bluuebunny/crossref_metadata_2025 huggingface dataset:
An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning
Authors: Poghosyan Arnak , Harutyunyan Ashot , Grigoryan Naira , Pang Clement , Oganesyan George , Ghazaryan Sirak , Hovhannisyan Narek
Published: February 2021

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.
Adapted Fringe Projection Sequences for Changing Illumination Conditions on the Example of Measuring a Wrought-Hot Object Influenced by Forced Cooling
Authors: Quentin Lorenz , Beermann Rüdiger , Reinke Carl , Kern Pascal , Kästner Markus , Reithmeier Eduard
Published: February 2021

Optical 3D geometry reconstruction, or more specific, fringe projection profilometry, is a state-of-the-art technique for the measurement of the shape of objects in confined spaces or under rough environmental conditions, e.g., while inspecting a wrought-hot specimen after a forging operation. While the contact-less method enables the measurement of such an object, the results are influenced by the light deflection effect occurring due to the inhomogeneous refractive index field induced by the hot air around the measurand. However, the developed active compensation methods to fight this issue exhibits a major drawback, namely an additional cooling of the object and a subsequent transient illumination component. In this paper, we investigate the cooling and its effect on temporal phase reconstruction algorithms and take a theoretical approach to its compensation. The simulated compensation measures are transferred to a fringe projection profilometry setup and are evaluated using established and newly developed methods. The results show a significant improvement when measuring specimens under a transient illumination and are easily transferable to any kind of multi-frequency phase-shift measurement.
Time Distribution of Strong Seismic Events in the Fore-Sudetic Monocline in Context of Signals Registered by Water-Tube Gauges in Książ Geodynamic Laboratory
Authors: Kaczorowski Marek , Kasza Damian , Zdunek Ryszard , Wronowski Roman
Published: February 2021

Changes in the stress field in Świebodzice Depression (ŚU) unit area are the reason of complex kinematics of the rock blocks consisting of rotations and horizontal/vertical displacements. The measurement system of the Geodynamic Laboratory in Książ, associated with rock blocks which are separated by faults, is a natural detector of tectonic activity. Installed in laboratory long water-tube gauges allowing to determine the functions of tectonic activity—TAF, and their derivatives. A comparison of the TAF with the seismic activity of the Fore-Sudetic Monocline showed that the strong seismic shocks (magnitude ≥3.6) occur in the Monocline only during defined and repeatable phases of the kinematic activity of the ŚU. Observed concordance proves the thesis of the existence of a large-scale, and largely homogeneous field of tectonic forces which, at the same time, cover the ŚU and the Fore-Sudetic Monocline units. The results of comparison between seismic events temporal distribution and phases of tectonic activity of the ŚU orogen indicate existence of the time relation between function of derivative of the tectonic activity (TAF) and seismic events.
Detection Line Spectrum of Ship Radiated Noise Based on a New 3D Chaotic System
Authors: Hu Gang , Wang Kejun , Liu Liangliang
Published: February 2021

This paper proposes a new detection model of a weak signal based on a third-order chaotic system. Using a dynamic analysis tool, such as the Lyapunov exponent and the bifurcation diagram, variations of dynamic behavior can be observed, and the weak signal underwater can be picked up. In order to improve the observability of detection signals in the time domain and frequency domain, the spectral entropy complexity algorithm (SE) and C0 complexity algorithms are used to analyze and extract the weak signal. The experimental results show that the spectrum extraction based on the complexity algorithm can accurately reflect the dynamic characteristics of the detected signal. It provides the theoretical direction and experimental data support for the application of the chaotic system in the field of acoustic detection.
Chalcogenide Glass-Capped Fiber-Optic Sensor for Real-Time Temperature Monitoring in Extreme Environments
Authors: Badamchi Bahareh , Simon Al-Amin Ahmed , Mitkova Maria , Subbaraman Harish
Published: February 2021

