bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China
Authors: Ding Jianli , Yang Shengtian , Shi Qian , Wei Yang , Wang Fei
Published: August 2020

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m−1), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R2 range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m−1. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales.
Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities
Authors: Bhatnagar Saheba , Gill Laurence , Ghosh Bidisha
Published: August 2020

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.
Integration of Digital Image Correlation of Sentinel-2 Data and Continuous GNSS for Long-Term Slope Movements Monitoring in Moderately Rapid Landslides
Authors: Mulas Marco , Ciccarese Giuseppe , Truffelli Giovanni , Corsini Alessandro
Published: August 2020

This work explores the advantages and drawbacks of the application of Digital Image Correlation (DIC) to Sentinel-2 Multi Spectral Instrument (MSI) data in conjunction with continuous Global Navigation Satellite System (GNSS) monitoring. The goal is to retrieve a spatially distributed and long-term time-series of slope movements in large-scale moderately rapid landslides. The short revisit time of Sentinel-2 satellites (5 days since March 2017 and 10 days before) increases the availability of cloud and snow free satellite acquisitions of the area of interest, which is a prerequisite for the extrapolation of slope movement time-series using DIC techniques. Despite the Sentinel-2 limited spatial resolution, the derived long time-series can be integrated with—and validated by—continuous GNSS monitoring data. This allows to effectively monitor landslide movements that are too fast for the application of interferometric approaches. In this study, we used the Normalized Cross Correlation (NCC) digital image correlation technique by 51 Sentinel-2 MSI scenes (band 4 with 10 m spatial resolution), acquired between 19 February 2016 and 16 July 2019, to derive the slope movement time-series of the Ca’ Lita earthslide-earthflow in the northern Apennines (Italy). During the period considered, the landslide experienced two to three months-long phases of moderately rapid velocity (around 10 m/month) and, in between, prolonged periods of slow movements (approx. 10 cm/month). NCC results have been integrated with, and are compared to, time series from three continuous GNSS devices located in different geomorphic zones of the landslide. On this basis, the errors and limitations associated to NCC time series are analysed and discussed together with their advantages and potentialities for assessing the spatial distribution and monitoring slope movements during moderately rapid reactivation events.
Validation of Sentinel-3 OLCI Integrated Water Vapor Products Using Regional GNSS Measurements in Crete, Greece
Authors: Mertikas Stelios , Partsinevelos Panagiotis , Tripolitsiotis Achilleas , Kokolakis Costas , Petrakis George , Frantzis Xenophon
Published: August 2020

Water vapor is one of the essential variables in monitoring the Earth’s climate. The Ocean and Land Color Instrument (OLCI) on-board the Copernicus Sentinel-3 missions measures the Integrated Water Vapor (IWV) column over land and ocean surfaces. Post-launch calibration and validation of satellite measurements constitutes a key process in the operational phase of Earth observation satellites. This work presents the external and independent validation of OLCI-A IWV product using the regional network of continuously operating Global Navigation Satellite System (GNSS) comprised 10 stations distributed over the island of Crete in the eastern Mediterranean. The Sentinel-3A/-3B OLCI imagery that captures in a single scene the entire area of Crete has been examined. For each OLCI image, the IWV value of cloud-free pixels containing the GNSS stations have been derived and compared against simultaneous GNSS-derived measurements. The absolute as well as the relative bias between OLCI-A and OLCI-B IWV measurements have been determined. There is a good agreement between OLCI and GNSS with a bias of −0.57 mm ± 2.90 mm for OLCI(A) and +2.42 ± 3.41 mm for OLCI(B). The results of this regional validation activity are compared against other studies and the regular validation carried out at the Sentinel-3 Mission Performance Center. This work concludes that the accuracy of the OLCI IWV products is within its design requirements. The potential synergy between Sentinel-2 and Sentinel-3 IWV products is also discussed.
Spatio-Temporal Vegetation Dynamic and Persistence under Climatic and Anthropogenic Factors
Authors: Bashir Barjeece , Cao Chunxiang , Naeem Shahid , Zamani Joharestani Mehdi , Bo Xie , Afzal Huma , Jamal Kashif , Mumtaz Faisal
Published: August 2020

Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.
An Operational Split-Window Algorithm for Retrieving Land Surface Temperature from Geostationary Satellite Data: A Case Study on Himawari-8 AHI Data
Authors: Li Ruibo , Li Hua , Sun Lin , Yang Yikun , Hu Tian , Bian Zunjian , Cao Biao , Du Yongming , Liu Qinhuo
Published: August 2020

An operational split-window (SW) algorithm was developed to retrieve high-temporal-resolution land surface temperature (LST) from global geostationary (GEO) satellite data. First, the MODTRAN 5.2 and SeeBor V5.0 atmospheric profiles were used to establish a simulation database to derive the SW algorithm coefficients for GEO satellites. Then, the dynamic land surface emissivities (LSEs) in the two SW bands were estimated using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED), fractional vegetation cover (FVC), and snow cover products. Here, the proposed SW algorithm was applied to Himawari-8 Advanced Himawari Imager (AHI) observations. LST estimates were retrieved in January, April, July, and October 2016, and three validation methods were used to evaluate the LST retrievals, including the temperature-based (T-based) method, radiance-based (R-based) method, and intercomparison method. The in situ night-time observations from two Heihe Watershed Allied Telemetry Experimental Research (HiWATER) sites and four Terrestrial Ecosystem Research Network (TERN) OzFlux sites were used in the T-based validation, where a mean bias of −0.70 K and a mean root-mean-square error (RMSE) of 2.29 K were achieved. In the R-based validation, the biases were 0.14 and −0.13 K and RMSEs were 0.83 and 0.86 K for the daytime and nighttime, respectively, over four forest sites, four desert sites, and two inland water sites. Additionally, the AHI LST estimates were compared with the Collection 6 MYD11_L2 and MYD21_L2 LST products over southeastern China and the Australian continent, and the results indicated that the AHI LST was more consistent with the MYD21 LST and was generally higher than the MYD11 LST. The pronounced discrepancy between the AHI and MYD11 LST could be mainly caused by the differences in the emissivities used. We conclude that the developed SW algorithm is of high accuracy and shows promise in producing LST data with global coverage using observations from a constellation of GEO satellites.
Mask Gradient Response-Based Threshold Segmentation for Surface Defect Detection of Milled Aluminum Ingot
Authors: Liang Ying , Xu Ke , Zhou Peng
Published: August 2020

The surface quality of aluminum ingot is crucial for subsequent products, so it is necessary to adaptively detect different types of defects in milled aluminum ingots surfaces. In order to quickly apply the calculations to a real production line, a novel two-stage detection approach is proposed. Firstly, we proposed a novel mask gradient response-based threshold segmentation (MGRTS) in which the mask gradient response is the gradient map after the strong gradient has been eliminated by the binary mask, so that the various defects can be effectively extracted from the mask gradient response map by iterative threshold segmentation. In the region of interest (ROI) extraction, we combine the MGRTS and the Difference of Gaussian (DoG) to effectively improve the detection rate. In the aspect of the defect classification, we train the inception-v3 network with a data augmentation technology and the focal loss in order to overcome the class imbalance problem and improve the classification accuracy. The comparative study shows that the proposed method is efficient and robust for detecting various defects on an aluminum ingot surface with complex milling grain. In addition, it has been applied to the actual production line of an aluminum ingot milling machine, which satisfies the requirement of accuracy and real time very well.
Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers
Authors: Sieciński Szymon , Kostka Paweł S. , Tkacz Ewaryst J.
Published: August 2020

Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan–Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
On Performance and Perceived Effort in Trail Runners Using Sensor Control to Generate Biosynchronous Music
Authors: Williams Duncan , Fazenda Bruno , Williamson Victoria , Fazekas György
Published: August 2020

