bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Structural, Photochemical and Instrumental Effects on Leaf and Canopy Reflectance Variability in the 500–600 nm Range
Authors: Moncholi-Estornell Adrián , Van Wittenberghe Shari , Cendrero-Mateo Maria Pilar , Alonso Luis , Malenovský Zbyněk , Moreno José
Published: December 2021

Current rapid technological improvement in optical radiometric instrumentation provides an opportunity to develop innovative measurements protocols where the remote quantification of the plant physiological status can be determined with higher accuracy. In this study, the leaf and canopy reflectance variability in the PRI spectral region (i.e., 500–600 nm) is quantified using different laboratory protocols that consider both instrumental and experimental set-up aspects, as well as canopy structural effects and vegetation photoprotection dynamics. First, we studied how an incorrect characterization of the at-target incoming radiance translated into an erroneous vegetation reflectance spectrum and consequently in an incorrect quantification of reflectance indices such as PRI. The erroneous characterization of the at-target incoming radiance translated into a 2% overestimation and a 31% underestimation of estimated chlorophyll content and PRI-related vegetation indexes, respectively. Second, we investigated the dynamic xanthophyll pool and intrinsic Chl vs. Car long-term pool changes affecting the entire 500–600 nm spectral region. Consistent spectral behaviors were observed for leaf and canopy experiments. Sun-adapted plants showed a larger optical change in the PRI range and a higher capacity for photoprotection during the light transient time when compared to shade-adapted plants. Outcomes of this work highlight the importance of well-established spectroscopy sampling protocols to detect the subtle photochemical features which need to be disentangled from the structural and biological effects.
Effect of the Partitioning of Diffuse and Direct APAR on GPP Estimation
Authors: Chen Siyuan , Sui Lichun , Liu Liangyun , Liu Xinjie
Published: December 2021

Accurate estimation of gross primary productivity (GPP) is necessary to better understand the interaction of global terrestrial ecosystems with climate change and human activities. Light use efficiency (LUE)-based GPP models are widely used for retrieving several GPP products with various temporal and spatial resolutions. However, most LUE-based models assume a clear-sky condition, and the influence of diffuse radiation on GPP estimations has not been well considered. In this paper, a diffuse and direct (DDA) absorbed photosynthetically active radiation (APAR)-based method is proposed for better estimation of half-hourly GPP, which partitions APAR under diffuse and direct radiation conditions. Firstly, energy balance residual (EBR) FAPAR, moderate resolution imaging spectroradiometer (MODIS) leaf area index (LAI) (MCD15A2H) and clumping index (CI) products, as well as solar radiation records supplied by FLUXNET2015 were used to calculate diffuse and direct APAR at a half-hourly scale. Then, an eddy covariance-LUE (EC-LUE) model and meteorological observations from FLUXNET2015 data sets were used for obtaining corresponding LUE values. A co-variation relationship between LUE and diffuse fraction was observed, and the LUE was higher under more diffuse radiation conditions. Finally, the DDA-based method was tested using the half-hourly FLUXNET GPP and compared with half-hourly GPP calculated using total APAR (GPP_TA). The results indicated that the half-hourly GPP estimated using the DDA-based method (GPP_DDA) was more accurate, giving higher R2 values, lower RMSE and RMSE* values (R2 varied from 0.565 to 0.682, RMSE ranged from 3.219 to 12.405 and RMSE* were within the range of 2.785 to 8.395) than the GPP_TA (R2 varied from 0.558 to 0.653, RMSE ranged from 3.407 to 13.081 and RMSE* were within the range of 3.321 to 9.625) across FLUXNET sites within different vegetation types. This study explored the effects of partitioning the diffuse and direct APAR on half-hourly GPP estimations, which demonstrates a higher agreement with FLUXNET GPP than total APAR-based GPP.
High Accuracy Motion Detection Algorithm via ISM Band FMCW Radar
Authors: Qu Kui , Zhang Rongfu , Fang Zhijun
Published: December 2021

