energy efficiency, e.g., energy consumption and negative externalities in container terminals. Most researchers consider energy-saving goals when allocating berths and quay cranes to vessels, assuming that internal trucks are in sufficient supply. Furthermore, recent studies have revealed that shortage of internal trucks has become an issue that greatly affects the operational and energy efficiencies of container terminals. This work presents a planning model that integrates berth allocation, quay crane assignment, and internal truck assignment problems. The developed model contributes to existing literature by including energy-saving goals in the integrated planning of these problems, as well as including important realistic factors such as shortages of internal trucks and handling time estimations, thus producing a reliable handling plan that achieves energy and cost savings without additional truck investment. To solve realistic problems, a Lagrangian relaxation-based method is developed. Furthermore, the benefits of the developed approach are demonstrated by comparing it to an existing approach. On average, our approach could improve the solutions of the integrated problem with different numbers of internal trucks by 6% compared to the solutions obtained using the existing approach.
Psychometric Study of a Scale of Measurement of the Digital Stories Creation Using Utellstory
Authors: Muñoz-González Juan Manuel , Vega-Gea Esther M. , Ballesteros-Regaña Cristobal , Hidalgo-Ariza María Dolores
Published: April 2020

The purpose of this paper is to validate a scale about learning how to use the Utellstory software for the creation of digital stories. The questionnaire was provided to a sample of 142 and 276 students from Masters in Inclusive Education and Early Childhood Education Degree of the University of Córdoba (Spain), respectively. The data were subjected to an analysis of reliability and validity through the use of exploratory and confirmatory factorial analyses. The results revealed an instrument with correct validity and reliability that demonstrate that the model is stable and consistent with the starting theoretical assumptions.
Investing in Sustainable Built Environments: The Willingness to Pay for Green Roofs and Green Walls
Authors: Teotónio Inês , Oliveira Cruz Carlos , Matos Silva Cristina , Morais José
Published: April 2020

Green infrastructure, such as green roofs/walls, plays a key role in addressing urban problems. Despite the well-established sustainable mentality, aspects such as aesthetics, recreation, and welfare are still the main drivers for undertaking such investments. Several studies have focused on proving the benefits of green infrastructure, namely, thermal insulation, air quality, and others. However, significant soft benefits have not yet been properly addressed. It is, therefore, important to understand how much citizens are willing to invest in those mitigating measures and list the aspects that influence that value. This study suggests a methodology based on stated preferences to evaluate the willingness-to-pay of owner/tenants for green roofs/walls in residential buildings and determine the influence of multiple factors. Results show that consumers reveal higher willingness-to-pay for accessible green roofs. Knowledge of benefits and the accessibility of green roofs have a great impact on the willingness-to-pay. Recreation benefit is at the forefront of individuals’ concerns; even more than aesthetics.
Bioenergy for a Cleaner Future: A Case Study of Sustainable Biogas Supply Chain in the Malaysian Energy Sector
Authors: Aziz Nur Izzah Hamna A. , Hanafiah Marlia M. , Gheewala Shabbir H. , Ismail Haikal
Published: April 2020

A life cycle assessment (LCA)-based environmental sustainability evaluation conceptual framework of biogas production has been proposed to improve the sustainability of biogas supply chains. The conceptual framework developed in this study can be used as a guideline for the related stakeholders and decision makers to improve the quality and enhance the sustainability of biogas production in Malaysia as well as promoting biogas as a clean, reliable and secure energy. A case study on an LCA analysis of a zero waste discharge treatment process has been conducted. In the zero discharge treatment system, biogas can be produced with a maximum water recycle and reuse. It was indicated that the biogas production and zero discharge treatment of a palm oil mill effluent were environmentally sustainable as the system utilized organic waste to produce bioenergy and achieved zero discharge. However, there were other aspects that should be taken into consideration, particularly regarding the sources of electricity and upstream activity, to ensure the sustainability of the system holistically.
Opportunities for Improving the Environmental Profile of Silk Cocoon Production under Brazilian Conditions
Authors: Barcelos Silvia Mara Bortoloto Damasceno , Salvador Rodrigo , Guedes Maria da Graça , de Francisco Antonio Carlos
Published: April 2020

