bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating Ex Vivo Animal Models to Test the Performance of Intravitreal Liposomal Drug Delivery Systems
Authors: Christensen Gustav , Barut Leon , Urimi Dileep , Schipper Nicolaas , Paquet-Durand François
Published: July 2021

There is a strong need for innovative and efficient drug delivery systems for ocular therapy development. However, testing intravitreal drug delivery systems without using live animals is challenging. Ex vivo animal models offer an interesting alternative. We analyzed the potential of using fresh porcine eyes obtained from the local slaughterhouse as a model for testing the intravitreal biodistribution and retention of liposomes with or without polyethylene glycol (PEG) conjugation and with different surface charges. The histology of the eyes was analyzed to localize the liposomes, and it was found that liposomes with PEG absorbed rapidly on the retina (within 1 h), with positively charged and PEG-coated liposomes being retained for at least 24 h. In parallel, fluorophotometry was employed on intact eyes, to determine the pharmacokinetics of the fluorophore calcein, as a substitute for a small hydrophilic therapeutic compound. We found a 4.5-fold increase in the vitreous half-life of calcein loaded in liposomes, compared with the free solution. Retinal toxicity was addressed using murine-derived retinal explant cultures. Liposomes were non-toxic up to 500 µg/mL. Toxicity was observed at 5 mg/mL for anionic and cationic liposomes, with 2-fold and 2.5-fold increased photoreceptor cell death, respectively. Overall, we could show that important ocular drug delivery considerations such as pharmacokinetics and biodistribution can be estimated in ex vivo porcine eyes, and may guide subsequent in vivo experiments.
Assessment of Morpho-Physiological and Biochemical Responses of Mercury-Stressed Trigonella foenum-gracum L. to Silver Nanoparticles and Sphingobacterium ginsenosidiumtans Applications
Authors: Khalofah Ahlam , Kilany Mona , Migdadi Hussein
Published: July 2021

Heavy metals are primarily generated and deposited in the environment, causing phytotoxicity. This work evaluated fenugreek plants’ morpho-physiological and biochemical responses under mercury stress conditions toward Ag nanoparticles and Sphingobacterium ginsenosidiumtans applications. The fabrication of Ag nanoparticles by Thymus vulgaris was monitored and described by UV/Vis analysis, FTIR, and SEM. The effect of mercury on vegetative growth was determined by measuring the root and shoots length, the number and area of leaves, the relative water content, and the weight of the green and dried plants; appraisal of photosynthetic pigments, proline, hydrogen peroxide, and total phenols content were also performed. In addition, the manipulation of Ag nanoparticles, S. ginsenosidiumtans, and their combination were tested for mercury stress. Here, Ag nanoparticles were formed at 420 nm with a uniform cuboid form and size of 85 nm. Interestingly, the gradual suppression of vegetal growth and photosynthetic pigments by mercury, Ag nanoparticles, and S. ginsenosidiumtans were detected; however, carotenoids and anthocyanins were significantly increased. In addition, proline, hydrogen peroxide, and total phenols content were significantly increased because mercury and S. ginsenosidiumtans enhance this increase. Ag nanoparticles achieve higher levels by the combination. Thus, S. ginsenosidiumtans and Ag nanoparticles could have the plausible ability to relieve and combat mercury’s dangerous effects in fenugreek.
Towards the Well-Tempered Chloroplast DNA Sequences
Authors: Turudić Ante , Liber Zlatko , Grdiša Martina , Jakše Jernej , Varga Filip , Šatović Zlatko
Published: July 2021

With the development of next-generation sequencing technology and bioinformatics tools, the process of assembling DNA sequences has become cheaper and easier, especially in the case of much shorter organelle genomes. The number of available DNA sequences of complete chloroplast genomes in public genetic databases is constantly increasing and the data are widely used in plant phylogenetic and biotechnological research. In this work, we investigated possible inconsistencies in the stored form of publicly available chloroplast genome sequence data. The impact of these inconsistencies on the results of the phylogenetic analysis was investigated and the bioinformatic solution to identify and correct inconsistencies was implemented. The whole procedure was demonstrated using five plant families (Apiaceae, Asteraceae, Campanulaceae, Lamiaceae and Rosaceae) as examples.
Precise Tuning of Polymeric Fiber Dimensions to Enhance the Mechanical Properties of Alginate Hydrogel Matrices
Authors: Li Zehua , Pearce Amanda K. , Dove Andrew P. , O’Reilly Rachel K.
Published: July 2021