We demonstrate a novel chalcogenide glass (ChG)-capped optical fiber temperature sensor capable of operating within harsh environment. The sensor architecture utilizes the heat-induced phase change (amorphous-to-crystalline) property of ChGs, which rapidly (80–100 ns) changes the optical properties of the material. The sensor response to temperature variation around the phase change of the ChG cap at the tip of the fiber provides abrupt changes in the reflected power intensity. This temperature is indicative of the temperature at the sensing node. We present the sensing performance of six different compositions of ChGs and a method to interpret the temperature profile between 440 °C and 600 °C in real-time using an array structure. The unique radiation-hardness property of ChGs makes the devices compatible with high-temperature and high-radiation environments, such as monitoring the cladding temperature of Light Water (LWR) or Sodium-cooled Fast (SFR) reactors.
IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories
Authors: Martini Bruno Guilherme , Helfer Gilson Augusto , Barbosa Jorge Luis Victória , Espinosa Modolo Regina Célia , da Silva Marcio Rosa , de Figueiredo Rodrigo Marques , Mendes André Sales , Silva Luís Augusto , Leithardt Valderi Reis Quietinho
Published: February 2021

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.
LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: February 2021

Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of intelligent and smart environments and systems that cover all aspects of human life including healthcare, sports, manufacturing, commerce, etc. Such environments and systems necessitate and subsume activity recognition, aimed at recognizing the actions, characteristics, and goals of one or more individuals from a temporal series of observations streamed from one or more sensors. Due to the reliance of conventional Machine Learning (ML) techniques on handcrafted features in the extraction process, current research suggests that deep-learning approaches are more applicable to automated feature extraction from raw sensor data. In this work, the generic HAR framework for smartphone sensor data is proposed, based on Long Short-Term Memory (LSTM) networks for time-series domains. Four baseline LSTM networks are comparatively studied to analyze the impact of using different kinds of smartphone sensor data. In addition, a hybrid LSTM network called 4-layer CNN-LSTM is proposed to improve recognition performance. The HAR method is evaluated on a public smartphone-based dataset of UCI-HAR through various combinations of sample generation processes (OW and NOW) and validation protocols (10-fold and LOSO cross validation). Moreover, Bayesian optimization techniques are used in this study since they are advantageous for tuning the hyperparameters of each LSTM network. The experimental results indicate that the proposed 4-layer CNN-LSTM network performs well in activity recognition, enhancing the average accuracy by up to 2.24% compared to prior state-of-the-art approaches.
Towards Secure Fitness Framework Based on IoT-Enabled Blockchain Network Integrated with Machine Learning Algorithms
Authors: Jamil Faisal , Kahng Hyun Kook , Kim Suyeon , Kim Do-Hyeun
Published: February 2021

Blockchain technology has recently inspired remarkable attention due to its unique features, such as privacy, accountability, immutability, and anonymity, to name of the few. In contrast, core functionalities of most Internet of Things (IoT) resources make them vulnerable to security threats. The IoT devices, such as smartphones and tablets, have limited capacity in terms of network, computing, and storage, which make them easier for vulnerable threats. Furthermore, a massive amount of data produced by the IoT devices, which is still an open challenge for the existing platforms to process, analyze, and unearth underlying patterns to provide convenience environment. Therefore, a new solution is required to ensure data accountability, improve data privacy and accessibility, and extract hidden patterns and useful knowledge to provide adequate services. In this paper, we present a secure fitness framework that is based on an IoT-enabled blockchain network integrated with machine learning approaches. The proposed framework consists of two modules: a blockchain-based IoT network to provide security and integrity to sensing data as well as an enhanced smart contract enabled relationship and inference engine to discover hidden insights and useful knowledge from IoT and user device network data. The enhanced smart contract aims to support users with a practical application that provides real-time monitoring, control, easy access, and immutable logs of multiple devices that are deployed in several domains. The inference engine module aims to unearth underlying patterns and useful knowledge from IoT environment data, which helps in effective decision making to provide convenient services. For experimental analysis, we implement an intelligent fitness service that is based on an enhanced smart contract enabled relationship and inference engine as a case study where several IoT fitness devices are used to securely acquire user personalized fitness data. Furthermore, a real-time inference engine investigates user personalized data to discover useful knowledge and hidden insights. Based on inference engine knowledge, a recommendation model is developed to recommend a daily and monthly diet, as well as a workout plan for better and improved body shape. The recommendation model aims to facilitate a trainer formulating effective future decisions of trainee’s health in terms of a diet and workout plan. Lastly, for performance analysis, we have used Hyperledger Caliper to access the system performance in terms of latency, throughput, resource utilization, and varying orderer and peers nodes. The analysis results imply that the design architecture is applicable for resource-constrained IoT blockchain platform and it is extensible for different IoT scenarios.
Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion
Authors: Liu Ming , Chen Shichao , Lu Fugang , Xing Mengdao , Wei Jingbiao
Published: February 2021

