bluuebunny/crossref_metadata_2025 huggingface dataset:
Mesoscale Simulations of Polymer Solution Self-Assembly: Selection of Model Parameters within an Implicit Solvent Approximation
Authors: Park Juhae , Ramírez-Hernández Abelardo , Thapar Vikram , Hur Su-Mi
Published: March 2021

Coarse-grained modeling is an outcome of scientific endeavors to address the broad spectrum of time and length scales encountered in polymer systems. However, providing a faithful structural and dynamic characterization/description is challenging for several reasons, particularly in the selection of appropriate model parameters. By using a hybrid particle- and field-based approach with a generalized energy functional expressed in terms of density fields, we explore model parameter spaces over a broad range and map the relation between parameter values with experimentally measurable quantities, such as single-chain scaling exponent, chain density, and interfacial and surface tension. The obtained parameter map allows us to successfully reproduce experimentally observed polymer solution assembly over a wide range of concentrations and solvent qualities. The approach is further applied to simulate structure and shape evolution in emulsified block copolymer droplets where concentration and domain shape change continuously during the process.
On-Line Separation and Determination of Trivalent and Hexavalent Chromium with a New Liquid Membrane Annular Contactor Coupled to Inductively Coupled Plasma Optical Emission Spectrometry
Authors: Astolfi Maria Luisa , Ginese Domenico , Ferrante Riccardo , Marconi Elisabetta , Girelli Anna Maria , Canepari Silvia
Published: March 2021

We describe a new on-line sensitive and selective procedure for the determination of trivalent and hexavalent chromium in liquid samples by a tailor-made contactor (TMC), specifically a liquid membrane annular TMC, coupled with inductively coupled plasma with optical detection. The TMC was designed and developed to integrate the extraction and stripping phases of the analyte in one module to minimize the membrane solvent’s consumption and maximize the speed of transport through the liquid membrane. Moreover, the particular geometry studied, which consists of two coaxial hollow fibers, allows the TMC to be used for both separating and preconcentrating purposes. Both (−)-N-dodecyl-N-methylephedrinium bromide (30 mM) in dichloroethane and HNO3 (0.75 M) were used as the liquid membrane and receiving solution, respectively. The proposed method’s performance was evaluated in terms of the hexavalent chromium extraction efficiency and the coefficient of variation percentages; these were higher than 85% and less than 5%, respectively. In addition, the proposed procedure was applied to two real samples: a tap water sample and an eluate from solid urban waste. In both cases, the analytical performances were good and comparable to those obtained using synthetic standard solutions.
Discovery of Cell Aggregate-Inducing Peptides
Authors: Futaki Yudai , Amimoto Ikumi , Tanaka Megumi , Ito Tomoki , Hirano Yoshiaki
Published: March 2021

Most cells within the human body interact with neighboring cells and extracellular matrix (ECM) components to establish a unique 3D organization. These cell–cell and cell–ECM interactions form a complex communication network of biochemical and mechanical signals critical for normal cell physiology. The behavior of cells in a 3D environment is fundamentally different from that of cells in monolayer culture. Aggregation can affect cell–cell interactions, being more representative of the normal tissue microenvironment. Therefore, 3D cell culture technologies have been developed. The general method for cell aggregate is a physical method; it is difficult to control the size and number of cell aggregates. In any case, no chemical method has been discovered yet, so a new method to solve these problems is needed. In this paper, we describe the induction of a cell aggregate of the newly discovered (Lys-Pro)12(KP24) peptide. Since it was revealed that KP24 had cell aggregate-inducing activity, its derivatives were molecularly designed to clarify the importance of the KP24 sequence. We report that cell aggregations were induced by KP24 to form aggregates of fibroblast cells. We evaluated KP24 derivative periodic peptides such as (Lys-Pro-Pro)8(KPP24) and (Lys-Lys-Pro)8(KKP24). The relationship between the structure of the peptide chain and the activity induced by the cell aggregations was investigated from the viewpoint of basic research and the biomedical engineering field.
Modelling and Optimization of Processing Factors of Pumpkin Seeds Oil Extraction under Uniaxial Loading
Authors: Kabutey Abraham , Mizera Čestmír , Dajbych Oldřich , Hrabě Petr , Herák David , Demirel Cimen
Published: March 2021