Hydrogels based on biopolymers, such as alginate, are commonly used as scaffolds in tissue engineering applications as they mimic the features of the native extracellular matrix (ECM). However, in their native state, they suffer from drawbacks including poor mechanical performance and a lack of biological functionalities. Herein, we have exploited a crystallization-driven self-assembly (CDSA) methodology to prepare well-defined one-dimensional micellar structures with controlled lengths to act as a mimic of fibrillar collagen in native ECM and improve the mechanical strength of alginate-based hydrogels. Poly(ε-caprolactone)-b-poly(methyl methacrylate)-b-poly(N, N-dimethyl acrylamide) triblock copolymers were self-assembled into 1D cylindrical micelles with precise lengths using CDSA epitaxial growth and subsequently combined with calcium alginate hydrogel networks to obtain nanocomposites. Rheological characterization determined that the inclusion of the cylindrical structures within the hydrogel network increased the strength of the hydrogel under shear. Furthermore, the strain at flow point of the alginate-based hydrogel was found to increase with nanoparticle content, reaching an improvement of 37% when loaded with 500 nm cylindrical micelles. Overall, this study has demonstrated that one-dimensional cylindrical nanoparticles with controlled lengths formed through CDSA are promising fibrillar collagen mimics to build ECM scaffold models, allowing exploration of the relationship between collagen fiber size and matrix mechanical properties.
Application of 3D Laser Image Scanning Technology and Cellular Automata Model in the Prediction of the Dynamic Process of Rill Erosion
Authors: Li Song , Li Qiqi , Chen Jian , Han Yu
Published: July 2021

Black soil areas are strongly affected by rill erosion due to the geomorphic characteristics of flood plains and heavy rainfall. To study the problem of rill erosion in black soil areas and achieve ecological restoration, based on the method of artificially simulated rainfall, the effects of rainfall intensity and slope on the characteristics of flow and sand production on the slope surface of black soil areas were studied, and the erosion pattern of the slope surface after rainfall was monitored by a 3D laser scanner to analyze the erosion of the soil on the slope surface. The slope erosion model was constructed on the basis of the cellular automata (CA) method, and the results of the model’s operation were compared with actual rainfall measurement results to deepen research on the slope erosion mechanism in black soil areas. By analyzing the slope erosion pattern after rainfall, it was found that the surface area and erosion volume of serious slope erosion areas increased with increases in slope gradient. Based on the physical model test results combined with the CA model to simulate flow and sand production on bare slopes under different rainfall intensities, comparison showed that the CA model can accurately simulate flow and sand production on a slope where the Ens coefficient of the flow production rate is between 0.70 and 0.97, thus theoretically verifying the reliability of the model, and on this basis, the erosion pattern of the slope after rainfall was predicted to explore the evolution and development law of erosion.
Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
Authors: Wang Zhihao , Brenning Alexander
Published: July 2021

Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling.
Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia
Authors: Bahari Nurul Iman Saiful , Muharam Farrah Melissa , Zulkafli Zed , Mazlan Norida , Husin Nor Azura
Published: July 2021

MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS Ts products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS Ts as much as the performance of the more complex QM-MBC method.
Fine-Grained Tidal Flat Waterbody Extraction Method (FYOLOv3) for High-Resolution Remote Sensing Images
Authors: Zhang Lili , Fan Yu , Yan Ruijie , Shao Yehong , Wang Gaoxu , Wu Jisen
Published: July 2021