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.
Identifying Technology Opportunities for Electric Motors of Railway Vehicles with Patent Analysis
Authors: Cho Yunkoo , Han Young Jae , Hwang Jumi , Yu Jiwon , Kim Sangbaek , Lee Chulung , Lee Sugil , Yi Kyung Pyo
Published: February 2021

An electric motor is a device that changes electrical energy into mechanical energy for railway vehicles. When developing the electric motor, it used to be developed simply for structures or control methods of the motor itself without considering convergence with other devices or technologies. However, as the railway vehicles become more advanced, technology development through convergence with other devices or technologies is spreading. Therefore, based on patent data related to the electric motors applied to the railway vehicles, this research aims to carry out technical forecasting for establishing research and development (R and D) direction for new technologies by predicting vacant technologies from the point of view of technology convergence. In other words, we studied how to find the vacant technologies in a field of convergence technology for the electric motor of the railway vehicles by analyzing the patent data. More specifically, we search the patents data associated with the electric motor of the railway vehicle that contain multiple IPC codes, and use multiple IPC codes to determine the field of convergence technology. In addition, we extract keywords from the patents data related to each of the determined convergence technologies and define the vacant technologies by interpreting the field of convergence technology and the extracted keywords.
Advancing Sustainable Development and Protected Area Management with Social Media-Based Tourism Data
Authors: Arkema Katie K. , Fisher David M. , Wyatt Katherine , Wood Spencer A. , Payne Hanna J.
Published: February 2021

Sustainable tourism involves increasingly attracting visitors while preserving the natural capital of a destination for future generations. To foster tourism while protecting sensitive environments, coastal managers, tourism operators, and other decision-makers benefit from information about where tourists go and which aspects of the natural and built environment draw them to particular locations. Yet this information is often lacking at management-relevant scales and in remote places. We tested and applied methods using social media as data on tourism in The Bahamas. We found that visitation, as measured by numbers of geolocated photographs, is well correlated with counts of visitors from entrance surveys for islands and parks. Using this relationship, we predicted nearly 4 K visitor-days to the network of Bahamian marine protected areas annually, with visitation varying more than 20-fold between the most and least visited parks. Next, to understand spatial patterns of tourism for sustainable development, we combined social media-based data with entrance surveys for Andros, the largest island in The Bahamas. We estimated that tourists spend 125 K visitor-nights and more than US$45 M in the most highly visited district, five times that of the least visited district. We also found that tourists prefer accessible, natural landscapes—such as reefs near lodges—that can be reached by air, roads, and ferries. The results of our study are being used to inform development and conservation decisions, such as where to invest in infrastructure for visitor access and accommodation, siting new marine protected areas, and management of established protected areas. Our work provides an important example of how to leverage social media as a source of data to inform strategies that encourage tourism, while conserving the environments that draw visitors to a destination in the first place.
A Scientometric Analysis of Worldwide Intercropping Research Based on Web of Science Database between 1992 and 2020
Authors: Lv Wen , Zhao Xining , Wu Pute , Lv Jialong , He Hailong
Published: February 2021