In the present study, a Box–Behnken design of response surface methodology (RSM) was employed to optimize the processing factors (force: 100, 150, and 200 kN; speed: 3, 5, and 7 mm/min; and temperature: 40, 60, and 80 °C) for extracting pumpkin seeds oil under uniaxial compression. The design generated 15 experiments including twelve combinations of factors and three replicates at the center point. The responses: oil yield (%), oil expression efficiency (%), and energy (J) were calculated, and the regression models determined were statistically analyzed and validated. The optimum factors combination: 200 kN, 4 mm/min and 80 °C predicted the oil yield of 20.48%, oil expression efficiency of 60.90%, and energy of 848.04 J. The relaxation time of 12 min at the optimum factors increased the oil efficiency to 64.53%. The lower oil point force was determined to be 57.32 kN for estimating the maximum oil output. The tangent curve and generalized Maxwell models adequately (R2 = 0.996) described the compression and relaxation processes of pumpkin seeds oil extraction. Peroxide value increased with temperatures. The study provides detailed information useful for processing different bulk oilseeds under uniaxial loading for optimizing the mechanical oil pressing in large-scale oil production.
Analysis of a Series‑Parallel Resonant Converter for DC Microgrid Applications
Authors: Lin Bor-Ren
Published: March 2021

An input-series output-parallel soft switching resonant circuit with balance input voltage and primary-side current is studied and implemented for direct current (DC) microgrid system applications. Two resonant circuits are connected with input-series and output-parallel structure to have the advantages of low voltage stresses on active devices and low current stresses on power diodes. A balance capacitor is adopted on high voltage side to balance two input capacitor voltages. The LLC (inductor–inductor–capacitor) resonant circuit cells are employed in the converter to have soft switching operation for power semiconductors. The magnetic coupling component is adopted on the primary-side to automatically realize current balance of the two resonant circuits. In the end, a laboratory hardware circuit is built and tested. Experiments demonstrate and prove the validity of the resonant converter.
Synbiotic Fermented Milk with Double Fortification (Fe-Zn) as a Strategy to Address Stunting: A Randomized Controlled Trial among Children under Five in Yogyakarta, Indonesia
Authors: Helmyati Siti , Shanti Karina Muthia , Sari Fahmi Tiara , Sari Martha Puspita , Atmaka Dominikus Raditya , Pratama Rio Aditya , Wigati Maria , Wisnusanti Setyo Utami , Nisa’ Fatma Zuhrotun , Rahayu Endang Sutriswati
Published: March 2021

Stunting is one of the public health problems that has yet to be solved in Indonesia. This study developed synbiotic fermented milk with iron and zinc fortification that was then tested in a clinical setting. The product was made from skimmed milk and fructooligosaccharides (FOS) and fermented with Lactobacillus plantarum. A sample of 94 stunted children under five years old were randomly assigned to intervention or control groups. The intervention group received double-fortified synbiotic milk, while the control group drank non-fortified milk. After three months, the number of normal children in both groups, according to weight- or height-for-age z-score category, was found to be increasing. However, the difference between the two groups was not significant (p > 0.05). The study suggests that fermented milk may have a good effect on child growth. Further research is needed to deepen the potency of synbiotic fermented milk for stunted children.
Radiation: A New Multi-Disciplinary Open Access Journal for Advances in Radiation Technology
Authors: Multhoff Gabriele
Published: March 2021

Radiation is a perpetual part of our daily life [...]
“But in the Thunder, I Still Hear Thor”: The Character Athelstan as a Narrative Focal Point in the Series Vikings
Authors: Steenbakker Margaret
Published: March 2021