The tidal flat is long and narrow area along rivers and coasts with high sediment content, so there is little feature difference between the waterbody and the background, and the boundary of the waterbody is blurry. The existing waterbody extraction methods are mostly used for the extraction of large water bodies like rivers and lakes, whereas less attention has been paid to tidal flat waterbody extraction. Extracting tidal flat waterbody accurately from high-resolution remote sensing imagery is a great challenge. In order to solve the low accuracy problem of tidal flat waterbody extraction, we propose a fine-grained tidal flat waterbody extraction method, named FYOLOv3, which can extract tidal flat water with high accuracy. The FYOLOv3 mainly includes three parts: an improved object detection network based on YOLOv3 (Seattle, WA, USA), a fully convolutional network (FCN) without pooling layers, and a similarity algorithm for water extraction. The improved object detection network uses 13 convolutional layers instead of Darknet-53 as the model backbone network, which guarantees the water detection accuracy while reducing the time cost and alleviating the overfitting phenomenon; secondly, the FCN without pooling layers is proposed to obtain the accurate pixel value of the tidal flat waterbody by learning the semantic information; finally, a similarity algorithm for water extraction is proposed to distinguish the waterbody from non-water pixel by pixel to improve the extraction accuracy of tidal flat water bodies. Compared to the other convolutional neural network (CNN) models, the experiments show that our method has higher accuracy on the waterbody extraction of tidal flats from remote sensing images, and the IoU of our method is 2.43% higher than YOLOv3 and 3.7% higher than U-Net (Freiburg, Germany).
Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers
Authors: Lu Yijie , Zhang Zhen , Shangguan Donghui , Yang Junhua
Published: July 2021

Glaciers in High Mountain Asia (HMA) have a significant impact on human activity. Thus, a detailed and up-to-date inventory of glaciers is crucial, along with monitoring them regularly. The identification of debris-covered glaciers is a fundamental and yet challenging component of research into glacier change and water resources, but it is limited by spectral similarities with surrounding bedrock, snow-affected areas, and mountain-shadowed areas, along with issues related to manual discrimination. Therefore, to use fewer human, material, and financial resources, it is necessary to develop better methods to determine the boundaries of debris-covered glaciers. This study focused on debris-covered glacier mapping using a combination of related technologies such as random forest (RF) and convolutional neural network (CNN) models. The models were tested on Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), selecting Eastern Pamir and Nyainqentanglha as typical glacier areas on the Tibetan Plateau to construct a glacier classification system. The performances of different classifiers were compared, the different classifier construction strategies were optimized, and multiple single-classifier outputs were obtained with slight differences. Using the relationship between the surface area covered by debris and the machine learning model parameters, it was found that the debris coverage directly determined the performance of the machine learning model and mitigated the issues affecting the detection of active and inactive debris-covered glaciers. Various classification models were integrated to ascertain the best model for the classification of glaciers.
Improvement of UAV Positioning Performance Based on EGNOS+SDCM Solution
Authors: Krasuski Kamil , Wierzbicki Damian , Bakuła Mieczysław
Published: July 2021

The article presents the results of research on multi-SBAS (multi-satellite-based augmentation system) positioning in UAV (unmanned aerial vehicle) technology. For this purpose, a new solution was developed for combining the UAV position navigation solution from several SBAS systems. In this particular case, the presented linear combination algorithm is based on the fusion of EGNOS (European geostationary navigation overlay service) and SDCM (system of differential correction and monitoring) positioning to determine the resultant UAV coordinates. The algorithm of the mathematical model uses weights of measurements in three ways, i.e., Variant I, the reciprocal of the number of tracked satellites from a single SBAS solution; Variant II, the inverse square of mean coordinate errors from a single SBAS solution; and Variant III, the reciprocal of UAV flight speed from a single SBAS solution. The research experiment used real GNSS (global navigation satellite system) navigation data recorded by the VTOL unmanned platform. The test flight was made in April 2020 in Poland, near Warsaw. Based on the developed research results, it was found that the highest accuracy of UAV positioning was obtained when using the weighting model for Variant II. In the weight model of Variant II, the accuracy of the solution of the UAV position increased by 1–2% for the horizontal components and 19–22% for the vertical component h, concerning the results obtained from the weighing Variants I and III. It is worth noting that the proposed research model significantly improves the results of determining the ellipsoidal height h. Compared to the arithmetic mean model, determining the h component in the Variant II weight model is improved by about 23%. The paper also shows the advantage of EGNOS+SDCM positioning over EGNOS positioning alone in determining the accuracy of the vertical component h. The obtained research results show the significant advantages of the multi-SBAS positioning model in UAV technology.
An Improved Aggregated-Mosaic Method for the Sparse Object Detection of Remote Sensing Imagery
Authors: Zhao Boya , Wu Yuanfeng , Guan Xinran , Gao Lianru , Zhang Bing
Published: July 2021