Brazilian silk production is amongst the five largest in the world. Nonetheless, there is no life cycle assessment study on silk cocoon production and its upstream processes, pertaining to the mulberry production (fundamental upstream process for silk production), in the existing literature. The objective of this study was to identify opportunities to improve the environmental profile of mulberry and silk cocoon production under Brazilian conditions. To that end, a life cycle assessment was conducted for the core processes of mulberry and silk cocoon production and upstream processes of raw material production, using the ReCiPe method for life cycle impact assessment using nine impact categories. Overall, the mulberry production showed greater impacts than the cocoon production for the impact categories analyzed. A few opportunities for improving the environmental profile of mulberry and silk cocoon production under Brazilian conditions included replacing the Kraft paper used to cover the rearing beds, replacing the standard tractor used to fetch mulberry leaves, replacing light bulbs, conducting a more sustainable mulberry and cocoon production, and setting a reverse logistics system for plastic and paper packaging waste. Nevertheless, many of these measures are long-term strategies. Besides, many of them need further economic feasibility assessment.
Development of Environmental Health Competencies through Compulsory Education. A Polyhedral Approach Based on the SDGs
Authors: Montero-Pau Javier , Álvaro Nuria , Gavidia Valentín , Mayoral Olga
Published: April 2020

This paper focuses on the competencies in environmental health acquired by students during compulsory education. Questionnaires addressing environmental health problems were completed by 923 students of primary and secondary schools from five different Spanish regions. The results for five challenging situations related to hunger, consumerism, climate change, pollution in the cities and allergies are analysed according to the internal coherence of each sub-competency, i.e., addressing knowledge, skills and attitudes towards these topics. Our results show that problems related to air and water pollution were the most commonly described by the students. Focusing on competency achievement, the higher the educational level, the higher the score students obtained, especially regarding pollution and climate change, two problems that appear directly in the school curriculum. The complexity of the concept of environmental health matches with the necessary holistic perspective of the Sustainable Development Goals in a polyhedral approach including as many factors (facets) as necessary to complete the approach to this evolving concept.
A Combination of DEMATEL and BWM-Based ANP Methods for Exploring the Green Building Rating System in Taiwan
Authors: Liu Perry C. Y. , Lo Huai-Wei , Liou James J. H.
Published: April 2020

Green buildings are a crucial environmentally friendly component for sustainable human habitation and for the preservation of a healthy ecosystem. This study proposes a novel hybrid model to explore the influential relationship of various indicators and their importance for a green building evaluation system. First, a green building index system is established based on a review of recent literature on green building evaluation systems. Then, the decision making trial and evaluation laboratory (DEMATEL) technique is utilized to determine the influential relationship among the criteria. Finally, a novel best worst method (BWM)-based analytic network process (ANP) model is applied to determine the influential weight of the criteria. The result indicated that the top five ranked criteria are all classified within the two dimensions of waste reduction and energy saving. An empirical case study of an example in Taiwan is carried out to demonstrate the effectiveness of the proposed model. Management implications are also provided.
The Innovativeness and Usage of the Balanced Scorecard Model in SMEs
Authors: Dudic Zdenka , Dudic Branislav , Gregus Michal , Novackova Daniela , Djakovic Ivana
Published: April 2020