Music has been shown to be capable of improving runners’ performance in treadmill and laboratory-based experiments. This paper evaluates a generative music system, namely HEARTBEATS, designed to create biosignal synchronous music in real-time according to an individual athlete’s heartrate or cadence (steps per minute). The tempo, melody, and timbral features of the generated music are modulated according to biosensor input from each runner using a combination of PPG (Photoplethysmography) and GPS (Global Positioning System) from a wearable sensor, synchronized via Bluetooth. We compare the relative performance of athletes listening to music with heartrate and cadence synchronous tempos, across a randomized trial (N = 54) on a trail course with 76 ft of elevation. Participants were instructed to continue until their self-reported perceived effort went beyond an 18 using the Borg rating of perceived exertion. We found that cadence-synchronous music improved performance and decreased perceived effort in male runners. For female runners, cadence synchronous music improved performance but it was heartrate synchronous music which significantly reduced perceived effort and allowed them to run the longest of all groups tested. This work has implications for the future design and implementation of novel portable music systems and in music-assisted coaching.
A High-Strength Strain Sensor Based on a Reshaped Micro-Air-Cavity
Authors: Chen Yanping , Luo Junxian , Liu Shen , Zou Mengqiang , Lu Shengzhen , Yang Yong , Liao Changrui , Wang Yiping
Published: August 2020

We demonstrate a high-strength strain sensor based on a micro-air-cavity reshaped through repeating arc discharge. The strain sensor has a micro-scale cavity, approximate plane reflection, and large wall thickness, contributing to a broad free spectrum range ~36 nm at 1555 nm, high fringe contrast ~38 dB, and super-high mechanical robustness, respectively. A sensitivity of ~2.39 pm/με and a large measurement range of 0 to 9800 με are achieved for this strain sensor. The strain sensor has a high strength, e.g., the tensile strain applied the sensor is up to 10,000 με until the tested the single-mode fiber is broken into two sections. In addition, it exhibited low thermal sensitivity of less than 1.0 pm/°C reducing the cross-sensitivity between tensile strain and temperature.
Temperature Sequential Data Fusion Algorithm Based on Cluster Hierarchical Sensor Networks
Authors: Yang Tianwei , Nan Xinyuan , Jin Weixu
Published: August 2020

The process of extracting gold by biological oxidation involves oxidizing the refractory high-sulfur and high-arsenic ore with the help of bacteria to decompose the wrapping material of gold to extract the gold. Therefore, maximizing the activity of bacteria will directly affect the efficiency of gold extraction, for which it is particularly important to maintain the pulp temperature in the oxidation tank at the optimal bacteria breeding temperature. However, gold mines are generally located in mountainous areas, and the large temperature difference between day and night in winter, coupled with the influence of wind and snow, creates variations in the temperature in the oxidation tank. The traditional temperature measurement method cannot fully reflect the temperature change of the oxidation tank. As a multi-field application method, sensor information fusion can effectively address the problem of pulp temperature measurement. First, we analyzed the heat transfer principle inside the oxidation tank, and designed the cluster hierarchical sensor network according to the spatial position of each oxidation tank and the environmental interference factors. The network structure is divided into three layers; the bottom of the sensor to collect pulp temperature data shows a spiral distribution in the inner wall of the oxidation tank. Each cluster head node sensor is used as an intermediate layer to complete local measurement fusion estimation. Finally, the fusion center is taken as the upper layer to realize the global state fusion estimation. Secondly, in the data processing of the bottom temperature sensor, the traditional unscented Kalman filter (UKF) algorithm is improved and the fading memory matrix is added to improve the identification of nonlinear modeling errors. The sequential observation fusion estimator (SOFE) algorithm is embedded in the measurement update to improve the performance of local measurement fusion. Finally, in the global state fusion estimation, the sequential analysis is combined with the inverse covariance intersection, and the sequential analysis and inverse covariance intersection-global state fusion estimation (SICI-GSFE) algorithm is proposed. Through calculation and simulation, the results show that the external interference can be reduced by combining all the temperature state estimations, and the accuracy of the best global temperature state estimation is improved.
Wavelet-Based Kalman Smoothing Method for Uncertain Parameters Processing: Applications in Oil Well-Testing Data Denoising and Prediction
Authors: Feng Xin , Feng Qiang , Li Shaohui , Hou Xingwei , Zhang Mengqiu , Liu Shugui
Published: August 2020