Object detection based on remote sensing imagery has become increasingly popular over the past few years. Unlike natural images taken by humans or surveillance cameras, the scale of remote sensing images is large, which requires the training and inference procedure to be on a cutting image. However, objects appearing in remote sensing imagery are often sparsely distributed and the labels for each class are imbalanced. This results in unstable training and inference. In this paper, we analyze the training characteristics of the remote sensing images and propose the fusion of the aggregated-mosaic training method, with the assigned-stitch augmentation and auto-target-duplication. In particular, based on the ground truth and mosaic image size, the assigned-stitch augmentation enhances each training sample with an appropriate account of objects, facilitating the smooth training procedure. Hard to detect objects, or those in classes with rare samples, are randomly selected and duplicated by the auto-target-duplication, which solves the sample imbalance or classes with insufficient results. Thus, the training process is able to focus on weak classes. We employ VEDAI and NWPU VHR-10, remote sensing datasets with sparse objects, to verify the proposed method. The YOLOv5 adopts the Mosaic as the augmentation method and is one of state-of-the-art detectors, so we choose Mosaic (YOLOv5) as the baseline. Results demonstrate that our method outperforms Mosaic (YOLOv5) by 2.72% and 5.44% on 512 × 512 and 1024 × 1024 resolution imagery, respectively. Moreover, the proposed method outperforms Mosaic (YOLOv5) by 5.48% under the NWPU VHR-10 dataset.
Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms
Authors: Rodríguez-Abreo Omar , Rodríguez-Reséndiz Juvenal , Velásquez Francisco Antonio Castillo , Ortiz Verdin Alondra Anahi , Garcia-Guendulain Juan Manuel , Garduño-Aparicio Mariano
Published: July 2021

The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.
Photoplethysmography behind the Ear Outperforms Electrocardiogram for Cardiovascular Monitoring in Dynamic Environments
Authors: Bradke Brian S. , Miller Tiffany A. , Everman Bradford
Published: July 2021

An increasing proportion of occupational mishaps in dynamic, high-risk operational environments have been attributed to human error, yet there are currently no devices to routinely provide accurate physiological data for insights into underlying contributing factors. This is most commonly due to limitations of commercial and clinical devices for collecting physiological data in environments of high motion. Herein, a novel Photoplethysmography (PPG) sensor device was tested, called SPYDR (Standalone Performance Yielding Deliberate Risk), reading from a behind-the-ear location, specifically designed for high-fidelity data collection in highly dynamic high-motion, high-pressure, low-oxygen, and high-G-force environments. For this study, SPYDR was installed as a functional ear-cup replacement in flight helmets worn by rated US Navy aircrew. Subjects were exposed to reduced atmospheric pressure using a hypobaric chamber to simulated altitudes of 25,000 feet and high G-forces in a human-rated centrifuge up to 9 G acceleration. Data were compared to control devices, finger and forehead PPG sensors, and a chest-mounted 12-lead ECG. SPYDR produced high-fidelity data compared to controls with little motion-artifact controls in the no-motion environment of the hypobaric chamber. However, in the high-motion, high-force environment of the centrifuge, SPYDR recorded consistent, accurate data, whereas PPG controls and ECG data were unusable due to a high-degree-motion artifacts. The data demonstrate that SPYDR provides an accurate and reliable system for continuous physiological monitoring in high-motion, high-risk environments, yielding a novel method for collecting low-artifact cardiovascular assessment data important for investigating currently inaccessible parameters of human physiology.
Systematic Review of Electricity Demand Forecast Using ANN-Based Machine Learning Algorithms
Authors: Román-Portabales Antón , López-Nores Martín , Pazos-Arias José Juan
Published: July 2021