By using the balanced scorecard model, a company is able to identify its advantages, as well as its deficiencies, and thus improve its business. The introduction of innovations and the implementation of innovative activities in companies are key for gaining a competitive advantage. There is no ideal model that would measure the non-financial, non-tangible perspectives of a company (such as customer perspective, the perspective of research and innovation, and the perspective of internal processes). The main goal of this paper is researching the applicability of the balanced scorecard model in small- and medium-sized companies as the basis for a model for assessing innovative activities in the Republic of Slovakia and the Republic of Serbia. First, a hypothetical model was created based on theoretical data from world scientific articles. Then, the structural equation model (SEM model) was created, based on the conducted research in 223 companies and the obtained results.
AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change
Authors: Fathi Soheil , Srinivasan Ravi S. , Kibert Charles J. , Steiner Ruth L. , Demirezen Emre
Published: April 2020

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).
Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach
Authors: Runfola Daniel , Batra Geeta , Anand Anupam , Way Audrey , Goodman Seth
Published: April 2020

Since 1992, the Global Environment Facility (GEF) has mobilized over $131 billion in funds to enable developing and transitioning countries to meet the objectives of international environmental conventions and agreements. While multiple studies and reports have sought to examine the environmental impact of these funds, relatively little work has examined the potential for socioeconomic co-benefits. Leveraging a novel database on the geographic location of GEF project interventions in Uganda, this paper explores the impact of GEF projects on household assets in Uganda. It employs a new methodological approach, Quasi-experimental Geospatial Interpolation (QGI), which seeks to overcome many of the core biases and limitations of previous implementations of causal matching studies leveraging geospatial information. Findings suggest that Sustainable Forest Management (SFM) GEF projects with initial implementation dates prior to 2009 in Uganda had a positive, statistically significant impact of approximately $184.81 on the change in total household assets between 2009 and 2011. Leveraging QGI, we identify that (1) this effect was statistically significant at distances between 2 and 7 km away from GEF projects, (2) the effect was positive but not statistically significant at distances less than 2 km, and (3) there was insufficient evidence to establish the impact of projects beyond a distance of approximately 7 km.
Proposing a Multidimensional Bankruptcy Prediction Model: An Approach for Sustainable Islamic Banking
Authors: Mehreen Mehreen , Marimuthu Maran , Karim Samsul , Jan Amin
Published: April 2020

The main purpose of this study is to conceptualize a sustainable banking model for Islamic banking by blending three essential business aspects namely financial performance, Islamic corporate governance, and sustainability practices dimension. In the case of Islamic banking, evidence shows that a Shariah-based bankruptcy prediction model for apprehending the true bankruptcy prediction is over-sighted. This study offers an efficient Shariah-based bankruptcy prediction model by first, reviewing the previously applied conventional bankruptcy prediction models; secondly, by developing and proposing a robust, multidimensional model for predicting bankruptcy in Islamic banking. This framework may have profound implications on the existing bankruptcy evaluation structure of the Islamic banking industry and may provide a strong sustainability management guideline to the global Islamic banking industry.
A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015
Authors: Sakti Anjar Dimara , Takeuchi Wataru
Published: April 2020

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.
Toward an Evolutionary and Sustainability Perspective of the Innovation Ecosystem: Revisiting the Panarchy Model
Authors: Boyer James
Published: April 2020

This paper proposes an evolutionary and sustainability perspective of the innovation ecosystem. This study revisits the Panarchy model in order to generate new perspectives on the innovation ecosystem. The Panarchy model describes the evolutionary nature of complex adaptive systems relying on four phases, without, however, being deterministic: exploitation, conservation, decline, and reorganization. When ecosystems face important shocks, adaptive mechanisms and properties within the ecosystem lead the ecosystem to a new reorganization phase, which gives birth to another exploitation phase. In this perspective, the innovation ecosystem allows the avoidance of technology lock-ins and structural and organizational rigidity by providing mechanisms to enhance both resilience and competitiveness. Innovation ecosystem sustainability relies on two major dual forces: the exploitative function and the generative or autopoiesis function. Therefore, evolutionary and sustainability perspectives remain the “natural home” for developing works and models about the innovation ecosystem, and instrumental for policy-makers and practitioners involved in innovation management.
Urban Forestry in Brazilian Amazonia
Authors: Vieira Thiago Almeida , Panagopoulos Thomas
Published: April 2020