The conventional frequency modulated continuous wave (FMCW) radar accuracy range detection algorithm is based on the frequency estimation and additional phase evaluation which contains Fourier transform and frequency refining analysis in each chirp, so it has the disadvantages of being computationally expensive, and not being suitable for real-time motion measurement. In addition, if there are other objects near the target, the spectra of the clutter and the target will be adjacent and affect each other, making it more challenging to estimate the frequency of the target. In this paper, the analytical expression of the Fourier transform of the beat signal is presented and it can be seen that spectrum leakage makes the phase of Fourier transform no longer consistent with the real phase of signal. The change regularities of real and imaginary parts of Fourier transform are studied, and the corrected phase of ellipse approximation is given in the industrial, scientific, and medical (ISM) band. Accurate displacement can be obtained by accurate phase. The algorithm can filter the direct current (DC) offset which is mainly caused by stationary objects. The performance of the algorithm is evaluated by a radar system whose center frequency is 24.075 GHz and the bandwidth is 0.15 GHz; the measurement accuracy of displacement is 0.087 mm and the accuracy of distance is 0.043 m.
Comprehensive Assessment of Performances of Long Time-Series LAI, FVC and GPP Products over Mountainous Areas: A Case Study in the Three-River Source Region, China
Authors: Zhang Wenqi , Jin Huaan , Li Ainong , Shao Huaiyong , Xie Xinyao , Lei Guangbin , Nan Xi , Hu Guyue , Fan Wenjie
Published: December 2021

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support.
COVID-19 Influence on Developments in the Global Beef and Sheep Sectors
Authors: Almadani Mohamad Isam , Weeks Peter , Deblitz Claus
Published: December 2021

While the COVID-19 pandemic and associated government responses have had a substantial impact on consumers and meat supply chains worldwide, the effect on beef and sheep farming has been surprisingly small, short-lived and largely offset by other global influences. However, the impact has also varied greatly between countries and regions, largely due to differences in Government measures and in industry circumstances and influences. This study aims to provide insights into the pandemic’s impacts throughout global beef and sheep supply chains, but with a focus on the farm level, particularly producer prices in 2020. At the centre of the study is an analysis of online questionnaire-based survey responses to the Global agri benchmark Beef and Sheep Network. The study also utilizes a variety of other studies and information sources to explore other potential factors that could have also driven beef and sheep sectors worldwide in 2020. It explores how these influences interacted with the effect of the pandemic. Food service sales were highly impacted by the pandemic, meat processing was temporarily disrupted in North America but global livestock prices remained high due, in large part, to the continuation of strong beef and sheep meat demand and imports in China.
Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition
Authors: Stoimchev Marjan , Ivanovska Marija , Štruc Vitomir
Published: December 2021

In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.
A Review of Data Gathering Methods for Evaluating Socially Assistive Systems
Authors: Qiu Shi , An Pengcheng , Kang Kai , Hu Jun , Han Ting , Rauterberg Matthias
Published: December 2021

Social interactions significantly impact the quality of life for people with special needs (e.g., older adults with dementia and children with autism). They may suffer loneliness and social isolation more often than people without disabilities. There is a growing demand for technologies to satisfy the social needs of such user groups. However, evaluating these systems can be challenging due to the extra difficulty of gathering data from people with special needs (e.g., communication barriers involving older adults with dementia and children with autism). Thus, in this systematic review, we focus on studying data gathering methods for evaluating socially assistive systems (SAS). Six academic databases (i.e., Scopus, Web of Science, ACM, Science Direct, PubMed, and IEEE Xplore) were searched, covering articles published from January 2000 to July 2021. A total of 65 articles met the inclusion criteria for this systematic review. The results showed that existing SASs most often targeted people with visual impairments, older adults, and children with autism. For instance, a common type of SASs aimed to help blind people perceive social signals (e.g., facial expressions). SASs were most commonly assessed with interviews, questionnaires, and observation data. Around half of the interview studies only involved target users, while the other half also included secondary users or stakeholders. Questionnaires were mostly used with older adults and people with visual impairments to measure their social interaction, emotional state, and system usability. A great majority of observational studies were carried out with users in special age groups, especially older adults and children with autism. We thereby contribute an overview of how different data gathering methods were used with various target users of SASs. Relevant insights are extracted to inform future development and research.
VLC Network Design for High Mobility Users in Urban Tunnels
Authors: Torres-Zapata Edmundo , Guerra Victor , Rabadan Jose , Luna-Rivera Martin , Perez-Jimenez Rafael
Published: December 2021