The forecast of electricity demand has been a recurrent research topic for decades, due to its economical and strategic relevance. Several Machine Learning (ML) techniques have evolved in parallel with the complexity of the electric grid. This paper reviews a wide selection of approaches that have used Artificial Neural Networks (ANN) to forecast electricity demand, aiming to help newcomers and experienced researchers to appraise the common practices and to detect areas where there is room for improvement in the face of the current widespread deployment of smart meters and sensors, which yields an unprecedented amount of data to work with. The review looks at the specific problems tackled by each one of the selected papers, the results attained by their algorithms, and the strategies followed to validate and compare the results. This way, it is possible to highlight some peculiarities and algorithm configurations that seem to consistently outperform others in specific settings.
Detection of Triacetone Triperoxide (TATP) and Hexamethylene Triperoxide Diamine (HMTD) from the Gas Phase with Differential Ion Mobility Spectrometry (DMS)
Authors: Maziejuk Mirosław , Szyposzyńska Monika , Spławska Aleksandra , Wiśnik-Sawka Monika , Ceremuga Michał
Published: July 2021

One of the significant problems in the modern world is the detection of improvised explosives made of materials synthesized at home. Such compounds include triacetone triperoxide (TATP) and hexamethylene triperoxide diamine (HMTD). An attempt was made to construct an instrument allowing for the simultaneous detection of both compounds despite the large difference of vapor pressure: very high for TATP and very low for HMTD. The developed system uses differential ion mobility spectrometry (DMS) in combination with a specially designed gas sample injection system. The created system of detectors allowed for the detection of a high concentration of TATP and a very low concentration of HMTD. TATP detection was possible despite the presence of impurities—acetone remaining from the technological process and formed as a coproduct of diacetone diperoxide (DADP) synthesis. Ammonia added to the carrier gas improved the possibility of detecting the abovementioned explosives, reducing the intensity of the acetone signal. The obtained results were then compared with the detection capabilities of drift tube ion mobility spectrometer (DT-IMS), which has not made possible such detection as DMS.
Diagnostic of Operation Conditions and Sensor Faults Using Machine Learning in Sucker-Rod Pumping Wells
Authors: Nascimento João , Maitelli André , Maitelli Carla , Cavalcanti Anderson
Published: July 2021

In sucker-rod pumping wells, due to the lack of an early diagnosis of operating condition or sensor faults, several problems can go unnoticed. These problems can increase downtime and production loss. In these wells, the diagnosis of operation conditions is carried out through downhole dynamometer cards, via pre-established patterns, with human visual effort in the operation centers. Starting with machine learning algorithms, several papers have been published on the subject, but it is still common to have doubts concerning the difficulty level of the dynamometer card classification task and best practices for solving the problem. In the search for answers to these questions, this work carried out sixty tests with more than 50,000 dynamometer cards from 38 wells in the Mossoró, RN, Brazil. In addition, it presented test results for three algorithms (decision tree, random forest and XGBoost), three descriptors (Fourier, wavelet and card load values), as well as pipelines provided by automated machine learning. Tests with and without the tuning of hypermeters, different levels of dataset balancing and various evaluation metrics were evaluated. The research shows that it is possible to detect sensor failures from dynamometer cards. Of the results that will be presented, 75% of the tests had an accuracy above 92% and the maximum accuracy was 99.84%.
Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation
Authors: Hussein Burhan Rashid , Malik Owais Ahmed , Ong Wee-Hong , Slik Johan Willem Frederik
Published: July 2021

With the increase in the digitization efforts of herbarium collections worldwide, dataset repositories such as iDigBio and GBIF now have hundreds of thousands of herbarium sheet images ready for exploration. Although this serves as a new source of plant leaves data, herbarium datasets have an inherent challenge to deal with the sheets containing other non-plant objects such as color charts, barcodes, and labels. Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity of leaf trait extraction and analysis. Focusing on segmentation and trait extraction on individual intact herbarium leaves, this study proposes a pipeline consisting of deep learning semantic segmentation model (DeepLabv3+), connected component analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits. The proposed method achieved a higher F1-score for both the in-house dataset (96%) and on a publicly available herbarium dataset (93%) compared to object detection-based approaches including Faster R-CNN and YOLOv5. Furthermore, using the proposed approach, the phenotypic measurements extracted from the segmented individual leaves were closer to the ground truth measurements, which suggests the importance of the segmentation process in handling background noise. Compared to the object detection-based approaches, the proposed method showed a promising direction toward an autonomous tool for the extraction of individual leaves together with their trait data directly from herbarium specimen images.
A Novel Approach to the Job Shop Scheduling Problem Based on the Deep Q-Network in a Cooperative Multi-Access Edge Computing Ecosystem
Authors: Moon Junhyung , Yang Minyeol , Jeong Jongpil
Published: July 2021