Urban forests provide multiple benefits in improving people’s lives and can be an important tool for achieving the goal of carbon neutral cities. In this study, we analyzed the diversity of plant species from urban forests in cities in the Brazilian Amazonia, based on data from scientific articles, through a systematic literature review. Our analysis revealed that 530 taxa, of which 479 were identified at the species level and 51 at the genus level, covering 38,882 individuals were distributed in 29 cities. The three most frequent species were Ficus benjamina, Mangifera indica, and Licania tomentosa. Exotic species were more frequent than native. The three most frequent species had almost 42% of the inventoried individuals. The choice of species has been made mainly by the local population, without monitoring by the public authorities. Recommendations for sustainable management of urban forests in Amazonia include investing in training of management bodies, periodic inventories, and awareness actions about the benefits of urban green infrastructure and on the advantages of native species. Policies for the sustainable management of urban green areas are necessary. The municipal governments must continuously monitor indicators of urban ecosystem services and provide financial resources for maintaining and increasing those area rates per person.
Pricing Policies in a Retailer Stackelberg O2O Green Supply Chain
Authors: Wan Gan , Kou Gang , Li Tie , Xiao Feng , Chen Yang
Published: April 2020

Due to the popularization of the concept of “new retailing”, we study a new commercial model named O2O (online-to-offline), which is a good combination model of a direct channel and a traditional retail channel. We analyze an O2O supply chain in which manufacturers are responsible for making green products and selling them through both online and offline channels. The retailer is responsible for all online and offline channels’ orders, and the manufacturer gives the retailer a fixed fee. We construct a mathematical function model and analyze the greenness and pricing strategies of centralized and decentralized settings through the retailer Stackelberg game model. Due to the effects of the double marginalization of supply chain members, we adopt a simple contract to coordinate the green supply chain. The paper’s contributions are that we obtain pricing and greening strategies by taking the cooperation of offline channels and online channels into consideration under the O2O green supply chain environment.
Spatial-Temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018
Authors: Sui Lichun , Wang Jun , Yang Xiaomei , Wang Zhihua
Published: April 2020

As a valuable resource in coastal areas, coastlines are not only vulnerable to natural processes such as erosion, siltation, and disasters, but are also subjected to strong pressures from human processes such as urban growth, resource development, and pollution discharge. This is especially true for reef nations with rich coastline resources and a large population, like Indonesia. The technical joint of remote sensing (RS) and geographic information system (GIS) has significant advantages for monitoring coastline changes on a large scale and for quantitatively analyzing their change mechanisms. Indonesia was taken as an example in this study because of its abundant coastline resources and large population. First, Landsat images from 1990 to 2018 were used to obtain coastline information. Then, the index of coastline utilization degree (ICUD) method, the changes in land and sea patterns method, and the ICUD at different scales method were used to reveal the spatiotemporal change pattern for the coastline. The results found that: (1) Indonesia’s total coastline length has increased by 777.40 km in the past 28 years, of which the natural coastline decreased by 5995.52 km and the artificial coastline increased by 6771.92 km. (2) From the analysis of the island scale, it was known that the island with the largest increase in ICUD was Kalimantan, at the expense of the mangrove coastline. (3) On the provincial scale, the province with the largest change of ICUD was Sumatera Selatan Province, which increased from 100 in 1900 to 266.43 in 2018. (4) The change trend of the land and sea pattern for the Indonesian coastline was mainly expanded to the sea. The part that eroded to the land was relatively small; among which, Riau Province had the most significant expansion of land area, about 177.73 km2, accounting for 23.08% of the increased national land area. The worst seawater erosion was in the Jawa Barat Province. Based on the analysis of population and economic data during the same period, it was found that the main driving mechanism behind Indonesia’s coastline change was population growth, which outweighed the impact of economic development. However, the main constraint on the Indonesian coastline was the topographic factor. The RS and GIS scheme used in this study can not only provide support for coastline resource development and policy formulation in Indonesia, but also provide a valuable reference for the evolution of coastline resources and environments in other regions around the world.
Educating at Scale for Sustainable Development and Social Enterprise Growth: The Impact of Online Learning and a Massive Open Online Course (MOOC)
Authors: Calvo Sara , Lyon Fergus , Morales Andrés , Wade Jeremy
Published: April 2020