The low-distortion processing of well-testing geological parameters is a key way to provide decision-making support for oil and gas field development. However, the classical processing methods face many problems, such as the stochastic nature of the data, the randomness of initial parameters, poor denoising ability, and the lack of data compression and prediction mechanisms. These problems result in poor real-time predictability of oil operation status and difficulty in offline interpreting the played back data. Given these, we propose a wavelet-based Kalman smoothing method for processing uncertain oil well-testing data. First, we use correlation and reconstruction errors as analysis indicators and determine the optimal combination of decomposition scale and vanishing moments suitable for wavelet analysis of oil data. Second, we build a ground pressure measuring platform and use the pressure gauge equipped with the optimal combination parameters to complete the downhole online wavelet decomposition, filtering, Kalman prediction, and data storage. After the storage data are played back, the optimal Kalman parameters obtained by particle swarm optimization are used to complete the data smoothing for each sample. The experiments compare the signal-to-noise ratio and the root mean square error before and after using different classical processing models. In addition, robustness analysis is added. The proposed method, on the one hand, has the features of decorrelation and compressing data, which provide technical support for real-time uploading of downhole data; on the other hand, it can perform minimal variance unbiased estimates of the data, filter out the interference and noise, reduce the reconstruction error, and make the data have a high resolution and strong robustness.
The Academic Mobility of Students from Kazakhstan to Japan: Problems and Prospects
Authors: Rustemova Aktolkyn , Meirmanov Serik , Okada Akito , Ashinova Zhanar , Rustem Kamshat
Published: August 2020

Background: Despite the internationalization of higher education (IoHE) in Kazakhstan and it being among the top 15 countries sending students abroad, the level of student mobility between Kazakhstan and Japan and factors influencing it have not been well analyzed. Aim: The purpose of this study was to explore outgoing student mobility from Kazakhstan to Japan and the reasons underlining this situation. Methods: A descriptive study, involving a literature review and an analysis of data from the two largest universities in Kazakhstan together with those from the Bolashak international scholarship scheme, was performed. Results: The analysis suggested that outgoing student mobility from Kazakhstan to Japan remains at a stable but low level. The mobility of students is mainly a matter of the initiative of universities or the students themselves. The main reasons are the dependence of the policy of the IoHE on the foreign policy of Kazakhstan; the low level of pull factors for students such as affordability, recommendations from acquaintances, or lack of awareness about the country; and absence of a supportive legislative basis for the development of cooperation on higher education between the two countries. Conclusion: Given the areas of common interest economically and politically between Kazakhstan and Japan, there is potential for significantly more student mobility than there is at present.
Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation
Authors: Feng Yanhong , Xu Dilong , Failler Pierre , Li Tinghui
Published: August 2020

Due to multiple properties, the international crude oil price is influenced by various and complex interrelated factors from different determinants in different periods. However, the previous studies on crude oil price fluctuation with economic policy uncertainty (EPU) haven’t taken a wider range of volatility sources into their analysis frameworks. In this paper, the time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is introduced in order to avoid important information loss, as well as capture the time-varying impact on crude oil price fluctuation by EPU. Furthermore, the differences on crude oil fluctuations from net-oil exporting and net-oil importing country’s EPU are also elaborated. Here are three findings as follows. First, the impacts of global EPU on the crude oil price volatility show time-varying characteristics both in time duration and time-points. Second, the instantaneous impacts of global EPU on the price volatility of crude oil are directly relevant to major events, and the impacts are different in event types as well. Third, the time-varying characteristics depicting the impacts of EPU in countries who are net-oil exporter and net-oil importer on price volatility of crude oil show heterogeneity in fluctuation range, fluctuation intensity, and stage.
Evaluation of Family Houses in Slovakia Using a Building Environmental Assessment System
Authors: Krídlová Burdová Eva , Selecká Iveta , Vilčeková Silvia , Burák Dušan , Sedláková Anna
Published: August 2020