In this study, based on multi-access edge computing (MEC), we provided the possibility of cooperating manufacturing processes. We tried to solve the job shop scheduling problem by applying DQN (deep Q-network), a reinforcement learning model, to this method. Here, to alleviate the overload of computing resources, an efficient DQN was used for the experiments using transfer learning data. Additionally, we conducted scheduling studies in the edge computing ecosystem of our manufacturing processes without the help of cloud centers. Cloud computing, an environment in which scheduling processing is performed, has issues sensitive to the manufacturing process in general, such as security issues and communication delay time, and research is being conducted in various fields, such as the introduction of an edge computing system that can replace them. We proposed a method of independently performing scheduling at the edge of the network through cooperative scheduling between edge devices within a multi-access edge computing structure. The proposed framework was evaluated, analyzed, and compared with existing frameworks in terms of providing solutions and services.
An Interoperable Communication Framework for Grid Frequency Regulation Support from Microgrids
Authors: Tightiz Lilia , Yang Hyosik , Bevrani Hassan
Published: July 2021

Renewable energy sources, which are controllable under the management of the microgrids with the contribution of energy storage systems and smart inverters, can support power system frequency regulation along with traditionally frequency control providers. This issue will not be viable without a robust communication architecture that meets all communication specification requirements of frequency regulation, including latency, reliability, and security. Therefore, this paper focuses on providing a communication framework of interacting between the power grid management system and microgrid central controller. In this scenario, the microgrid control center is integrated into the utility grid as a frequency regulation supporter for the main grid. This communication structure emulates the information model of the IEC 61850 protocol to meet interoperability. By employing IoT’s transmission protocol data distribution services, the structure satisfies the communication requirements for interacting in the wide-area network. This paper represents an interoperable information model for the microgrid central controller and power system management sectors’ interactions based on the IEC 61850–8–2 standard. Furthermore, we evaluate our scenario by measuring the latency, reliability, and security performance of data distribution services on a real communication testbed.
Local vs. International Hamburger Foodservice in the Consumer’s Mind: An Exploratory Study
Authors: Giachino Chiara , Terrevoli Niccolò , Bonadonna Alessandro
Published: July 2021

Fast-food chains are everywhere and every day millions of people choose to have a break in a fast-food outlet. However, in recent years some local hamburger foodservice chains outside of the well-known international fast-food chains have found success by leveraging products linked with their territory. How do consumers value the service received in an international, rather than a local, fast-food outlet? This aspect is under-investigated in the literature, but is relevant in order to capture the main and most important differences between the two systems. Through a structured survey, consumers’ perceptions of both international and local hamburger foodservice outlets in the Turin Metropolitan area (Italy) were measured and analysed. The results indicate that consumers generally have a break in an international fast-food restaurant, but the value assigned to local fast-food chains is higher than that assigned to international ones. Specifically, local fast-food chains are appreciated for particular aspects related to the supply chain (animal welfare, ethical and social aspects, the origin of the raw materials, and some other characteristics of the food). The findings contribute to a more in-depth understanding of consumer behaviour, and give an insight into the relevance of the local aspects as opposed to the international ones.
Effects of Reconstruction Planning on the Utility of Social Capital in Minamisanriku, Miyagi after the 2011 Great East Japan Earthquake
Authors: Ward Kayleigh
Published: July 2021

This mixed-methods community-based participatory research project is set in the rural coastal community of Minamisanriku, Miyagi. Ten years after the 2011 Great East Japan Earthquake, this study investigates whether and to what extent social capital acts as an asset to drive economic growth, recovery, collaboration, and decision making for residents to address social and economic problems as they perceive them. The connection between reconstruction planning and recovery is also investigated. Primary data collection methods include a conceptual social capital mapping exercise (n = 200) to document resident bonding, bridging, and linking capital relationships (n = 1994), and semi-structured interviews (n = 70) to capture how residents with high linking capital do or do not utilize it. Participant observation and secondary data analysis contextualized resident maps and interviews. Overall, the study results suggest that the utility of social capital is highly sensitive to the cultural norms of social and power hierarchies, that it fortifies in group–out group dynamics, and enables residents to address immediate needs, but lacks the ability to enable residents to use the resources and information entrenched in their social networks and other structures effectively due to intermediating issues of unequal development of social and economic infrastructure across districts and other community disparities that emerge through the reconstruction process.
Effects of Service Justice, Quality, Social Influence and Corporate Image on Service Satisfaction and Customer Loyalty: Moderating Effect of Bank Ownership
Authors: Hossain Md. Alamgir , Yesmin Most. Nirufer , Jahan Nusrat , Kim Minho
Published: July 2021