This article explores the way the character Athelstan serves as a narrative focal point in the popular television series Vikings. Using this series as its main case study, it addresses the question of the ways in which the character functions as a synthesis between the two opposing world views of Christianity and Norse religion that are present in the series. After establishing that Vikings is a prime example of the trend to romanticize Viking culture in popular culture, I will argue that while the character Athelstan functions as a narrative focal point in which the worlds can be united and are united for a while, his eventual death when he has reverted back to Christianity shows that the series ultimately favors Viking culture and paints a very negative picture of (medieval) Christianity indeed.
Recruitment of Employees—Assumptions of the Risk Model
Authors: Sobocka-Szczapa Halina
Published: March 2021

The aim of this article is to present the risk model premises related to worker recruitment. Recruitment affects the final selection of workers, whose activities contribute to corporate competitive advantages. Hiring unfavorable workers can influence the results produced by an organization. This risk mostly affects situations when searching for workers via the external labor market, although it can also affect internal recruitment. Therefore, it is necessary to attempt to identify recruitment risk determinants and classify their meaning in such processes. Model formation has both theoretical and intuitive characteristics. Model dependencies and their characteristics are identified in this paper. We attempted to assess the usability of the risk model for economic praxis. The analyses and results provide a model identification of dependencies between the factors determining a workers recruitment process and the risk which is caused by this process (employing inadequate workers who do not meet the employer’s expectations). The identification of worker recruitment process determinants should allow for practically reducing the risk of employing an inadequate worker and contribute to the reduction in unfavorable recruitment processes. The added value of this publication is the complex identification of recruitment process risk determinants and dependency formulations in a model form.
Risk Factor Detection and Landslide Susceptibility Mapping Using Geo-Detector and Random Forest Models: The 2018 Hokkaido Eastern Iburi Earthquake
Authors: Liu Yimo , Zhang Wanchang , Zhang Zhijie , Xu Qiang , Li Weile
Published: March 2021

Landslide susceptibility mapping is an effective approach for landslide risk prevention and assessments. The occurrence of slope instability is highly correlated with intrinsic variables that contribute to the occurrence of landslides, such as geology, geomorphology, climate, hydrology, etc. However, feature selection of those conditioning factors to constitute datasets with optimal predictive capability effectively and accurately is still an open question. The present study aims to examine further the integration of the selected landslide conditioning factors with Q-statistic in Geo-detector for determining stratification and selection of landslide conditioning factors in landslide risk analysis as to ultimately optimize landslide susceptibility model prediction. The location chosen for the study was Atsuma Town, which suffered from landslides following the Eastern Iburi Earthquake in 2018 in Hokkaido, Japan. A total of 13 conditioning factors were obtained from different sources belonging to six categories: geology, geomorphology, seismology, hydrology, land cover/use and human activity; these were selected to generate the datasets for landslide susceptibility mapping. The original datasets of landslide conditioning factors were analyzed with Q-statistic in Geo-detector to examine their explanatory powers regarding the occurrence of landslides. A Random Forest (RF) model was adopted for landslide susceptibility mapping. Subsequently, four subsets, including the Manually delineated landslide Points with 9 features Dataset (MPD9), the Randomly delineated landslide Points with 9 features Dataset (RPD9), the Manually delineated landslide Points with 13 features Dataset (MPD13), and the Randomly delineated landslide Points with 13 features Dataset (RPD13), were selected by an analysis of Q-statistic for training and validating the Geo-detector-RF- integrated model. Overall, using dataset MPD9, the Geo-detector-RF-integrated model yielded the highest prediction accuracy (89.90%), followed by using dataset MPD13 (89.53%), dataset RPD13 (88.63%) and dataset RPD9 (87.07%), which implied that optimized conditioning factors can effectively improve the prediction accuracy of landslide susceptibility mapping.
Uneven Frontiers: Exposing the Geopolitics of Myanmar’s Borderlands with Critical Remote Sensing
Authors: Bennett Mia M. , Faxon Hilary Oliva
Published: March 2021