The presented study is focused on the verification of a Building Environmental Assessment System (BEAS). A total of 13 detached family houses representing typical construction sites in Slovakia were chosen for analysis, evaluation and certification by using a BEAS which contains several main fields: A—Site Selection and Project Planning; B—Building Construction; C—Indoor Environment; D—Energy Performance; E—Water Management; and F—Waste Management. The results of this study show that the current construction method for family houses does not respect the criteria of sustainable construction as much as it possibly can. The reason for this is that investment costs for construction are prioritized over environmental and social aspects. Therefore, one house with a score of 1.10 is certified as BEAS BRONZE, ten family houses with scores of 1.56–2.88 are certified as BEAS SILVER and only two family houses with total scores of 3.59 and 3.87, respectively, are certified as BEAS GOLD. The overall results show that the weakest fields of sustainability are Waste management, Energy performance and Building construction. The best-rated fields are Site Selection and Project Planning, Indoor Environment and Water Management. In the future, it is essential to pay attention to those areas where the sustainability criteria have not been reached, as well as to raise project teams’ awareness of sustainability issues and subsequently to transfer them to building practices.
The Impact of Startups’ Dual Learning on Their Green Innovation Capability: The Effects of Business Executives’ Environmental Awareness and Environmental Regulations
Authors: Huang Shi-Zheng , Chau Ka Yin , Chien Fengsheng , Shen Huawen
Published: August 2020

Under the environment of a green economy, green innovation serves as the only way for enterprises to grow, upgrade their competitiveness and seek continued business. Based on a questionnaire survey of 212 enterprises established within 4 years in the Pearl River Delta of China, this research utilizes structural methods to analyze the impacts of exploratory and applied learning (dual learning) on green innovation capability and verifies the environmental protection awareness of senior executives and the adjustment effects of environmental regulation. The results suggest that (1) exploratory and applied learning have a positively significant impact on green innovation capability; (2) under the regulation of environmental protection awareness of internal executives, there are differences in green innovation capabilities under the dual influences of exploratory and applied learning; and (3) under the adjustment of external environmental regulation, there are differences in green innovation capabilities under the dual influences of exploratory and applied learning. The findings indicate that new start-up ventures should raise awareness of environmental protection among senior executives under dual learning and perceive the changes of the government’s environmental regulations to enhance their green innovation capabilities.
An Economic Comparison between Alternative Rice Farming Systems in Tanzania Using a Monte Carlo Simulation Approach
Authors: Kadigi Ibrahim L. , Mutabazi Khamaldin D. , Philip Damas , Richardson James W. , Bizimana Jean-Claude , Mbungu Winfred , Mahoo Henry F. , Sieber Stefan
Published: August 2020

Tanzania is the second-largest producer of rice (Oryza sativa) in Eastern, Central, and Southern Africa after Madagascar. Unfortunately, the sector has been performing poorly due to many constraints, including poor agricultural practices and climate variability. In addressing the challenge, the government is making substantial investments to speed the agriculture transformation into a more modernized, commercial, and highly productive and profitable sector. Our objective was to apply a Monte Carlo simulation approach to assess the economic feasibility of alternative rice farming systems operating in Tanzania while considering risk analysis for decision-makers with different risk preferences to make better management decisions. The rice farming systems in this study comprise rice farms using traditional practices and those using some or all of the recommended system of rice intensification (SRI) practices. The overall results show 2% and zero probability of net cash income (NCI) being negative for partial and full SRI adopters, respectively. Meanwhile, farmers using local and improved seeds have 66% and 60% probability of NCI being negative, correspondingly. Rice farms which applied fertilizers in addition to improved seeds have a 21% probability of negative returns. Additionally, net income for rice farms using local seeds was slightly worthwhile when the transaction made during the harvesting period compared to farms applied improved varieties due to a relatively high price for local seeds. These results help to inform policymakers and agencies promoting food security and eradication of poverty on the benefits of encouraging improved rice farming practices in the country. Despite climate variability, in Tanzania, it is still possible for rice farmers to increase food production and income through the application of improved technologies, particularly SRI management practices, which have shown a promising future.
Agrifood Chains as Complex Systems and the Role of Informality in Their Sustainability in Small Scale Societies
Authors: Bryceson Kim P. , Ross Anne
Published: August 2020