Current vehicular systems require real-time information to keep drivers safer and more secure on the road. In addition to the radio frequency (RF) based communication technologies, Visible Light Communication (VLC) has emerged as a complementary way to enable wireless access in intelligent transportation systems (ITS) with a simple design and low-cost deployment. However, integrating VLC in vehicular networks poses some fundamental challenges. In particular, the limited coverage range of the VLC access points and the high speed of vehicles create time-limited links that the existing handover procedures of VLC networks can not be accomplished timely. Therefore, this paper addresses the problem of designing a vehicular VLC network that supports high mobility users. We first modify the traditional VLC network topology to increase uplink reliability. Then, a low-latency handover scheme is proposed to enable mobility in a VLC network. Furthermore, we validate the functionality of the proposed VLC network design method by using system-level simulations of a vehicular tunnel scenario. The analysis and the results show that the proposed method provides a steady connection, where the vehicular node is available more than 99% of the time regardless of the number of vehicular nodes on this network. Additionally, the system is able to achieve a Frame-Error-Rate (FER) performance lower than 10−3.
Simultaneous Determination of 16 Kinds of Synthetic Cathinones in Human Urine Using a Magnetic Nanoparticle Solid-Phase Extraction Combined with Gas Chromatography-Mass Spectrometry
Authors: Wang Dan , Chen Xueguo , Ming Zhu , Jiang Limin , Zhou Yan
Published: December 2021

A specific and sensitive approach using magnetic nanoparticle solid-phase extraction combined with gas chromatography-mass spectrometry (GC-MS) was carried out and applied in the simultaneous determination of 16 kinds of synthetic cathinones in human urine. The functionalized extraction material (Fe3O4/NH2-MWCNTs) was synthesized and factors affecting the extraction efficiency were all tested. Under the optimized conditions of magnetic nanoparticle solid-phase extraction, the determination of synthetic cathinones in human urine was carried out with GC-MS. Good linear relationships of 16 kinds of synthetic cathinones were obtained in the range of 0.005–5.00 μg/mL with the correlation coefficients (r) ranging from 0.9901 to 0.9979, the limits of detection were in the range between 0.005 and 0.01 μg/mL, and the limits of quantitation were between 0.01 and 0.02 μg/mL. Furthermore, the average intra-day precisions were below 8.90%, the average inter-day precisions were less than 9.52%, and the average recoveries were between 87.03% and 99.13%, respectively. The results show the advantages of the approach in the determination of trace synthetic cathinones in complex matrixes, such as environmentally friendly, fast detection, high efficiency and sensitivity. The practical application indicated that this method could provide scientific basis for the determination of drugs of abuse in forensic laboratories.
Spectral Clustering of Mixed-Type Data
Authors: Mbuga Felix , Tortora Cristina
Published: December 2021

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.
Precision Agroecology
Authors: Duff Hannah , Hegedus Paul , Loewen Sasha , Bass Thomas , Maxwell Bruce
Published: December 2021

In response to global calls for sustainable food production, we identify two diverging paradigms to address the future of agriculture. We explore the possibility of uniting these two seemingly diverging paradigms of production-oriented and ecologically oriented agriculture in the form of precision agroecology. Merging precision agriculture technology and agroecological principles offers a unique array of solutions driven by data collection, experimentation, and decision support tools. We show how the synthesis of precision technology and agroecological principles results in a new agriculture that can be transformative by (1) reducing inputs with optimized prescriptions, (2) substituting sustainable inputs by using site-specific variable rate technology, (3) incorporating beneficial biodiversity into agroecosystems with precision conservation technology, (4) reconnecting producers and consumers through value-based food chains, and (5) building a just and equitable global food system informed by data-driven food policy. As a result, precision agroecology provides a unique opportunity to synthesize traditional knowledge and novel technology to transform food systems. In doing so, precision agroecology can offer solutions to agriculture’s biggest challenges in achieving sustainability in a major state of global change.
Developing (Transformative) Environmental and Sustainability Education in Classroom Practice
Authors: Walshe Nicola , Sund Louise
Published: December 2021