Today, the banking sector plays a significant role due to the substantial increase in the number of banks and has become an intensely competitive field. The purpose of this paper is to strengthen knowledge of retail banking services by finding the interrelationships between service justice, service quality, social influence, and corporate image concerning service satisfaction and loyalty. In addition, we sought to determine the moderating effect of bank ownership (i.e., state-owned and private sector banks) on the above relationships. Data were collected at random through online surveys that were analyzed using structural equation modeling. Empirical findings revealed that service justice and quality have a significant effect on service satisfaction and customer loyalty. Social influence has a significant effect on customer loyalty, but not on service satisfaction; however, corporate image is positively related to service satisfaction, but not to customer loyalty. Understandably, service satisfaction was assumed to have a fundamental relationship to consumer loyalty. However, moderation results indicated that state or private sector ownership of banks was an equally important moderating factor for almost all dimensions relevant to customer loyalty, other than service justice, social influence, and service satisfaction. The study presents theoretical contributions and considers the managerial implications for banking services that are potentially applicable to other financial institutions.
Vitamins and Minerals in Four Traditional Garlic Ecotypes (Allium sativum L.) from Italy: An Example of Territorial Biodiversity
Authors: Gambelli Loretta , Marconi Stefania , Durazzo Alessandra , Camilli Emanuela , Aguzzi Altero , Gabrielli Paolo , Marletta Luisa , Lisciani Silvia
Published: July 2021

Garlic (Allium sativum L.) is one of the most important food products in the world and an ancient and widespread medicinal herb. It is rich in minerals and vitamins, which are both essential nutrients for human health. This study was carried out on four traditional landraces of garlic: Rosso di Sulmona, Rosso di Proceno, Bianco Piacentino, and Rosso di Castelliri, all cultivated with the same agricultural practices in two different areas of the Lazio region. The aim was to study the effects of both production soil and genetic characteristics on the concentrations of certain micronutrients in these garlic bulbs. The content of minerals and trace elements (Ca, K, P, Mg, Na, Fe, Zn, Cu, Mn) was determined via ICP plasma after liquid washing. Water-soluble vitamins (riboflavin, niacin, thiamine, B6, vitamin C) were evaluated using an HPLC and UV detector. Among the minerals identified, potassium was most represented, with a value ranging between 645 and 1057 mg/100 g d.w., whose content is influenced by the interaction between cultivars and cultivation area, suggesting that pedoclimatic and genetic characteristics determine this content. Among vitamins, vitamin C was found to be most represented, with an amount that varied from 9.7 to 15.6 mg/100 g f.w. and did not depend on the cultivar or the geographical area. Among the B vitamins, the most abundant was vitamin B6, the maximum content of which was found in the Rosso di Castelliri cultivar grown in Viterbo (2.04 mg/100 g f.w.). Its concentration is influenced by the cultivar and the soil and their interaction. The results obtained showed good levels of essential micronutrients and highlighted that for some of them, the effect of soil typology is expressed in the presence of specific characteristics linked to the genotype.
Sustainable Smoke Extraction System for Atrium: A Numerical Study
Authors: Ivanov Martin Lyubomirov , Peng Wei , Wang Qi , Chow Wan Ki
Published: July 2021