Agrifood chains are complex systems; they encompass biological, economic, social, health and political variables at different scales (e.g., on-farm, local, regional, national and global). Consequently, what enables a food system to achieve ‘sustainability’ is also complex. This is particularly the case in small-scale societies in developing nations which are socially constituted. In this paper we posit that a habitus of informality underpins food systems’ sustainability in these societies. We argue that conventional applications of approaches like the Triple Bottom Line (TBL) and Circles of Sustainability (CoS) frameworks fail to assist understanding of sustainability in informal socio-economic systems because they either place too much emphasis on economic growth (TBL) or underplay the strength of socio–cultural obligations and responsibilities (CoS). This is seen in international aid programs that encourage economic growth in the agrifood sector, which is challenging for villages in such societies. We review data from two Pacific Island countries—Tonga and Solomon Islands—to demonstrate the need for a more holistic way to think about sustainability in informal agrifood systems in small-scale developing nations. We demonstrate the value of employing a Hybrid Value Chain Framework for collecting information necessary to understanding how sustainability is constituted in the food systems of small-scale societies.
Sustainable Acoustic Materials
Authors: Arenas Jorge P. , Sakagami Kimihiro
Published: August 2020

Technological advances in materials science, manufacturing processes, chemistry and nanoscience have led to enormous developments in innovatively engineered materials over recent decades. Among them, sustainable acoustic materials have helped to improve acoustical comfort in built environments, and their use is rapidly growing in the architecture, automotive, aerospace and construction industries. These materials are manufactured through a responsible interaction with the environment in order to avoid a depletion or degradation of the natural resources, and to allow for long-term environmental quality. This Special Issue reports on some research studies on membrane absorbers and fibrous materials of natural origin that can be sustainable alternatives to traditional acoustic materials.
Fruit Sector Strategic Management: An Exploration of Agro-food Chain Actors’ Perception of Market Sustainability of Apple Innovation
Authors: Zanetti Moritz , Samoggia Antonella , Young Jennifer
Published: August 2020

The fresh fruit industry is a highly dynamic sector in the food market. Fresh fruit chain actors have to adapt to the changing market environment in order to face upcoming challenges. The objective of this study is to analyze the market sustainability of red-fleshed apples (RFA) by exploring consumers’ and fresh fruit industry experts’ and stakeholders’ opinions in Italy and New Zealand. The study was carried out in Italy and New Zealand from December 2018 to June 2019 including 778 consumers and 29 expert and stakeholder interviews. Results show a promising market potential of RFA. Innovative attributes and nutritional value of RFA are the most important factors that push consumers’ interest and willingness to buy RFA. New Zealand consumers and women show a higher appreciation of RFA. The nutritional value of the fruit can be a strategic marketing management attribute especially for health-conscious consumers. However, experts think the market for RFA will develop slowly, and will remain a niche product. Some stakeholders are skeptical about consumer RFA appreciation, and fear that RFA are too radical a novelty on the market. The RFA may be perceived as not natural. There is the need to coordinate food chain stakeholders’ management practices on RFA. This allows to mitigate risks, set food standards acceptable for all chain stakeholders, and make sustainable economic investment on innovation.
Models of Intersectoral Cooperation in Municipal Health Promotion and Prevention: Findings from a Scoping Review
Authors: Quilling Eike , Kruse Stefanie , Kuchler Maja , Leimann Janna , Walter Ulla
Published: August 2020

Intersectoral cooperation is regarded as a promising strategy in setting-oriented prevention and health promotion for interdepartmental, joint efforts towards improved health equity. This paper deals with models of intersectoral cooperation in municipal health promotion. It frames the methodology and the central results of a literature and database search (on behalf of the Federal Centre for Health Education, BZgA) and presents the partial results regarding the models of intersectoral cooperation. Of 48 publications analyzed for the review, nine publications each present different models of intersectoral cooperation. The models describe typical processes in the context of the joint work as detailed step sequences and formulate general recommendations as success factors of intersectoral cooperation. With reference to the findings of the review, it can be concluded that models for intersectoral cooperation have not yet been systematized and consequently have not been researched for transferability into specific fields of practice.
The Reuse of Waste Heaps from Extraction Sites: An Architectural Methodology
Authors: Talento Katia , Amado Miguel , Kullberg José Carlos
Published: August 2020