We are writing this editorial in the weeks preceding COP26; as we do, wall-to-wall coverage of events and speculation of what might (or might not) be achieved across mainstream and social media channels suggest that awareness of the impacts of climate change on planetary sustainability and human and nonhuman forms of life has never been higher [...]
Utility of Human Footprint Pressure Mapping for Large Carnivore Conservation: The Kafue-Zambezi Interface
Authors: Lines Robin , Bormpoudakis Dimitrios , Xofis Panteleimon , MacMillan Douglas C. , Pieterse Lucy , Tzanopoulos Joseph
Published: December 2021

Proxies and indicators to monitor cumulative human pressures provide useful tools to model change and understanding threshold pressures at which species can persist, are extirpated, or might recolonize human-impacted landscapes. We integrated modelling and field observations of human pressure variables to generate a site-specific, fine scale Human Footprint Pressure map for 39,000 km2 of rangelands at the Kafue–Zambezi interface—a key linkage in the Kavango-Zambezi Transfrontier Conservation Area. We then modelled Human Footprint Pressure against empirically derived occurrence data for lion (Panthera leo), leopard (Panthera pardus), and spotted hyena (Crocuta crocuta) to generate Human Footprint Pressure threshold ranges at which each species were persisting or extirpated within ten wildlife managed areas linking Kafue National Park to the Zambezi River. Results overcame many limitations inherent in existing large-scale Human Footprint Pressure models, providing encouraging direction for this approach. Human Footprint Pressure thresholds were broadly similar to existing studies, indicating this approach is valid for site- and species-specific modelling. Model performance would improve as additional datasets become available and with improved understanding of how asymmetrical and nonlinear threshold responses to footprint pressure change across spatial-temporal scales. However, our approach has broader utility for local and region-wide conservation planning where mapping and managing human disturbance will help in managing carnivore species within and without protected area networks.
Estimation of Traffic Delay Due to U-Turns at Uncontrolled Medians: Case Study in Phnom Penh, Cambodia
Authors: Omarov Maksat , Ismail Syuhaida , Rani Wan Nurul Mardiah Wan Mohd , Durdyev Serdar
Published: December 2021

Lack of infrastructure capacity to the demand in mobility results in delays in traffic. U-turns restrict the movement of the opposing through-traffic during the maneuver, hence triggering delays. Significant delays can be observed, especially in high volume traffic, where U-turning is not restricted. Hence, it is imperative to study the delays incurred by the U-turning vehicles to assess their impacts on traffic congestion. This study thus proposes a quantitative method to estimate the aggregate and average delay per vehicle caused by U-turns at uncontrolled medians of urban roads. Traffic data were collected at a corridor where vehicles were allowed to U-turn on a four-lane divided mixed traffic urban road in Phnom Penh, Cambodia. The delay in the opposing lane of traffic was calculated by analyzing the data using a novel analytical method; a derivative of the Webster’s delay model. The results reveal that the suggested technique evaluates the caused delay with good accuracy. Excessive traffic delay and about three seconds delay per vehicle in the through-traffic of the opposite lane align well with the experienced delay at the site. The method can be used to assess the delay at straight corridor sections where U-turns are not restricted, understand the causes of the delay, and develop control measures for improved traffic performance.
The Renewal of Arts, Lives, and a Community through Social Enterprise: The Case of Oficina de Agosto
Authors: Walther Luciana , da Costa Carlos Eduardo Félix
Published: December 2021

The present work investigates, with a cultural approach, the emergence of an art-based social enterprise and an art-entrepreneurial ecosystem in Southeastern semi-rural Brazil, shedding light on how local private initiatives may build stronger communities and vice-versa, in a mutually transformative relationship. The focus lies on Oficina de Agosto, a folk-art studio, school, and shop. Fieldwork design combined ethnography and art-based research. The thick description of the phenomenon is organized under the acronym P.L.A.C.E., a conceptual framework describing five principles of community development. The contributions of this study are three-fold: (1) it illustrates how social enterprise may work as an alternative market model that could support community building; (2) it raises awareness to the possibility that social enterprises’ initial social focus may not be perennial or unshakeable, in an undesirable change that might require a both/and mindset and a patient management of paradoxes; and (3) it offers practical managerial recommendations to the SE under focus, which might be extended to other local businesses, or to SEs in other semi-rural Brazilian towns, or even in international settings that might bear economic and social resemblance to our researched context.
System Modeling and Reliability Assessment of Microgrids: A Review
Authors: Nazir Masood Ibni , Hussain Ikhlaq , Ahmad Aijaz , Khan Irfan , Mallik Ayan
Published: December 2021

