bluuebunny/crossref_metadata_2025 huggingface dataset:
A Hybrid Methodology to Minimize Freshwater Consumption during Shrimp Shell Waste Valorization Combining Multi-Contaminant Pinch Analysis and Superstructure Optimization
Authors: Quintero Viviana , Gonzalez-Quiroga Arturo , Gonzalez-Delgado Angel Darío
Published: June 2021

The conservation and proper management of natural resources constitute one of the main objectives of the 2030 Agenda for Sustainable Development designed by the Member States of the United Nations. In this work, a hybrid strategy based on process integration is proposed to minimize freshwater consumption while reusing wastewater. As a novelty, the strategy included a heuristic approach for identifying the minimum consumption of freshwater with a preliminary design of the water network, considering the concept of reuse and multiple pollutants. Then, mathematical programming techniques were applied to evaluate the possibilities of regeneration of the source streams through the inclusion of intercept units and establish the optimal design of the network. This strategy was used in the shrimp shell waste process to obtain chitosan, where a minimum freshwater consumption of 277 t/h was identified, with a reuse strategy and an optimal value of US $5.5 million for the design of the water network.
Kinetics of Non-Enzymatic Synthesis of Dipeptide Cbz-Phe-Leu with AOT Reversed Micelles
Authors: Matsumoto Michiaki , Hano Tadashi
Published: June 2021

The non-enzymatic synthesis of N-benzyloxycarbonyl-L-phenylalanyl-L-leucine (Cbz-Phe-Leu) from lipophilic N-benzyloxycarbonyl-L-phenylalanine (Cbz-Phe) and hydrophilic L-leucine (Leu), by N, N’-dicyclohexylcarbodiimide (DCC) as a condensing agent, was carried out using a reversed micellar system composed of bis(2-ethylhexyl) sodium sulfosuccinate (AOT) as a surfactant and isooctane. We successfully synthesized Cbz-Phe-Leu in a short time and investigated the effects of its operational conditions, the DCC concentration, w0, and the pH on the kinetic parameters and the maximum yields. For dipeptide synthesis, we had to add an excess of DCC with the substrates because of the side reactions of Cbz-Phe. From the pH dependency of the reactivity, a partially cationic form of Leu was better for a synthesis reaction because of the enrichment of Leu at the interface by anionic AOT. The optimum water content on the dipeptide synthesis was w0 = 28 due to the competition of the peptide synthesis and the side reactions. The maximum yield of Cbz-Phe-Leu was 0.565 at 80 h under optimum experimental conditions.
Toward the Identification of Potential α-Ketoamide Covalent Inhibitors for SARS-CoV-2 Main Protease: Fragment-Based Drug Design and MM-PBSA Calculations
Authors: Hassab Mahmoud A. El , Fares Mohamed , Amin Mohammed K. Abdel-Hamid , Al-Rashood Sara T. , Alharbi Amal , Eskandrani Razan O. , Alkahtani Hamad M. , Eldehna Wagdy M.
Published: June 2021

Since December 2019, the world has been facing the outbreak of the SARS-CoV-2 pandemic that has infected more than 149 million and killed 3.1 million people by 27 April 2021, according to WHO statistics. Safety measures and precautions taken by many countries seem insufficient, especially with no specific approved drugs against the virus. This has created an urgent need to fast track the development of new medication against the virus in order to alleviate the problem and meet public expectations. The SARS-CoV-2 3CL main protease (Mpro) is one of the most attractive targets in the virus life cycle, which is responsible for the processing of the viral polyprotein and is a key for the ribosomal translation of the SARS-CoV-2 genome. In this work, we targeted this enzyme through a structure-based drug design (SBDD) protocol, which aimed at the design of a new potential inhibitor for Mpro. The protocol involves three major steps: fragment-based drug design (FBDD), covalent docking and molecular dynamics (MD) simulation with the calculation of the designed molecule binding free energy at a high level of theory. The FBDD step identified five molecular fragments, which were linked via a suitable carbon linker, to construct our designed compound RMH148. The mode of binding and initial interactions between RMH148 and the enzyme active site was established in the second step of our protocol via covalent docking. The final step involved the use of MD simulations to test for the stability of the docked RMH148 into the Mpro active site and included precise calculations for potential interactions with active site residues and binding free energies. The results introduced RMH148 as a potential inhibitor for the SARS-CoV-2 Mpro enzyme, which was able to achieve various interactions with the enzyme and forms a highly stable complex at the active site even better than the co-crystalized reference.
Emerging Floating Photovoltaic System—Case Studies High Dam and Aswan Reservoir in Egypt
Authors: Ravichandran Nagananthini , Fayek Hady H. , Rusu Eugen
Published: June 2021