The solutions to the grand challenges of sustainability, poverty, and health affecting the world will require education and capacity building for all individuals implementing change on a global scale. The challenge ahead is to reach those missed by traditional education and support networks. Online Learning and Massive Open Online Courses (MOOCs) have the potential to build knowledge and share best practice experiences among individuals worldwide. This paper examines the case of the FutureLearn Social Enterprise Program, a series of MOOCs with over 50,000 registered learners, of which 15% become active learners, engaging in online exercises, debates, and conversations. This paper draws on quantitative and qualitative data collected over four years. The findings show that the course has not only had an impact on the creation of new startup social enterprises, but it has also supported a large proportion of learners in developing sustainability and social entrepreneurial ideas within a range of organisations in the public, private, and civil society sectors. The findings also show a positive cascading impact effect from the learners registered on the course to those in their network, as ideas are shared, and learners become mentors to others. Our conclusions demonstrate how digital education and online courses contribute to global education for sustainable development and social enterprise development.
Life Cycle Costing: Understanding How It Is Practised and Its Relationship to Life Cycle Management—A Case Study
Authors: Kambanou Marianna Lena
Published: April 2020

Despite the existence of many life cycle costing (LCC) methods, LCC is not widely adopted and LCC methods are usually further tailored by practitioners. Moreover, little is known about how practising LCC improves life cycle management (LCM) especially if LCM is considered emergent and constantly developing. In a manufacturing company, LCC is prescriptively introduced to improve LCM. In the first part, this study describes how various methodological choices and other aspects of practising LCC were the outcome of contestation and conformity with extant practices and not only the best way to fulfil the LCC’s objective. This contestation can even influence if LCC is adopted. In the second part of the research, the implications of practising LCC on LCM are explored. LCC is found to positively propel LCM in many ways e.g., by spreading the life cycle idea, but may lead to a narrower understanding of the term life cycle resulting in the sustainability focus of LCM being overridden. The article also discusses how the findings can be taken into consideration when researchers develop LCC methods and when industry practises LCC.
Correction: Liu, P., et al. Threshold Analysis and Stationary Distribution of a Stochastic Model with Relapse and Temporary Immunity. Symmetry 2020, 12, 331
Authors: Liu Peng , Meng Xinzhu , Qi Haokun
Published: April 2020

The authors wish to make the following corrections and explanations to this paper [...]
Singular Nonsymmetric Jack Polynomials for Some Rectangular Tableaux
Authors: Dunkl Charles F.
Published: April 2020

In the intersection of the theories of nonsymmetric Jack polynomials in N variables and representations of the symmetric groups S N one finds the singular polynomials. For certain values of the parameter κ there are Jack polynomials which span an irreducible S N -module and are annihilated by the Dunkl operators. The S N -module is labeled by a partition of N, called the isotype of the polynomials. In this paper the Jack polynomials are of the vector-valued type, i.e., elements of the tensor product of the scalar polynomials with the span of reverse standard Young tableaux of the shape of a fixed partition of N. In particular, this partition is of shape m , m , … , m with 2 k components and the constructed singular polynomials are of isotype m k , m k for the parameter κ = 1 / m + 2 . This paper contains the necessary background on nonsymmetric Jack polynomials and representation theory and explains the role of Jucys–Murphy elements in the construction. The main ingredient is the proof of uniqueness of certain spectral vectors, namely the list of eigenvalues of the Jack polynomials for the Cherednik–Dunkl operators, when specialized to κ = 1 / m + 2 . The paper finishes with a discussion of associated maps of modules of the rational Cherednik algebra and an example illustrating the difficulty of finding singular polynomials for arbitrary partitions.
A Confrontation Decision-Making Method with Deep Reinforcement Learning and Knowledge Transfer for Multi-Agent System
Authors: Hu Chunyang
Published: April 2020