The world today is plagued with problems of increased transmission and distribution (T&D) losses leading to poor reliability due to power outages and an increase in the expenditure on electrical infrastructure. To address these concerns, technology has evolved to enable the integration of renewable energy sources (RESs) like solar, wind, diesel and biomass energy into small scale self-governing power system zones which are known as micro-grids (MGs). A de-centralised approach for modern power grid systems has led to an increased focus on distributed energy resources and demand response. MGs act as complete power system units albeit on a small scale. However, this does not prevent them from large operational sophistication allowing their independent functioning in both grid-connected and stand-alone modes. MGs provide greater reliability as compared to the entire system owing to the large amount of information secured from the bulk system. They comprise numerous sources like solar, wind, diesel along with storage devices and converters. Several modeling schemes have been devised to reduce the handling burden of large scale systems. This paper gives a detailed review of MGs and their architecture, state space representation of wind energy conversion systems & solar photovoltaic (PV) systems, operating modes and power management in a MG and its impact on a distribution network.
Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea
Authors: Kyeong Sunghyon , Kim Daehee , Shin Jinho
Published: December 2021

The credit scoring model is one of the most important decision-making tools for the sustainability of banking systems. This study is the first to examine whether it can be improved by using system log data that are stoed extensively for system operation. We used the log data recorded by the mobile application system of KakaoBank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from KakaoBank’s log data, we created a credit scoring model by utilizing variables with high information values and logistic regression, the most common method for developing credit scoring models in financial institutions. To prove our hypothesis on the improvement of credit scoring model performance, we performed an independent sample t-test using the simulation results of repeated model development and performance measurement based on randomly sampled data. Consequently, the discrimination power of the proposed model using logistic regression (neural network) compared to the credit bureau-based model significantly improved by 1.84 (2.22) percentage points based on the Kolmogorov–Smirnov statistics. The results of this study suggest that a bank can utilize the accumulated log data inside the bank to improve decision-making systems, including credit scoring, at a low cost.
Appropriate Technology for Access to Universal Basic Services: A Case Study on Basic Electricity Service Provision to Remote Communities in the Napo River Basin
Authors: Del-Río-Carazo Laura , Iglesias-Pradas Santiago , Acquila-Natale Emiliano , Martín-Fernández José Gabriel
Published: December 2021

Appropriate technologies (ATs) refer to technologies that are controlled by the communities that benefit from their implementation. Technologies have become a fundamental element in projects aiming to solve problems related to poverty and access to basic needs in some areas in emerging countries, and appropriation of these technologies is necessary to ensure effective transfer of knowledge and sustainability. However, due to the lack of consensus on the definition of ATs, there is a need to clarify and define the scope and boundaries of the term to facilitate the implementation of technology in projects in developing countries. This study addresses this gap by means of a scoping review, which presents a detailed analysis of 17 journal articles (from an initial selection of 95 articles) and provides a comprehensive definition of ATs. To guide AT-oriented interventions in technology-intensive cooperation projects, this study proposes an Action Framework based on that definition of ATs. The Action Framework provides guidance on how to implement technology in these projects to ensure that the technology is appropriated. To facilitate the understanding of the Action Framework, the study showcases its application in a real project of rural electrification in the Peruvian Amazon Rainforest.
Organic Cocoa Value Chain Sustainability: The Perception of São Tomé and Príncipe’s Stakeholders
Authors: Prazeres Ibrahim , Lucas Maria Raquel , Marta-Costa Ana
Published: December 2021