The world has a target of achieving 100% renewable energy by the end of the century. This paper presents a case study to establish a new floating photovoltaic park (FPV) in Egyptian dams. In Egypt, two hydroelectric dams, namely High Dam and Aswan Reservoir, together produce 2.65 GW in the Upper-Egypt region. The addition of 5 MW FPV for each dam is simulated using the Helioscope software application. A comparison between the performance of the dams with and without adding the FPV is presented in terms of the evaporation rate and total produced energy. A comparison between different types of FPV, namely polycrystalline, thin film and mono-crystalline in the two dams are also presented. The results show that installing FPV in the Egyptian dams will drive the dams to better performance in terms of carbon dioxide reduction, water-saving from reducing evaporation and increasing hydropower generation.
Effect of the Inlet Gas Void Fraction on the Work Performance of the Multiphase Pump at Different Cavitation Stages
Authors: Shi Guangtai , Dan Yue , Liu Xiaobing , Shu Zekui
Published: June 2021

The inlet gas void fraction (IGVF) has a great effect on the power performance of the multiphase pump, and the effect is even greater under the cavitation condition. To reveal the effect of the IGVF on the cavitation evolution and the work performance of the multiphase pump at different cavitation stages, the cavitation flow was calculated numerically for the pump under different inlet gas void fractions (IGVFs) of 0%, 10% and 20%. Meanwhile, the numerical simulation method was verified experimentally. The results showed that the increase of the IGVF could improve the cavitation performance of the multiphase pump and inhibit the increasing rate of the vapor. With the aggravation of the cavitation, the output power of the impeller decreased gradually under different IGVFs. In addition, the variation trend of the output power and the net energy gained by the fluid within each domain were exactly the same. At the same time, the position of better work performance was located in the impeller fore area at the critical and serious cavitation stages, while when the cavitation developed to the fracture cavitation, the position of better work performance moved to the impeller back area. At the fracture cavitation stage, the main work region of the multiphase pump moved from the back area to the fore area of the impeller with the increase of the IGVF. The research results are of great significance in improving the performance of the multiphase pump.
Comparative Study of a Life Cycle Assessment for Bio-Plastic Straws and Paper Straws: Malaysia’s Perspective
Authors: Moy Chun-Hung , Tan Lian-See , Shoparwe Noor Fazliani , Shariff Azmi Mohd , Tan Jully
Published: June 2021

Plastics are used for various applications, including in the food and beverage industry, for the manufacturing of plastic utensils and straws. The higher utilization of plastic straws has indirectly resulted in the significant disposal of plastic waste, which has become a serious environmental issue. Alternatively, bio-plastic and paper straws have been introduced to reduce plastic waste. However, limited studies are available on the environmental assessment of drinking straws. Life cycle assessment (LCA) studies for bio-plastic and paper straws have not been comprehensively performed previously. Therefore, the impact of both bio-plastic and paper straws on the environment are quantified and compared in this study. Parameters, such as the global warming potential (GWP), acidification potential (AP) and eutrophication potential (EP), were evaluated. The input–output data of the bio-plastic and paper straws processes from a gate-to-grave analysis were obtained from the literature and generated using the SuperPro Designer V9 process simulator. The results show that bio-plastic straws, which are also known as polylactic acid (PLA) straws, had reduced environmental impacts compared to paper straws. The outcomes of this work provide an insight into the application of bio-plastic and paper straws in effectively reducing the impact on the environment and in promoting sustainability, especially from the perspective of Malaysia.
The Relative Informativeness of Regular and E-Mini Euro/Dollar Futures Contracts and the Role of Trader Types
Authors: Malhotra Jatin , Corelli Angelo
Published: June 2021