Intercropping has been practiced worldwide in both traditional and sustainable agriculture to feed the growing population. This study aims to analyze the research status and evolution of intercropping, to identify the influential authors, research centers, and articles, and to reveal the main research topics between 1992 and 2020 based on the Web of Science Core Collection database. The results show that the volume of publications in this field has increased rapidly over the past three decades. The analysis identifies the top three authors (i.e., Meine Van Noordwijk, Wenyu Yang, and Teja Tscharntke), top three contributing organizations (i.e., the World Agroforestry Center (ICRAF), the Chinese Academy of Science, and the INRA), and three most productive countries (i.e., the USA, India, and China). Co-occurrence analysis demonstrates that studies on intercropping can be divided into four clusters as centered by keywords of intercropping/maize, biodiversity/conservation, agroforestry, and carbon, respectively. Lal 2004 is the most influential study with the greatest number of citations and Agroforestry Systems is the most utilized journal. Perspectives on future studies were also given. This study helps researchers to clarify the current research status in the field of intercropping and put forward its future research.
The Improved Phytoextraction of Heavy Metals and the Growth of Trifolium repens L.: The Role of K2HEDP and Plant Growth Regulators Alone and in Combination
Authors: Makarova Anna , Nikulina Elena , Avdeenkova Tatiana , Pishaeva Ksenia
Published: February 2021

Heavy metals are among the most widespread pollutants in soil. Phytoextraction technology is used to solve the problem of multi-metal-contaminated soil. The efficiency of this process can be increased by introducing various amendments. A soil amendment is any material added to a soil to improve its physical properties, such as water retention, permeability, water infiltration, drainage, aeration, and structure. Some chemical amendments for enhanced phytoextraction, such as amino polycarboxylates chelators, can be hazardous to the environment and perform poorly at pH > 8. The effect of the potassium salt of hydroxyethylidene diphosphonic acid (K2HEDP), plant growth regulators (PGRs), and iron chelate alone and in combination on the phytoextraction by Trifolium repens L. seedlings of Cd, Ni, and Cu was studied in this work. K2HEDP works in a wider pH range. The results of this study confirmed that amino polycarboxylate chelators, with the sodium salt of ethylene diamine tetraacetic acid (Na2EDTA) as an example, have a pronounced negative effect on the growth and development (organ mass) of Trifolium repens L. seedlings. K2HEDP, proposed by the authors instead of Na2EDTA, produced a pronounced positive effect on plant growth and development, which was further enhanced by the use of PGRs and with iron chelates. However, it should be noted that K2HEDP showed significantly lower efficiency in trials on the Trifolium repens L. seedlings. The highest was the efficiency of K2HEDP with PGRs and iron chelates for the phytoextraction of Cd.
Financial Reporting Quality and Online Disclosure Practices in Spanish Governmental Agencies
Authors: Garcia-Lacalle Javier , Torres Lourdes
Published: February 2021

Good governance in the public sector implies high accountability levels. Accountability is a multidimensional concept that includes the quantity and reliability of disclosures as well as rendering accounts to the citizenry. Nowadays, good governance, accountability, and financial performance are key for the long-term sustainability of autonomous public sector organizations. The objective of this paper is to study how key governance features relate to the quantity and quality of the information disclosed. In particular, how the governing body, financial performance and organizational features relate to online transparency and financial reporting quality, as well as how these two accountability dimensions are related. To fulfill this objective, we have used the Structural Equation Modeling, Partial Least Squares (PLS-SEM) approach. The focus of our analyses is on Spanish central government agencies. The size of, and a greater presence of independent members in, the governing body are explanatory factors behind the quality of the financial reports. Our findings also show that the quality of the financial information is also affected by the pressure that Eurostat requirements -deficit limits- puts on public sector entities, which leads to the use of smoothing practices. Online disclosure practices are not explained by the features of the governing body, but by the size of the agencies and their financial results. The better the financial reporting quality, the higher the online disclosure levels.
Cryptourbanomics: A Method to Boost Urban Sustainability with Blockchain Technology
Authors: Marsal-Llacuna Maria-Luisa
Published: February 2021