Cocoa farming in São Tomé and Príncipe (STP) faces several challenges due to its poor socioeconomic context, the adverse impact of climate changes, the complex and limited access to global value chains, and worldwide demand pressure for higher cocoa quality and productivity. This exploratory research investigates potential pathways to a more sustainable organic cocoa (OC) production in STP by mapping the perceptions of the stakeholders involved in its value chain. Qualitative interviews, field observation, and focus group discussions were applied to understand how sustainability dimensions, drivers, impacts, and challenges of OC are perceived and how these three dimensions can be improved and balanced. The gathered perceptions are rather diverse, reflecting the stakeholders’ position and knowledge of the specific contexts and processes. Producers do not perceive how governance is adopting new organizational structures or practices that allow for an effective sustainability improvement. Most stakeholders recognize that market-related factors drive the sustainability adoption and that financing-related constraints challenge their wide implementation. There are trade-offs and power asymmetries in the OC value chain, which manifest differently, due to the governance approaches, processes, overall regulations, and training of producers. An alignment of perceptions and activities as well as a stronger cooperation between cooperatives, private firms, and public institutions is strongly recommended.
Assessing the Impact of COVID-19 on Sustainable Food Supply Chains
Authors: Kazancoglu Yigit , Ozbiltekin-Pala Melisa , Sezer Muruvvet Deniz , Ekren Banu Y. , Kumar Vikas
Published: December 2021

Recently, it has become an important issue to ensure sustainability, especially in food supply chains, against the rapidly growing population, increasing demand, and sudden disruptions caused by uncertain times such as that caused by COVID-19. Since food supply chains has vulnerable products and processes, it is critical to understand the sustainability factors of food supply chains especially in uncertain times such during the COVID-19 pandemic. This study aims to determine sustainability factors of food supply chains. An Interpretive Structural Modelling method is used to state the relations between sustainability factors of food supply chains. As a result of the study, Information Sharing and Managerial Approaches are classified as driving factors; Food Safety and Security, Know-How Transfer, Logistics Networking, Risk Mitigation, Employee Commitment, Innovation, Traceability and Responsiveness are categorized as linkage factors. This article will be beneficial for managers in helping them develop sustainable food supply chains during uncertain times by focusing on traceability, information sharing, know-how transfer, food safety and security.
Exploring the Fiscal Responsibility through Active Citizenship for Governance Quality
Authors: Cigu Elena , Bercu Ana-Maria , Tofan Mihaela , Tiță Silviu
Published: December 2021

Active citizenship emphasizes the importance of understanding its added value to fiscal responsibility and governance quality for economic and sustainable growth. Firstly, the paper aims to review issues in the literature pertinent to the debate on the role and the characterization of active citizenship. Secondly, using a linear regression model, we provide new evidence on the relationship between active citizenship, on the one hand, and fiscal responsibility, government quality, and economic growth, on the other hand, for the 27 European Union countries, over the period from 2006 to 2019. Our results highlight that active citizenship determines a more responsible and efficient activity of government based on fiscal responsibility, contributing to economic growth and sustainable development.
Challenges in Land Use and Transport Planning Integration in Helsinki Metropolitan Region—A Historical-Institutional Perspective
Authors: Duman Oya , Mäntysalo Raine , Granqvist Kaisa , Johnson Emily , Ronikonmäki Niko-Matti
Published: December 2021

Land use and transport integration has been considered a must-have approach in achieving sustainable urban development. However, successful applications of the concept have been few, as institutional reforms to support land use and transport integration have lagged behind. Accordingly, this article argues that understanding difficulties in land use and transport integration requires an analysis of the long-term evolution of formal and informal institutional frameworks in planning practices. For this purpose, this article presents a case study of land use and transport planning in Finland’s Helsinki Metropolitan Region, which combines interview research on planners’ perceptions with a document analysis of the historical trajectories of the region’s plans, policy documents and related institutional and organizational changes. The historical-institutional approach of the article draws on discursive institutionalism as a novel analytical approach for studying how land use and transport integration is institutionally conditioned.
A Review of Groundwater Management Models with a Focus on IoT-Based Systems
Authors: Aderemi Banjo Ayoade , Olwal Thomas Otieno , Ndambuki Julius Musyoka , Rwanga Sophia Sudi
Published: December 2021