This paper examines the relative contribution of regular and e-mini futures market to price discovery of EUR/USD futures contracts on the Chicago Mercantile Exchange (CME), using intraday data in 2010.The relative contribution to price discovery is estimated using the information share approach proposed by Hasbrouck and Gonzalo-Granger. Empirical findings indicate that regular futures market contributes significantly to the price discovery, accounting for approximately 66.5% of price discovery in the EURO/USD market. This study also examines if the regular future’s information share (IS) can be explained by the positioning of commercial and non-commercial traders. We find a positive significant relationship between IS and both the speculative trade position and hedgers trade position. The results support the conclusion that the IS of regular futures can be better explained by speculators than hedgers.
Using Multi-Resolution Satellite Data to Quantify Land Dynamics: Applications of PlanetScope Imagery for Cropland and Tree-Cover Loss Area Estimation
Authors: Pickering Jeffrey , Tyukavina Alexandra , Khan Ahmad , Potapov Peter , Adusei Bernard , Hansen Matthew C. , Lima André
Published: June 2021

The Planet constellation of satellites represents a significant advance in the availability of high cadence, high spatial resolution imagery. When coupled with a targeted sampling strategy, these advances enhance land-cover and land-use monitoring capabilities. Here we present example regional and national-scale area-estimation methods as a demonstration of the integrated and efficient use of mapping and sampling using public medium-resolution (Landsat) and commercial high resolution (PlanetScope) imagery. Our proposed method is agnostic to the geographic region and type of land cover and change, which is demonstrated by applying the method across two very different geographies and thematic classes. Wheat extent is estimated in Punjab, Pakistan, for the 2018/2019 growing season, and tree-cover loss area is estimated over Peru for 2017 and 2018. We used a time series of PlanetScope imagery to classify a sample of 5 × 5 km blocks for each region and produce area estimates of 55,947 km2 (±9.0%) of wheat in Punjab and 5398 km2 (±9.1%) of tree-cover loss in Peru. We also demonstrate the use of regression estimation utilizing population information from Landsat-based maps to reduce standard errors of the sample-based estimates. Resulting regression estimates have SEs of 3.6% and 5.1% for Pakistan and Peru, respectively. The combination of daily global coverage and high spatial resolution of Planet imagery improves our ability to monitor crop phenology and capture ephemeral tree-cover loss and degradation dynamics, while Landsat-based maps provide wall-to-wall information to target the sample and increase precision of the estimates through the use of regression estimation.
Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine
Authors: Somasundaram Deepakrishna , Zhang Fangfang , Ediriweera Sisira , Wang Shenglei , Yin Ziyao , Li Junsheng , Zhang Bing
Published: June 2021

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (ZSD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. ZSD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean ZSD. Notable intra-annual variations were also indicating the highest and lowest ZSD during the north-east monsoon and south-west monsoon, respectively. The highest ZSD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on ZSD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the ZSD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies.
Evaluation of the Performances of Radar and Lidar Altimetry Missions for Water Level Retrievals in Mountainous Environment: The Case of the Swiss Lakes
Authors: Frappart Frédéric , Blarel Fabien , Fayad Ibrahim , Bergé-Nguyen Muriel , Crétaux Jean-François , Shu Song , Schregenberger Joël , Baghdadi Nicolas
Published: June 2021

Radar altimetry is now commonly used to provide long-term monitoring of inland water levels in complement to or for replacing disappearing in situ networks of gauge stations. Recent improvements in tracking and acquisition modes improved the quality the water retrievals. The newly implemented Open Loop mode is likely to increase the number of monitored water bodies owing to the use of an a priori elevation, especially in hilly and mountainous areas. The novelty of this study is to provide a comprehensive evaluation of the performances of the past and current radar altimetry missions according to their acquisition (Low Resolution Mode or Synthetic Aperture Radar) and tracking (close or open loop) modes, and acquisition frequency (Ku or Ka) in a mountainous area where tracking losses of the signal are likely to occur, as well as of the recently launched ICESat-2 and GEDI lidar missions. To do so, we evaluate the quality of water level retrievals from most radar altimetry missions launched after 1995 over eight lakes in Switzerland, using the recently developed ALtimetry Time Series software, to compare the performances of the new tracking and acquisition modes and also the impact of the frequency used. The combination of the Open Loop tracking mode with the Synthetic Aperture Radar acquisition mode on SENTINEL-3A and B missions outperforms the classical Low Resolution Mode of the other missions with a lake observability greater than 95%, an almost constant bias of (−0.17 ± 0.04) m, a RMSE generally lower than 0.07 m and a R most of the times higher than 0.85 when compared to in situ gauge records. To increase the number of lakes that can be monitored and the temporal sampling of the water level retrievals, data acquired by lidar altimetry missions were also considered. Very accurate results were also obtained with ICESat-2 data with RMSE lower than 0.06 and R higher than 0.95 when compared to in situ water levels. An almost constant bias (0.42 ± 0.03) m was also observed. More contrasted results were obtained using GEDI. As these data were available on a shorter time period, more analyses are necessary to determine their potential for retrieving water levels.
Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images
Authors: Waldner François , Diakogiannis Foivos I. , Batchelor Kathryn , Ciccotosto-Camp Michael , Cooper-Williams Elizabeth , Herrmann Chris , Mata Gonzalo , Toovey Andrew
Published: June 2021

Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics.
ISAR Imaging for Maneuvering Targets with Complex Motion Based on Generalized Radon-Fourier Transform and Gradient-Based Descent under Low SNR
Authors: Yang Zhijun , Li Dong , Tan Xiaoheng , Liu Hongqing , Liu Yuchuan , Liao Guisheng
Published: June 2021

The existing inverse synthetic aperture radar (ISAR) imaging algorithms for ship targets with complex three-dimensional (3D) rotational motion are not applicable because of continuous change of image projection plane (IPP), especially under low signal-to-noise-ratio (SNR) condition. To overcome this obstacle, an efficient approach based on generalized Radon Fourier transform (GRFT) and gradient-based descent optimal is proposed in this paper. First, the geometry and signal model for nonstationary IPP of ship targets with complex 3-D rotational motion is established. Furthermore, the two-dimensional (2D) spatial-variant phase errors caused by complex 3-D rotational motion which can seriously blur the imaging performance are derived. Second, to improve the computational efficiency for 2-D spatial-variant phase errors compensation, the coarse motion parameters of ship targets are estimated via the GRFT method. In addition, using the gradient-based descent optimal method, the global optimum solution is iteratively estimated. Meanwhile, to solve the local extremum for cost surface obtained via conventional image entropy, the image entropy combined with subarray averaging is applied to accelerate the global optimal convergence. The main contributions of the proposed method are: (1) the geometry and signal model for ship targets with a complex 3-D rotational motion under nonstationary IPP are established; (2) the image entropy conjunct with subarray averaging operation is proposed to accelerate the global optimal convergence; (3) the proposed method can ensure the imaging accuracy even with high imaging efficiency thanks to the sole optimal solution generated by using the subarray averaging and image entropy. Several experiments using simulated and electromagnetic data are performed to validate the effectiveness of the proposed approach.
Machine Learning Estimation of Fire Arrival Time from Level-2 Active Fires Satellite Data
Authors: Farguell Angel , Mandel Jan , Haley James , Mallia Derek V. , Kochanski Adam , Hilburn Kyle
Published: June 2021

Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters.
Occurrence of GPS Loss of Lock Based on a Swarm Half-Solar Cycle Dataset and Its Relation to the Background Ionosphere
Authors: Pezzopane Michael , Pignalberi Alessio , Coco Igino , Consolini Giuseppe , De Michelis Paola , Giannattasio Fabio , Marcucci Maria Federica , Tozzi Roberta
Published: June 2021

This paper discusses the occurrence of Global Positioning System (GPS) loss of lock events obtained by considering total electron content (TEC) measurements carried out by the three satellites of the European Space Agency Swarm constellation from December 2013 to December 2020, which represents the longest dataset ever used to perform such an analysis. After describing the approach used to classify a GPS loss of lock, the corresponding occurrence is analyzed as a function of latitude, local time, season, and solar activity to identify well-defined patterns. Moreover, the strict relation of the occurrence of the GPS loss of lock events with defined values of both the rate of change of electron density index (RODI) and the rate of change of TEC index (ROTI) is highlighted. The scope of this study is, on one hand, to characterize the background conditions of the ionosphere for such events and, on the other hand, to pave the way for their possible future modeling. The results shown, especially the fact that GPS loss of lock events tend to happen for well-defined values of both RODI and ROTI, are of utmost importance in the light of Space Weather effects mitigation.
An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification
Authors: Alkhelaiwi Munirah , Boulila Wadii , Ahmad Jawad , Koubaa Anis , Driss Maha
Published: June 2021

Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.
Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series
Authors: Wibowo Sandy Budi , Hadmoko Danang Sri , Isnaeni Yunus , Farda Nur Mohammad , Putri Ade Febri Sandhini , Nurani Idea Wening , Supangkat Suhono Harso
Published: June 2021

Lombok Island in Indonesia was hit by four major earthquakes (6.4 Mw to 7 Mw) and by at least 818 earthquakes between 29 July and 31 August 2018. The aims of this study are to measure ground deformation due to the 2018 Lombok earthquake series and to map its spatio-temporal distribution. The application of DinSAR was performed to produce an interferogram and deformation map. Time series Sentinel-1 satellite imageries were used as master and slave for each of these four major earthquakes. The spatio-temporal distribution of the ground deformation was analyzed using a zonal statistics algorithm in GIS. It focused on the overlapping area between the raster layer of the deformation map and the polygon layer of six observation sites (Mataram City, Pamenang, Tampes, Sukadana, Sembalun, and Belanting). The results showed that the deformation includes uplift and subsidence. The first 6.4 Mw foreshock hitting on 29 July 2018 produces a minimum uplift effect on the island. The 7.0 Mw mainshock on 5 August 2018 causes extreme uplift at the northern shore. The 6.2 Mw Aftershock on 9 August 2018 generates subsidence throughout the study area. The final earthquake of 6.9 Mw on 19 August 2018 initiates massive uplift in the study area and extreme uplift at the northeastern shore. The highest uplift reaches 0.713 m at the northern shore, while the deepest subsidence is measured −0.338 m at the northwestern shore. Dominant deformation on the northern area of Lombok Island indicates movement of Back Arc Trust in the north of the island. The output of this study would be valuable to local authorities to evaluate existing earthquake’s impacts and to design mitigation strategies to face earthquake-induced ground displacement.
Field Test and Numerical Simulation on the Long-Term Thermal Response of PHC Energy Pile in Layered Foundation
Authors: Zhang Guozhu , Cao Ziming , Liu Yiping , Chen Jiawei
Published: June 2021

Investigation on the long-term thermal response of precast high-strength concrete (PHC) energy pile is relatively rare. This paper combines field experiments and numerical simulations to investigate the long-term thermal properties of a PHC energy pile in a layered foundation. The major findings obtained from the experimental and numerical studies are as follows: First, the thermophysical ground properties gradually produce an influence on the long-term temperature variation. For the soil layers with relatively higher thermal conductivity, the ground temperature near to the energy pile presents a slowly increasing trend, and the ground temperature response at a longer distance from the center of the PHC pile appears to be delayed. Second, the short- and long-term thermal performance of the PHC energy pile can be enhanced by increasing the thermal conductivity of backfill soil. When the thermal conductivities of backfill soil in the PHC pile increase from 1 to 4 W/(m K), the heat exchange amounts of energy pile can be enhanced by approximately 30%, 79%, 105%, and 122% at 1 day and 20%, 47%, 59%, and 66% at 90 days compared with the backfill water used in the site. However, the influence of specific heat capacity of the backfill soil in the PHC pile on the short-term or long-term thermal response can be ignored. Furthermore, the variation of the initial ground temperature is also an important factor to affect the short-and-long-term heat transfer capacity and ground temperature variation. Finally, the thermal conductivity of the ground has a significant effect on the long-term thermal response compared with the short-term condition, and the heat exchange rates rise by about 5% and 9% at 1 day and 21% and 37% at 90 days as the thermal conductivities of the ground increase by 0.5 and 1 W/(m K), respectively.
Development and Validation of an Analytical Method for Deacetylasperulosidic Acid, Asperulosidic Acid, Scopolin, Asperuloside and Scopoletin in Fermented Morinda citrifolia L. (Noni)
Authors: Choi Sun-Il , Kwon Hee-Yeon , La Im-Joung , Jo Yeon-Hui , Han Xionggao , Men Xiao , Lee Se-Jeong , Kim Yong-Deok , Seong Geum-Su , Lee Ok-Hwan
Published: June 2021