In this paper, deep reinforcement learning (DRL) and knowledge transfer are used to achieve the effective control of the learning agent for the confrontation in the multi-agent systems. Firstly, a multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm with parameter sharing is proposed to achieve confrontation decision-making of multi-agent. In the process of training, the information of other agents is introduced to the critic network to improve the strategy of confrontation. The parameter sharing mechanism can reduce the loss of experience storage. In the DDPG algorithm, we use four neural networks to generate real-time action and Q-value function respectively and use a momentum mechanism to optimize the training process to accelerate the convergence rate for the neural network. Secondly, this paper introduces an auxiliary controller using a policy-based reinforcement learning (RL) method to achieve the assistant decision-making for the game agent. In addition, an effective reward function is used to help agents balance losses of enemies and our side. Furthermore, this paper also uses the knowledge transfer method to extend the learning model to more complex scenes and improve the generalization of the proposed confrontation model. Two confrontation decision-making experiments are designed to verify the effectiveness of the proposed method. In a small-scale task scenario, the trained agent can successfully learn to fight with the competitors and achieve a good winning rate. For large-scale confrontation scenarios, the knowledge transfer method can gradually improve the decision-making level of the learning agent.
Relaxation of the Radio-Frequency Linewidth for Coherent-Optical Orthogonal Frequency-Division Multiplexing Schemes by Employing the Improved Extreme Learning Machine
Authors: Zabala-Blanco David , Mora Marco , Azurdia-Meza Cesar A. , Dehghan Firoozabadi Ali , Palacios Játiva Pablo , Soto Ismael
Published: April 2020

A coherent optical (CO) orthogonal frequency division multiplexing (OFDM) scheme gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, as any coherent-detection OFDM system, the overall system performance is limited by laser phase noises. On the other hand, extreme learning machines (ELMs) have gained a lot of attention from the machine learning community owing to good generalization performance, negligible learning speed, and minimum human intervention. In this manuscript, a phase-error mitigation method based on the single-hidden layer feedforward network prone to the improved ELM algorithm for CO-OFDM systems is introduced for the first time. In the training step, two steps are distinguished. Firstly, pilots are used, which is very common in OFDM-based systems, to diminish laser phase noises as well as to correct frequency-selective impairments and, therefore, the bandwidth efficiency can be maximized. Secondly, the regularization parameter is included in the ELM to balance the empirical and structural risks, namely to minimize the root mean square error in the test stage and, consequently, the bit error rate (BER) metric. The operational principle of the real-complex (RC) ELM is analytically explained, and then, its sub-parameters (number of hidden neurons, regularization parameter, and activation function) are numerically found in order to enhance the system performance. For binary and quadrature phase-shift keying modulations, the RC-ELM outperforms the benchmark pilot-assisted equalizer as well as the fully-real ELM, and almost matches the common phase error (CPE) compensation and the ELM defined in the complex domain (C-ELM) in terms of the BER over an additive white Gaussian noise channel and different laser oscillators. However, both techniques are characterized by the following disadvantages: the CPE compensator reduces the transmission rate since an additional preamble is mandatory for channel estimation purposes, while the C-ELM requires a bounded and differentiable activation function in the complex domain and can not follow semi-supervised training. In the same context, the novel ELM algorithm can not compete with the CPE compensator and C-ELM for the 16-ary quadrature amplitude modulation. On the other hand, the novel ELM exposes a negligible computational cost with respect to the C-ELM and PAE methods.
Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization
Authors: Wang Zong-Sheng , Lee Jung , Song Chang Geun , Kim Sun-Jeong
Published: April 2020

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.