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.
Exploring the Application of Lean Best Practices in Remanufacturing: Empirical Insights into the Benefits and Barriers
Authors: Pawlik Elzbieta , Ijomah Winifred , Corney Jonathan , Powell Daryl
Published: December 2021

Remanufacturing processes have been widely identified as being important elements of a sustainable economy. However, the commercial viability of this product recovery route is significantly influenced by several operational challenges that are different from those that occur in conventional manufacturing. One of the most widely used systematic process improvement methods in conventional manufacturing is lean production, which seeks to minimize all forms of waste throughout an operation. As the academic study of lean best practices in remanufacturing processes is relatively new, there is a lack of knowledge regarding implementation and impact. As such, this paper presents multiple case studies investigating the application of lean methods in industrial remanufacturing operations. These studies focus on the automotive industry, where both conventional and remanufacturing operations have been the leading adopters of lean thinking and practice. The results of the investigation identify specific lean methods that help manage the inherent complexity of the remanufacturing processes, and consequently improve the overall productivity of the process. Similarly, factors that limit the application of lean practices within remanufacturing are also identified and discussed. Matrices for opportunities and threats are developed to enable the simple adoption of these findings. Overall, the research provides a more holistic understanding of the application of lean within a remanufacturing environment, with benefits for both academia and industry.
Has the Digital Economy Changed the Urban Network Structure in China?—Based on the Analysis of China’s Top 500 New Economy Enterprises in 2020
Authors: Chen Bo , Zhu Huasheng
Published: December 2021

The rapid development of the new generation of information technology makes digital enterprises and the digital economy important forces in promoting the sustainable growth of the world economy. Under the influence of the digital economy, the original urban network may undergo drastic changes. There have been studies that have arrived at conflicting conclusions. This paper primarily illustrates whether or not the digital economy has changed the urban network structure. China’s digital economy is developing rapidly, becoming a new engine for the high-quality development of the Chinese economy. Therefore, this paper demonstrates the impact of China’s digital economy on the urban network structure by using data from China’s Top 500 New Economy Enterprises in 2020 and the headquarter–subsidiary ownership method. The results show that (1) China’s urban network has changed significantly. Compared with APS enterprises and listed companies, the urban network of the digital economy has become more polarized, and Beijing has become the absolute control center. (2) Chinese cities have been reshuffled in the era of the digital economy. Beijing, Hangzhou, and Chengdu, with their industrial foundations in the digital economy, have performed better within the network. Simultaneously, some heavily industrialized cities, such as Wuhan, Shenyang, and Chongqing, have been declining due to the difficulties associated with transformation. (3) Although the digital economy has reshaped China’s urban network structure to a certain extent, the original urban pattern still plays a dominant role in the new system. The network spatial pattern of dense east and sparse west still exists, and provincial capitals and subprovincial cities still play a more significant role in the network than ordinary cities. (4) Network diffusion is typically a hierarchical diffusion between core nodes. Geographical proximity has a low constraint on network diffusion, and subsidiaries expand outward through hierarchical diffusion.
Investigating the Relationships between Renewable Energy Consumption, Socio-Economic Factors and Health: A PVAR Analysis from MENA Net Oil Importing Countries
Authors: Kadria Mohamed , Farhani Sahbi , Guirat Yosr
Published: December 2021

In this paper, we tried to contribute to the previous literature by analyzing the relationship between renewable energy consumption, socio-economic factors and health in the presence of a stringent environmental policy and lobbying power. Using a Panel Vector Auto-Regressive (PVAR) technique, we specifically examine the role of the government effectiveness and the lobbying pressure in moderating the impact of renewable energy consumption on CO2 emissions, economic growth and health factor considering the case of Middle East and North Africa (MENA) Net Oil Importing Countries (NOICs) from 1996 to 2019. Our analysis shows that (i) environmental policy stringency and good governance will induce a rise in the level of renewable energy consumption; (ii) lobbying power and interest groups discourage the renewable energy sector’s development since the add in economic growth of these economies is not oriented towards renewable energy projects; (iii) a rise in renewable energy consumption, perhaps generated by renewable energy policies, should favor the improvement of public health. Finally, the political implications of the findings are summarized and discussed.
Development, Application and Challenges of Set Pair Analysis in Environmental Science from 1989 to 2020: A Bibliometric Review
Authors: Xiang Weiqi , Yang Xiaohua , Babuna Pius , Bian Dehui
Published: December 2021