Nowadays, the rehabilitation of quarries is a widespread practice. It stems from the most significant environmental concern in the management of non-renewable resources. However, reusing quarry waste in the process of regeneration represents the aspect less prevalent. This procedure results from the non-commercialized material, accumulated in huge piles of waste, that constitute invasive elements of the landscape. Because of the intense industrial activity, the wastes further contribute to the degradation of the sites. The result of the waste abandonment could be reversed if this discarded material is considered as a product in the landscape requalification process. Mixing theoretical concepts with practical examples, this article proposes an application methodology to reuse the waste material in the landscape and architectural domains. This new contribution highlights the importance of considering scrap accumulations as an integral part of the architectural project, filling the existing gap in both architectural and theoretical domains. There is not any similar schematization in the literature and the idea of dealing with the mounds of wastes has been only slightly approached until now. The reached outcomes are demonstrative of the possibility of working with the waste heaps in architecture, creating a solid basis for further investigations not documented yet.
Making Use of Sustainable Local Plant Genetic Resources: Would Consumers Support the Recovery of a Traditional Purple Carrot?
Authors: Gracia Azucena , Sánchez Ana María , Jurado Francesc , Mallor Cristina
Published: August 2020

Local plant genetic resources are of vital importance for the resilience of the agroecosystems, especially under conditions of global climate change. The diversification of production using these resources is postulated as an alternative for the development of rural areas with non-optimal farming conditions and/or disadvantaged by depopulation. However, in order to sustainably utilize local genetic resources, their use has also to provide products accepted by consumers. The aim of this study was to evaluate consumer acceptance of a local purple carrot that is a Spanish landrace at risk of genetic erosion from Teruel, a province in the Aragón region seriously affected by depopulation and extreme weather conditions, to contribute to its sustainable recovery. Consumer preferences for carrots with different characteristics (color, price, variety, and production system) were studied, and their willingness to pay (WTP) for the local purple carrots was assessed. Data from a survey conducted in this Spanish region was used. We identified two segments of consumers with different willingness to pay, hedonic liking, and intention to purchase the purple local carrots. These traditional purple carrots would be accepted by the segment of consumers more willing to pay for and more likely to purchase these carrots. The traditional purple carrots should be promoted, emphasizing that they are produced by a local landrace whose purple color is due to anthocyanic pigments with known antioxidant properties.
Implications of BREEAM Sustainability Assessment on the Design of Hotels
Authors: Serrano-Baena María M. , Triviño-Tarradas Paula , Ruiz-Díaz Carlos , Hidalgo Fernández Rafael E.
Published: August 2020

This original research paper analyses the actual and important topic of the implications of BREEAM sustainability assessment on the design of hotels and it is a personal response to “The Agenda 2030 for Sustainable Development” and its influence on the Tourism and Hospitality Industry. The paper aims to examine the influence of the sustainable assessment method BREEAM on the design of hotels by using seven case studies and studying the changes that were implemented in order to achieve their targets. Qualitative data were obtained by conducting in-depth interviews and analyzing the supplied documentation. The authors notice that the results revealed that a BREEAM approach might limit the design of the hotels but, including the right measures at the early design stage of the project, the target can be easily achieved.
Recent Literature about Urban Sprawl: A Renewed Relevance of the Phenomenon from the Perspective of Environmental Sustainability
Authors: Rubiera-Morollón Fernando , Garrido-Yserte Ruben
Published: August 2020