Cryptourbanomics puts forward the idea that there are forces and capital in our society that cannot be dismissed or neglected but that the System (understood as the Establishment or Status-quo) has failed to acknowledge or been unable to address. These social forces have strong ideological, cultural, or identity components, sometimes related to an unrealized Right to the City (Lefèvre, 1968). The social capital behind those forces are often citizens who gave up—the so-called drop-outs because they lost their faith in the System and prefer living in their own world. Blockchain is the technology that empowers these unheard social forces and capital. However, blockchain will remain as an Anti-System technology until it finds a fit within the Establishment until the Status-quo acknowledges and ushers it. Cryptourbanomics is a novel method that brings into the blockchain those societal challenges that the System leaves unsolved. And because today’s societal challenges mostly take place in urban environments, the Cryptourbanomics method focuses on the overall urban sustainability and analyses them with a blockchain lens. The Cryptourbanomics method includes an array of blockchain tools to tackle legacy societal challenges yet unsolved by the System with a more decentralised, distributed, transparent and neutral approach. This paper shows how the Cryptourbanomics method can help deliver on Urban Sustainability by shifting powers, from the Establishment to Communities, and it showcases this with a use case within the Right to the City, that is the Right to Work.
Analysis on the Coupling Relationship and Coordinated Development between the Construction of Ethnic Minority Tourist Towns and the Tourism Industry
Authors: Lin Ling , Li Jiangfeng
Published: February 2021

The construction of minority characteristic tourist towns and the coordinated development of the tourism industry is an important way for ethnic minority tourist towns to improve their economic, social, resource, and environmental development levels. This article analyzed the mechanism of the coupling and coordination relationship between the construction system of ethnic minority characteristic tourist towns and the tourism industry system from three aspects: economy, society, and resource environment. Taking Longji Town as the research object, we constructed an index system for the coupling and coordinated development of the two systems. By using the coupling coordination degree model to analyze the coordinated development of Longji Town construction and tourism industry from 2013 to 2018, we found that the two systems generally show an upward development trend, and the degree of coupling coordination has changed from barely coupling coordination to good coupling coordination. The construction level of Longji Town and the degree of coupling coordination of the tourism industry system were manifested as the lagging development of the town construction level and the lagging tourism industry development alternately. The leading role of the development of Longji’s tourism industry in the construction of small towns was fluctuating and lacked stability. According to the research results, a policy reference for the sustainable development path of minority characteristic tourist towns was proposed.
Spare Parts Inventory Management: A Literature Review
Authors: Zhang Shuai , Huang Kai , Yuan Yufei
Published: February 2021

Spare parts are held as inventory to support product maintenance in order to reduce downtime and extend the lifetime of products. Recently, spare parts inventory management has been attracting more attention due to the “right-to-repair” movement which requires that manufacturers provide sufficient spare parts throughout the life-cyle of their products to reduce waste so as to achieve sustainability. In this review, 148 papers regarding spare parts inventory management published from 2010 to 2020 are examined. The studies are classified based on two groups of perspectives. The first group includes the characteristics of spare parts, products, inventory systems, and supply chains, while the second group focuses on the characteristics of research methodologies and topics in the reviewed studies. The novelty of this literature review is three-fold. Firstly, we focus on analyzing the supply chain structure of different inventory networks for managing spare parts. Secondly, we classify the current literature based on analytics techniques, i.e., descriptive analytics, predictive analytics, and prescriptive analytics. Finally, the research gaps in this field are discussed from the perspective of reverse logistics, consumer durable goods, inventory network structure and policy, spare parts demand pattern modeling, and big data analytics.
Research on Dynamic Analysis and Mitigation Strategies of Supply Chains under Different Disruption Risks
Authors: Zhang Qing , Fan Weiguo , Lu Jianchang , Wu Siqian , Wang Xuechao
Published: February 2021

Due to the globalization of supply and production, supply chain management has tightened the connection between upstream and downstream enterprises. Although this modern strategy has significantly improved the efficiency of enterprises, the increasingly complex relationship between nodes also makes the supply chain system more vulnerable and unstable. As a result, the interruption of any node location in the supply chain will spread to other nodes via their diffusion, which could cause irreparable damage to the entire supply chain. Therefore, under this realistic background, only by quantitatively analyzing the specific impact on the supply chain of interruption events in different locations we can formulate active and effective mitigation strategies to achieve the effective recovery of node enterprises from interruption accidents. In this study, the system dynamics method was used to simulate the changes in inventory level, order accumulation, and profit level caused by disruption of supply, production, and sales of different node companies. The results show that the closer the node enterprise to the interruption source, the greater the risk of loss. Due to the conduction effect of the supply chain system, the risk spreads to other node enterprises. Based on the above results, corresponding mitigation strategies for enterprises to cope with different node interruptions are proposed to improve the overall efficiency and operational capabilities of the enterprise.
The Evolution of Communicating the Uncertainty of Climate Change to Policymakers: A Study of IPCC Synthesis Reports
Authors: Molina Tomas , Abadal Ernest
Published: February 2021