Set pair analysis is a new intelligent algorithm for dealing with complex uncertain problems, and it is widely used in environmental science because of its concise structure and scalability of results. However, it is still unclear about the development stage distribution of set pair analysis in environmental science and the specific development and application in key areas. Therefore, based on the method of bibliometrics, this paper studies the development, application and challenges of set pair analysis in environmental science over the past 32 years (1989–2020). The analysis found that in terms of time dimension, the development process of set pair analysis is divided into three stages: the initial stage (1989–2011); the rapid development stage (2012–2015); the steady development stage (2016 to present). In terms of specific fields, this article focuses on the development and application of set pair analysis in the three fields of ecology, water resources, and atmospheric environmental science. It is found that set pair analysis is mainly used for environmental assessment, diagnosis and prediction. In particular, the development of partial connection numbers is a new research trend of set pair analysis, which plays an important role in environmental assessment, diagnosis and prediction. However, the current set pair analysis also has the shortcomings of strong subjectivity, an imperfect theoretical system, and unbalanced development at home and abroad. Only when these deficiencies are solved, can the development of set pair analysis in environmental science be further promoted.
Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion
Authors: Ma Jianpeng , Li Chengwei , Zhang Guangzhu
Published: December 2021

The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.
Recurrent Generalization of F-Polynomials for Virtual Knots and Links
Authors: Gill Amrendra , Ivanov Maxim , Prabhakar Madeti , Vesnin Andrei
Published: December 2021

F-polynomials for virtual knots were defined by Kaur, Prabhakar and Vesnin in 2018 using flat virtual knot invariants. These polynomials naturally generalize Kauffman’s affine index polynomial and use smoothing in the classical crossing of a virtual knot diagram. In this paper, we introduce weight functions for ordered orientable virtual and flat virtual links. A flat virtual link is an equivalence class of virtual links with respect to a local symmetry changing a type of classical crossing in a diagram. By considering three types of smoothing in classical crossings of a virtual link diagram and suitable weight functions, there is provided a recurrent construction for new invariants. It is demonstrated by explicit examples that newly defined polynomial invariants are stronger than F-polynomials.
Solving the Max-Diversity Orthogonal Regrouping Problem by an Integer Linear Programming Model and a GRASP/VND with Path-Relinking Approach
Authors: Canale Eduardo , Robledo Franco , Sartor Pablo , Stábile Luis
Published: December 2021

Students from Master of Business Administration (MBA) programs are usually split into teams. In light of the generalistic nature of MBA programs, diversity within every team is desirable in terms of gender, major, age and other criteria. Many schools rotate the teams at the beginning of every term so that each student works with a different set of peers during every term, thus training his or her adaptation skills and expanding the peer network. Achieving diverse teams while avoiding–or minimizing—the repetition of student pairs is a complex and time-consuming task for MBA Directors. We introduce the Max-Diversity Orthogonal Regrouping (MDOR) problem to manage the challenge of splitting a group of people into teams several times, pursuing the goals of high diversity and few repetitions. We propose a hybrid Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Descent (GRASP/VND) heuristic combined with tabu search and path relinking for its resolution, as well as an Integer Linear Programming (ILP) formulation. We compare both approaches through a set of real MBA cohorts, and the results show that, in all cases, the heuristic approach significantly outperforms the ILP and manually formed teams in terms of both diversity and repetition levels.
Correction: Garcia et al. Primary Model for Biomass Growth Prediction in Batch Fermentation. Symmetry 2021, 13, 1468
Authors: Garcia Blanca E. , Rodriguez Emmanuel , Salazar Yolocuauhtli , Valle Paul A. , Flores-Gallegos Adriana C. , Rutiaga-Quiñones O. Miriam , Rodriguez-Herrera Raul
Published: December 2021

The authors wish to make the following corrections to this paper [...]