The urban sprawl phenomenon has attracted the attention of social researchers since the mid-20th century. It seemed that all relevant aspects had been extensively studied and that it would be difficult to produce new studies with significant contributions. However, in the last decade, we have witnessed a revival of the literature on urban sprawl for three main reasons: (i) the existence of new methodologies to measure the phenomenon based on digital cartography and geo-referenced information, (ii) new hypotheses about the relevance of the formation of metropolitan areas not institutionally integrated into urban sprawl in many places and, mainly, (iii) the role of urban density in the environmental sustainability of cities. The recent literature on this third aspect has grown the most and around which it seems that new and interesting lines of future research will develop. The objective of this work is to present a synthetic review of the most recent literature on urban sprawl as of the end of the second decade of the XXI century. This review can serve to recapitulate the growing consensus that is being formed on the lower environmental sustainability of low-density cities and diffuse limits.
An IoT Smart Infrastructure for S. Domenico Church in Matera’s “Sassi’’: A Multiscale Perspective to Built Heritage Conservation
Authors: Lerario Antonella , Varasano Antonietta
Published: August 2020

Architectural heritage is perhaps the most important marker of the Italian and European landscapes. Over the last decades, its strategic relevance for local economic development has led to prioritize tourism-oriented promotion objectives. Therefore, new light has been thrown on once unknown resources that have thus received the attention of tourists interested in new visit experiences. To this end, Information and Communication Technologies (ICTs) have delivered a crucial support mainly in terms of public attraction and creation of new cultural offers. However, new urgent challenges now face tangible heritage, whose physical existence is jeopardized through extreme events and poor maintenance. Unexpected intense visit flows represent in themselves a further threat for sensitive heritages. ICTs have then to cope with more complex conservation tasks and the Internet of Things (IoT) can facilitate appropriate solutions. The paper presents a smart sensor-based infrastructure for the structural monitoring of S. Domenico Church in Matera, an emblematic city for the concerns described, which also highlighted the need for a wider conservation concept also embracing context and fruition issues. The article introduces the case study and its delicate environment, and the technological background of heritage monitoring solutions; the proposed IoT infrastructure is then described, discussing its potentialities and IoT contribution to creating more holistic and multiscale perspectives to heritage conservation.
Identification of the Factors That Affect the Environmental Administrative Burden for Businesses
Authors: Kotnik Žiga , Klun Maja , Slabe-Erker Renata
Published: August 2020

Environmental regulations bring social benefits and contribute to lessening environmental damage. At the same time, due to the rapidly changing and complex environmental legislation, businesses incur costs, including administrative burdens. The article presents quantitative evidence on the factors that affect the compliance costs of environmental regulations. For this purpose, we used a binary model of logistic regression with the following predictors: enterprise characteristics, the relevance of environmental regulations for business operations, and the impact of environmental stimulus measures on compliance costs. The results of the study suggest that medium-sized enterprises are less likely to experience the environmental administrative burden than small enterprises. However, no difference has been found between large and small enterprises. Further, we show that environmental consents are an important determinant of the environmental administrative burden and that financial environmental measures can have a positive impact thereon.
Sustainable Consumption Behavior at Home and in the Workplace: Avenues for Innovative Solutions
Authors: Banytė Jūratė , Šalčiuvienė Laura , Dovalienė Aistė , Piligrimienė Žaneta , Sroka Włodzimierz
Published: August 2020

Companies which offer innovative solutions to aid the achievement of sustainable consumption behavior of individuals in home environment gain a competitive advantage. The study aims to uncover the relationship between the engagement in sustainable consumption and sustainable consumption behavior of individuals at home and in the workplace environments enabling companies to provide innovative solutions to advance sustainability management. This research holds that sustainable consumption behavior is a process and the focus of this study is use behavior. An online survey was employed to collect data from 407 respondents in the United Kingdom. Consumers working in both private and public sectors were surveyed. Data analysis suggests that one dimension of engagement in sustainable consumption, namely, Enthusiasm and Attention, mostly influences sustainable consumption behavior at home and in the workplace. Further, females feature higher sustainable consumption behavior at home and in the workplace most of the time in comparison to males. Also, there are age differences apropos sustainable consumption behavior at home and in the workplace. Social Learning Theory and Collaborative Consumption Theory are used to raise hypotheses and explain findings. The findings lead to practical implications for companies regarding engagement and sustainable consumption behavior in both environments in terms of incentives, green product and service innovation that may be offered to individuals to enhance sustainability.