The Intergovernmental Panel on Climate Change (IPCC) reports on climate change have served to alert both the public and policymakers about the scope of the predicted changes and the effects they would have on natural and economic systems. The first IPCC report was published in 1990, since which time a further four have been produced. The aim of this study was to conduct a content analysis of the IPCC Summaries for Policymakers in order to determine the degree of certainty associated with the statements they contain. For each of the reports we analyzed all statements containing expressions indicating the corresponding level of confidence. The aggregated results show a shift over time towards higher certainty levels, implying a “Call to action” (from 32.8% of statements in IPCC2 to 70.2% in IPCC5). With regard to the international agreements drawn up to tackle climate change, the growing level of confidence expressed in the IPCC Summaries for Policymakers reports might have been a relevant factor in the history of decision making.
Topographic Effects on the Spatial Species Associations in Diverse Heterogeneous Tropical Evergreen Forests
Authors: Hai Nguyen Hong , Erfanifard Yousef , Bui Van Bac , Mai Trinh Hien , Petritan Any Mary , Petritan Ion Catalin
Published: February 2021

Studying spatial patterns and habitat association of plant communities may provide understanding of the ecological mechanisms and processes that maintain species coexistence. To conduct assessments of correlation between community compositions and habitat association, we used data from two topographically different plots with 2 ha area in tropical evergreen forests with the variables recorded via grid systems of 10 × 10 m subplots in Northern-Central Vietnam. First, we tested the relationship between community composition and species diversity indices considering the topographical variables. We then assessed the interspecific interactions of 20 dominant plant species using the nearest-neighbor distribution function, Dij(r), and Ripley’s K-function, Kij(r). Based on the significant spatial association of species pairs, indices of interspecific interaction were calculated by the quantitative amounts of the summary statistics. The results showed that (i) community compositions were significantly influenced by the topographic variables and (ii) almost 50% significant pairs of species interactions were increased with increasing spatial scales up to 10–15 m, then declined and disappeared at scales of 30–40 m. Segregation and partial overlap were the dominant association types and disappeared at larger spatial scales. Spatial segregation, mixing, and partial overlap revealed the important species interactions in maintaining species coexistence under habitat heterogeneity in diverse forest communities.
Effects of Salinity and Rootstock on Nutrient Element Concentrations and Physiology in Own-Rooted or Grafted to 1103 P and 101-14 Mgt Rootstocks of Merlot and Cabernet Franc Grapevine Cultivars under Climate Change
Authors: Nikolaou Kleopatra-Eleni , Chatzistathis Theocharis , Theocharis Serafeim , Argiriou Anagnostis , Koundouras Stefanos , Zioziou Elefteria
Published: February 2021

Under the current and future climate crisis, a significant rise in soil salinity will likely affect vine productivity in several Mediterranean regions. During the present research, the rootstock effects on salinity tolerance of Merlot and Cabernet Franc grapevine cultivars were studied. In a pot hydroponic culture, own-rooted Merlot and Cabernet Franc grapevine cultivars or grafted onto the rootstocks 1103 P and 101-14 Mgt were drip-irrigated with saline water. A completely randomized 3 × 2 × 2 factorial experiment was designed with two vine rootstocks or own-rooted vines, two scion cultivars, and 100 mM NaCl salinity or control treatments, with six replications. A significant effect of scion cultivar, rootstock, and salinity was observed for most of the measured parameters. At the end of salinity stress period, leaf, shoot, root, and trunk nutrient concentrations were measured. Salinity stress increased Chloride (Cl) and Sodium (Na) concentrations in all parts of the vines and decreased leaf concentrations of Potassium (K), Calcium (Ca), Magnesium (Mg), Nitrogen (N), and Iron (Fe). In contrast, salinity stress increased leaf Boron (B) concentrations, whereas that of Manganese (Mn) remained unaffected. Leaf chlorophyll concentration decreased from 42% to 40% after thirty and sixty days of salt treatment, respectively. A similar trend was observed for the CCM-200 relative chlorophyll content. Salinity significantly decreased steam water potential (Ws), net CO2 assimilation rate (A), and stomatal conductance (gs) in all cases of grafted or own-rooted vines. Sixty days after the beginning of salt treatment, total Phenolics and PSII maximum quantum yield (Fv/Fm) decreased significantly. The rootstock 1103 P seems to be a better excluder for Na and Cl and more tolerant to salinity compared to 101-14 Mgt rootstock.
Measuring the Level of Environmental Performance on Coastal Environment before and during the COVID-19 Pandemic: A Case Study from Cyprus
Authors: Loizia Pantelitsa , Voukkali Irene , Chatziparaskeva Georgia , Navarro-Pedreño Jose , Zorpas Antonis A.
Published: February 2021