Fermentation is a technology that enhances biologically active ingredients, improves the absorption rate and induces the generation of new functional ingredients by the catalytic action of enzyme systems possessed by microorganisms. In this study, changes in the content of five kinds of bioactive compounds (deacetylasperulosidic acid, asperulosidic acid, scopolin, asperuloside and scopoletin) of Morinda citrifolia L. were confirmed by fermentation, and a high-performance liquid chromatography-photodiode array (HPLC-PDA) analysis method for measuring analytes was developed and validated. HPLC method for the determination of five bioactive compounds in Morinda citrifolia L. extracts (MCE) was validated in terms of sensitivity, linearity, selectivity, limit of detection (LOD) and quantification (LOQ), precision and accuracy. The coefficient of determination of the calibration curve for bioactive compounds (1.56–100 μg/mL) showed linearity (R2 ≥ 0.9999). LOD and LOQ were in the range 0.04–0.97 and 0.13–2.95 μg/mL, respectively. The range of intra- and intraday accuracies values (recovery) were 97.5–121.9% and 98.8–118.1%, respectively, and precision value (RSDs) of the bioactive compounds were <4%. In addition, changes in the content of five bioactive compounds in MCE by fermentation were confirmed. These results indicate that the developed fermentation and analysis method could be applied in the development of potential functional food ingredients.
CE Marking of Construction Products—Evolution of the European Approach to Harmonisation of Construction Products in the Light of Environmental Sustainability Aspects
Authors: Wall Sebastian
Published: June 2021

European harmonisation of construction products provides a uniform expression of performance aspects relevant for essential characteristics coming from notified technical building regulations of the EU Member States. Since the current regulation has been in force for over seven years, this study evaluates further possibilities of its evolution, including a more efficient approach to implementing environmental sustainability aspects. The provided research is based on qualitative analysis of the past and current legislation, official documents, related guidance, judgements, scientific articles and the author’s practical experience coming from participation in the European committees, organisations and standardisation activities. Various legislative techniques and regulatory tools that could be potentially used to review the Construction Products Regulation are analysed and compared with regards to their impact on the inclusion of environmental sustainability principles. Therefore, the objective of this research is to provide substantive grounds that can be directly or indirectly used in the policymaking processes on the European and national level.
Case Study of the Integration of Digital Competencies into Teacher Preparation
Authors: Záhorec Ján , Hašková Alena , Poliaková Adriana , Munk Michal
Published: June 2021

The requirements imposed on schools and the competencies of teachers change depending on the development of society, and currently their constant growth is considerable. These facts lead to the need to continuously innovate pre-service teacher training, especially with a focus on creating professional digital literacy. The creation of a proposal of an optimal model of pre-service teacher training in the field of teacher trainees’ professional didactic-technological competency development was the subject of the research, which is described in the article. The described research examined the importance of the integration of various kinds of digital didactic tools into pre-service teacher training curricula with regard to the successful performance of the teaching profession. The necessary research data were obtained on the basis of screening the opinions of teacher trainees in Slovakia and the Czech Republic (n = 280). The respondents of the research survey expressed, in terms of various aspects, their opinions on the importance of integrating the issue of working with specified kinds of the given digital means into the curricula of teacher trainees’ study programs. The obtained research data were analysed depending on three segmentation factors of the respondents, which were the nationality of the student (i.e., the COUNTRY of his/her study), the GENDER of the respondent, and the combination of these two factors, i.e., COUNTRY X GENDER. According to the achieved results, there is a need to include or strengthen the teaching of software applications such as ActivInspire, FreeMind, SMART Notebook, Google Docs and, if possible, Prezi and Mindomo, and also a need to emphasize the methodological aspects of the use of these technical means in teaching.
Developing and Applying a Model for Evaluating Risks Affecting Greening Existing Buildings
Authors: Issa Usama , Sharaky Ibrahim , Alwetaishi Mamdooh , Balabel Ashraf , Shamseldin Amal , Abdelhafiz Ahmed , Al-Surf Mohammed , Al-Harthi Mosleh , Osman Medhat M. A.
Published: June 2021