Smoke extraction systems, either static with natural ventilation, or dynamic with mechanical ventilation are required to keep smoke layer at high levels in many tall atria. It is observed that a design fire with high heat release rate (HRR) is commonly used for designing natural vents, but a low HRR is used for mechanical ventilation system. This will not produce a sustainable environment. There are no internationally agreed on design guides to determine the HRR in the design fire for different extraction systems and scenarios. This issue will be studied using a Computational Fluid Dynamics (CFD)-based software, the Fire Dynamics Simulator (FDS) version 6.7.1. Simulations on natural smoke filling, static and dynamic smoke extractions were carried out in a big example atrium. CFD-FDS predictions were compared with previous full-scale burning tests. Results confirmed that static smoke extraction is a good option for big fires, and a dynamic system is best for small fires. A sustainable new hybrid design combining the advantages of static and dynamic systems is proposed, which could result in a lower smoke temperature and higher smoke layer interface height, indicating a better extraction design.
Understanding the New Characteristics and Development Strategies of Coastal Tourism for Post-COVID-19: A Case Study in Korea
Authors: Sohn Jun-Ik , Alakshendra Abhinav , Kim Hyun-Jung , Kim Kyu-Han , Kim Hyun-Dong
Published: July 2021

The COVID-19 outbreak has restricted international travel, halting tourism globally. Thus, travel demand has shifted from international to domestic destinations. The prolonged travel restriction has changed travel trends and travelers’ behaviors, adversely affecting the tourism industry worldwide. This study attempted to understand and examine the changes in travel preferences, such as choice of destinations, activities, and transportation modes, following the COVID-19 outbreak. This study used primary survey data of 200 respondents collected in June 2020 and secondary survey data collected by the Korea Tourism Organization in 2015 and 2017. The study also examined the role of the government in supporting strategies to prepare for the post-COVID tourism landscape. The analysis showed that the pandemic has caused travelers to favor short-haul destinations where non-contact (socially distanced) travel is possible. The study also found that the distributed land strategy that can make “untact” tourism a possibility could boost the struggling tourism industry.
State of the Art and Latest Advances in Exploring Business Models for Nature-Based Solutions
Authors: Mayor Beatriz , Toxopeus Helen , McQuaid Siobhan , Croci Edoardo , Lucchitta Benedetta , Reddy Suhana E. , Egusquiza Aitziber , Altamirano Monica A. , Trumbic Tamara , Tuerk Andreas , García Gemma , Feliu Efrén , Malandrino Cosima , Schante Joanne , Jensen Anne , López Gunn Elena
Published: July 2021

Nature-based solutions (NBS) offer multiple solutions to urban challenges simultaneously, but realising funding for NBS remains a challenge. When the concept of NBS for societal challenges was first defined by the EC in 2017, financing was recognised as one of the major challenges to its mainstreaming. The complexity of NBS finance has its origin in the multiple benefits/stakeholders involved, which obscures the argument for both public and private sector investment. Since 2017, subsequent waves of EU research- and innovation-funded projects have substantially contributed to the knowledge base of funding and business models for NBS, particularly in the urban context. Collaborating and sharing knowledge through an EU Task Force, this first set of EU projects laid important knowledge foundations, reviewing existing literature, and compiling empirical evidence of different financing approaches and the business models that underpinned them. The second set of EU innovation actions advanced this knowledge base, developing and testing new implementation models, business model tools, and approaches. This paper presents the findings of these projects from a business model perspective to improve our understanding of the value propositions of NBS to support their mainstreaming.
Application of the DEA Model in Tourism SMEs: An Empirical Study from Slovakia in the Context of Business Sustainability
Authors: Dobrovič Ján , Čabinová Veronika , Gallo Peter , Partlová Petra , Váchal Jan , Balogová Beáta , Orgonáš Jozef
Published: July 2021

Slovak spa services are not given sufficient attention directly following the support and sustainable development. The paper focuses on the evaluation of the overall development and current level of efficiency of the Slovak spas in 2013–2018, through the application of DEA models. Input variables (total number of beds, employees, medical staff) and output variables (use of bed capacity, number of treated clients) within the structure of DEA models analyzed (CCR-I, CCR-O, BCC-I, BCC-O) are determined by results of the correlation analysis. The data were obtained from the annual reports of the spa enterprises. By the results, the average efficiency score for all enterprises reached 0.7527, i.e., the average spa enterprise would need only 75.27% of currently used inputs for a given output production to move to the efficiency frontier. The development of the average efficiency score confirmed a positive growing trend until 2015; however, the efficiency decreased by 1.84% in a year-to-year comparison in 2016–2018. In each year of the analyzed period, the number of inefficient enterprises (66.67%) exceeded that of the efficient ones (33.33%). Through research carried out in spa facilities, the authors contributed to expanding the application of the DEA method in another tourism sector.