Tourism activities are considered, among others, the backbone of the local economies. However, tourism activities lead to adverse environmental impacts, especially in coastal zones. Coastal areas are considered and recognized as of strategic importance due to the fact that several activities take place, from leisure to business. At the same time, coastal areas are under pressure from tourist activities, and the waste generated is a very serious issue. Therefore, there are limited studies related to the environmental dimensions of the COVID-19 pandemic in the coastal environment. This paper provides answers to the hypothesis that the pandemic lockdown scenario would improve environmental performance due to reduced usage and, therefore, waste, taking into account specific key performance indicators (KPIs) as these KPIs are used to evaluate the performance of an area. The results showed that the study area improved, as did the selected KPIs, i.e., clean coast index (CCI), waste accumulation rate (WAR), and waste accumulation index (WAI). Additionally, according to the final results, the concentration of micro-, meso- and macroplastics on the beach reduced, and the main issues remained the solutions on cigarette butts, straws, and other plastic containers. Furthermore, the final results are considered very useful to local authorities, stakeholders, consultants, policymakers, and any other competent authorities, to reschedule their waste management strategies, to improve waste infrastructures and their level of services (LOS), as well as, to suggest frequent awareness-raising activities to their visitors on how to protect the coastal environment, taking into account a pandemic scenario, as well as, the policy alternative impacts on EU coastal zones 2000–2050.
The Analysis of Small Investors’ Demands on a Thermal Insulation System for a Family House: A Case Study
Authors: Tažiková Alena , Struková Zuzana , Kozlovská Mária
Published: February 2021

This study deals with small investors’ demands on thermal insulation systems when choosing the most suitable solution for a family house. By 2050, seventy percent of current buildings, including residential buildings, are still expected to be in operation. To reach carbon neutrality, it is necessary to reduce operational energy consumption and thus reduce the related cost of building operations and the cost of the life cycle of buildings. One solution is to adapt envelopes of buildings by proper insulation solutions. To choose an optimal thermal insulation system that will reduce energy consumption of building, it is necessary to consider the environmental cost of insulation materials in addition to the construction cost of the materials. The environmental cost of a material depends on the carbon footprint from the initial origin of the material. This study presents the results of a multi-criteria decision-making analysis, where five different contractors set the evaluation criteria for selection of the optimal thermal insulation system. In their decision-making, they involved the requirements of small investors. The most common requirements were selected: the construction cost, the construction time (represented by the total man-hours), the thermal conductivity coefficient, the diffusion resistance factor, and the reaction to fire. The confidences of the criteria were then determined with the help of the pairwise comparison method. This was followed by multi-criteria decision-making using the method of index coefficients, also known as the method of basic variant. The multi-criteria decision-making included thermal insulation systems based on polystyrene, mineral wool, thermal insulation plaster, and aerogels’ nanotechnology. As a result, it was concluded that, currently, in Slovakia, small investors emphasize the cost of material and the coefficient of thermal conductivity and they do not care as much about the carbon footprint of the material manufacturing, the importance of which is mentioned in this study.
Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review
Authors: Xie Rong , Chen Muyan , Liu Weihuang , Jian Hongfei , Shi Yanjun
Published: February 2021

Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.
Factors Allowing Users to Influence the Environmental Performance of Their T-Shirt
Authors: Schmutz Mélanie , Hischier Roland , Som Claudia
Published: February 2021