Improving building performance through reducing negative environmental impacts can be achieved by greening existing buildings (GEB), which is considered a very important sustainability process. Due to the risky and uncertain nature of the process of GEB, a growing amount of attention should be given to eliminating the effects of risks on GEB. This research aims to identify most expected risk factors related to GEB, as well as to evaluate their effects through calculating risk factor characteristics, such as risk factor presence (RFP), impact on the GEB process (IGEB), and impact on building performance in the long run (IBP), as new indices describe these risks. Sixty-six risk factors were categorized in seven risk groups related to the economic aspect, social aspect, environmental aspect, managerial aspect, sustainability operation, sustainable design, and renovation. Moreover, a fuzzy model for risk analysis was developed to combine the multi-effects of the aforementioned three risk factor characteristics in one index representing the risk factors’ overall importance. The model was applied and verified for data collected in Saudi Arabia. The results of this study showed that the most important risk group is the greening process of environmental control, while the least important is the greening process of renovation and construction. Using the proposed model improved the results of evaluating risks affecting GEB through merging the multi-effects of risk factor characteristics. The results and analysis proved that the most important key risk factors were environmental in nature. An intricate relationship of the impacts on the GEB process and building performance with the overall importance of the risk factors was clearly found. The decision makers who deal with greening projects in Saudi Arabia should be aware of the key risks identified in this study. The proposed methodology and model can be easily applied to other countries to help decision makers in evaluating their GEB projects, as well as comparing more greening projects based on risk analysis.
A Tourism Potential Index for Cultural Heritage Management through the Ecosystem Services Approach
Authors: Ramírez-Guerrero Gema , García-Onetti Javier , Arcila-Garrido Manuel , Chica-Ruiz Juan Adolfo
Published: June 2021

Tourism as a tool for the enhancement and conservation of heritage represents an opportunity for many managers of cultural properties. However, despite the numerous works developed so far on tourism governance, the elaboration of preliminary studies for decision-making in heritage buildings is still a challenge. Considering the ecosystem services approach, an index is proposed which allows tourism and cultural heritage managers to analyze and quantify the level of tourist exploitation (use) of a monument for the services (benefits) that it offers to society. In this paper, a multi-criteria evaluation system was proposed, related to the main use of the building and its relationship with various tourism components, which have been classified as cultural, leisure, recreational, lodging, catering, intermediation, transportation, and event organization. The model has been applied to the Parroquia del Mar (Alicante, Spain) of great cultural relevance, but not exploited for tourism. The results obtained demonstrate the usefulness and validity of the model, considered as a tool capable of bridging the gap between heritage conservation and its touristic use, highlighting the importance of the services offered as necessary attributes for its (re)valuation, combining the social benefits of its exploitation, its touristic use and the awareness of its conservation and protection.
Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates
Authors: Ibrahim Shewkar , Sayed Tarek
Published: June 2021

The Data Driven Approaches to Crime and Traffic Safety approach identifies opportunities where a single enforcement deployment can achieve multiple objectives: reduce collision and crime rates. Previous research focused on modeling both events separately despite evidence suggesting a high correlation. Additionally, there is a limited understanding of the impact of Mobile Automated Enforcement (MAE) on crime or the impact of changing a deployment strategy on collision and crime dates. For this reason, this study categorized MAE deployment into three different clusters. A random-parameter multivariate Tobit model was developed under the Bayesian framework to understand the impact of changing the deployment on collision and crime rates in a neighborhood. Firstly, the results of the analysis quantified the high correlation between collision and crime rates (0.86) which suggest that locations with high collision rates also coincide with locations with high crime rates. The results also demonstrated the safety effectiveness (i.e., reduced crime and collision rates) increased for the clusters that are associated with an increased enforcement duration at a neighborhood level. Understanding how changing the deployment strategy at a macro-level affects collision and crime rates provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources.
Analysis and Prediction of Ecosystem Service Values Based on Land Use/Cover Change in the Yiluo River Basin
Authors: Hou Jun , Qin Tianlin , Liu Shanshan , Wang Jianwei , Dong Biqiong , Yan Sheng , Nie Hanjiang
Published: June 2021

Ecosystem service values are closely related to land use/cover change, however, the values affected by land use/cover change in the context of climate variability remain unclear. Based on the land use/cover data of 2000, 2010, and 2020 in the Yiluo River Basin, we quantitatively analyzed the impacts of historical land use/cover change on the ecosystem service values. Then the future land use simulation model was applied to predict the land use/cover distribution in 2030 under three Representative Concentration Pathways scenarios, and the influences on ecosystem service values were analyzed further. We found that the total ecosystem service values in the Yiluo River Basin presented a growth from 9217 million dollars (2000) to 9676 million dollars (2020), which attributed to the increase of forestland and water bodies in recent years. By 2030, the total ecosystem service values continued to present an upward trend, while also showing a difference under three scenarios, this discrepancy was mainly caused by different precipitation conditions. With the introduction of the ecological protection and high-quality development of the Yellow River basin in the new period, climate change may be the main factors affecting the ecological field in the future.
Shared Learning from the Implementation of a Technical Leadership Program
Authors: Sharma Ankur , Bodger Christopher , Elahi Abdullah , Halbich Robert , Jyoti Harshita , Kennedy Rowan , Kravis Ruth , Law Bryan Meng-Yao , Li Allissa , Lim David , Lu Elton , Luu Christopher , Patajo Angela , Sigal Arina , Wells Vivienne , Browne Chris A
Published: June 2021

Capstone projects have been an established vehicle of student–industry engagement in a university setting. In engineering, capstone projects are a point of transition from student to professional, as student engineers gain practical work experience and apply their technical skills and knowledge for an industry-proposed design, research, and/or development challenge. In this paper, we report on a supplementary program—the Technical Leadership Program (TLP)—which was designed and delivered for students to hone their technical leadership skills within a capstone project. To report on the outcomes of this case study, we use a student-as-partners philosophy to share the case study as a series of vignettes from co-authors who participated in the TLP. These have been collated into four key thematic areas including building awareness through technical leadership; building a personal repertoire of leadership; creating a common platform for collaboration; and transitioning into a professional workplace environment. The reflections are then shown to map to relevant competencies in technical leadership. Alongside these themes, the paper shares the design of the TLP delivery and reflects on the challenges and benefits of this mechanism to enhance student experience.
Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future
Authors: Sun Xiaoqian , Wandelt Sebastian
Published: June 2021

Air transportation systems are an important part of the critical infrastructure in our connected world. Accordingly, a better understanding and improvements in the resilience of the overall air transportation system are essential to the well-functioning of our society and overall sustainability of human beings. In the literature, network science is increasingly used to better understand the resilience dynamics of air transportation. Given the wide application of tools for network science and the importance of designing resilient air transportation systems, a rich body of studies has emerged in recent years. This review paper synthesizes the related literature that has been published throughout the last 15 years regarding the robustness of air transportation systems. The contributions of this work consist of two major elements. The first part provides a comprehensive discussion and cross-comparison of the reported results. We cover several major topics, including node importance identification, failure versus attack profiles, recovery and improvement techniques, and networks of networks approaches. The second part of this paper complements the review of aggregated findings by elaborating on a future agenda for robust air transportation research. Our survey-style overview hopefully contributes toward a better understanding of the state of the art in this research area, and, in turn, to the improvement of future air transportation resilience and sustainability.
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization
Authors: Hamrang Farzad , Mahmoudi S. M. Seyed , Rosen Marc A.
Published: June 2021

Based on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas combustor, where the syngas produced in the gasifier is burned in the combustion chamber and fed to a gas turbine directly. The energy discharged from the gas turbine is utilized for further electricity and freshwater generation via Kalina and MED hybridization. The proposed system is analyzed from energy, exergy, exergoeconomic, and reliability–availability viewpoints. The optimal operating condition and optimum performance criteria are obtained by hybridizing an artificial neural network (ANN), the multi-objective particle swarm optimization (MOPSO) algorithm. According to results obtained, for the fourth scenario of the optimization process, optimal values of 45.10%, 14.27 kg·s−1, 12.95 USD·GJ−1, and 8141 kW are obtained for the exergy efficiency, freshwater production rate, sum unit cost of products, and net output power, respectively. According to reliability and availability assessment, the probability of the healthy working state of all components and subsystems is 88.4403%; the system is shown to be 87.74% available of the time over the 20-year lifetime.