Cotton t-shirts are a basic clothing item that everyone possesses. To date, no studies have taken into account the consumers’ perspective, even though they can play an important role regarding the actual environmental impact of their clothing items. Therefore, a life cycle assessment study was performed in order to inform the public about the environmental impacts of a typical cotton t-shirt and the relevance of consumer behavior (i.e., washing and drying) on the overall impacts along the entire life cycle of such a t-shirt. The aim was to provide hints, allowing users to reduce the impacts of their t-shirts. While the production phase was based on global data, the use phase focused on Switzerland as the study was established in the context of an exhibition in the Textile Museum in St. Gallen (Switzerland). With this study, it was found that users have various choices in order to make their t-shirt more sustainable. Wearing the t-shirt throughout its entire life expectancy was found to be the most important factor influencing the overall environmental performance of such a clothing item. The relevance of filling the washing machine to maximum capacity, washing at a lower temperature, or using a tumbler was also illustrated. In addition, choosing materials other than cotton or choosing textiles labelled for lower environmental impacts during production could further improve the environmental performance of t-shirts.
Window of Economic Opportunity or Door of Exclusion? Nandoni Dam and Its Local Communities
Authors: Sinthumule Ndidzulafhi Innocent
Published: February 2021

The construction of Nandoni Dam started in 1998 and was completed in 2005. The main promises made to affected communities living around the dam were that they would receive water and there would be economic opportunities for them. Water-based recreation and ecotourism at the dam were seen as the main vehicle for economic opportunities; it was envisaged that these would not only create local employment but would also improve local lives and livelihoods. The paper focuses on the rhetoric of economic opportunities and poverty alleviation and the perceived reasoning that the creation of the dam is the way forward for rural development. To achieve this aim, the study combined qualitative and quantitative methods. The claim that the creation of the dam would result in increased economic benefits for local residents is firmly rooted in business interests. The adoption of business or economic approach by government through the Department of Water and Sanitation has created conditions for segregation within society. The study also found that the creation of Nandoni Dam benefits those who are rich and elite, government officials, and politicians at the expense of the local population, and this has long-lasting detrimental impacts on their lives and livelihoods. The study concludes by giving an overview of lessons learned from the case study and suggestions for best practice that can enhance the long-term sustainability of dam projects.
The B Corp Movement: A Systematic Literature Review
Authors: Diez-Busto Elsa , Sanchez-Ruiz Lidia , Fernandez-Laviada Ana
Published: February 2021

This work develops a systematic literature review on the B Corp movement. Fifty articles were identified in the Web of Science and Scopus databases until 2020. Results show that it is an incipient field with great potential. There are two topics repeatedly addressed in the analyzed publications: the motivations of companies to be certified, and the economic and social effects that occur after certification. Sustainability is currently the reference journal in the field, since it has published a total of five articles. Finally, several lines of future work are proposed, such as the contribution of B Corp companies to the SDGs; the development of case studies on the certification process itself; and the development of methods for measuring social impact.
Funding Campus Sustainability through a Green Fee—Estimating Students’ Willingness to Pay
Authors: González-Ramírez Jimena , Cheng Heyi , Arral Sierra
Published: February 2021

Many higher education institutions promote sustainability by instilling environmental awareness within college students, the innovators of the future. As higher education institutions face budgetary constraints to achieve greener campuses, green fees have emerged as an alternative method for universities to encourage student participation and overall campus sustainability. A green fee is a mandatory student fee that funds sustainability projects on campus and is typically managed by a group of students and faculty. We are the first to assess students’ support for a mandatory green using a single dichotomous choice, contingent valuation question and estimating the willingness to pay to fund campus sustainability using a discrete choice model. Using results from a survey at a private college in New York City, we found more support for $5 and $10 green fee values. Using both parametric and non-parametric estimation methods, we found that mean and median willingness-to-pay values were between $13 and $15 and between $10 and $18, respectively. We suggest implementing a green fee between $10 and $13 following the lower values of the non-parametric median willingness to pay (WTP) range estimates that do not rely on distributional assumptions. We hope that other academic institutions follow our research steps to assess the support for a green fee and to suggest a green fee value for their institutions.