A critical remote sensing approach illuminates the geopolitics of development within Myanmar and across its ethnic minority borderlands. By integrating nighttime light (NTL) data from 1992–2020, long-term ethnographic fieldwork, and a review of scholarly and gray literature, we analyzed how Myanmar’s economic geography defies official policy, attesting to persistent inequality and the complex relationships between state-sponsored and militia-led violence, resource extraction, and trade. While analysis of DMSP-OLS data (1992–2013) and VIIRS data (2013–2020) reveals that Myanmar brightened overall, especially since the 2010s in line with its now-halting liberalization, growth in lights was unequally distributed. Although ethnic minority states brightened more rapidly than urbanized ethnic majority lowland regions, in 2020, the latter still emitted 5.6-fold more radiance per km2. Moreover, between 2013 and 2020, Myanmar’s borderlands were on average just 13% as bright as those of its five neighboring countries. Hot spot analysis of radiance within a 50 km-wide area spanning both sides of the border confirmed that most significant clusters of light lay outside Myanmar. Among the few hot spots on Myanmar’s side, many were associated with official border crossings such as Muse, the formal hub for trade with China, and Tachileik and Myawaddy next to Thailand. Yet some of the most significant increases in illumination between 2013 and 2020 occurred in areas controlled by the Wa United State Party and its army, which are pursuing infrastructure development and mining along the Chinese border from Panghsang to the illicit trade hub of Mongla. Substantial brightening related to the “world’s largest refugee camp” was also detected in Bangladesh, where displaced Rohingya Muslims fled after Myanmar’s military launched a violent crackdown. However, no radiance nor change in radiance were discernible in areas within Myanmar where ethnic cleansing operations occurred, pointing to the limitations of NTL. The diverse drivers and implications of changes in light observed from space emphasize the need for political and economically situated remote sensing.
Influencing Factors in Estimation of Leaf Angle Distribution of an Individual Tree from Terrestrial Laser Scanning Data
Authors: Jiang Hailan , Hu Ronghai , Yan Guangjian , Cheng Shiyu , Li Fan , Qi Jianbo , Li Linyuan , Xie Donghui , Mu Xihan
Published: March 2021

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.
Integrating Hierarchical Statistical Models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological Models
Authors: Damgaard Christian
Published: March 2021

In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.
Development of a Parcel-Level Land Boundary Extraction Algorithm for Aerial Imagery of Regularly Arranged Agricultural Areas
Authors: Hong Rokgi , Park Jinseok , Jang Seongju , Shin Hyungjin , Kim Hakkwan , Song Inhong
Published: March 2021

The boundary extraction of an object from remote sensing imagery has been an important issue in the field of research. The automation of farmland boundary extraction is particularly in demand for rapid updates of the digital farm maps in Korea. This study aimed to develop a boundary extraction algorithm by systematically reconstructing a series of computational and mathematical methods, including the Suzuki85 algorithm, Canny edge detection, and Hough transform. Since most irregular farmlands in Korea have been consolidated into large rectangular arrangements for agricultural productivity, the boundary between two adjacent land parcels was assumed to be a straight line. The developed algorithm was applied over six different study sites to evaluate its performance at the boundary level and sectional area level. The correctness, completeness, and quality of the extracted boundaries were approximately 80.7%, 79.7%, and 67.0%, at the boundary level, and 89.7%, 90.0%, and 81.6%, at the area-based level, respectively. These performances are comparable with the results of previous studies on similar subjects; thus, this algorithm can be used for land parcel boundary extraction. The developed algorithm tended to subdivide land parcels for distinctive features, such as greenhouse structures or isolated irregular land parcels within the land blocks. The developed algorithm is currently applicable only to regularly arranged land parcels, and further study coupled with a decision tree or artificial intelligence may allow for boundary extraction from irregularly shaped land parcels.
An Exponential Algorithm for Bottom Reflectance Retrieval in Clear Optically Shallow Waters from Multispectral Imagery without Ground Data
Authors: Ma Yunhan , Zhang Huaguo , Li Xiaorun , Wang Juan , Cao Wenting , Li Dongling , Lou Xiulin , Fan Kaiguo
Published: March 2021

Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a novel approach for quantitatively inverting the bottom reflectance at 550 nm without the dependence of in situ bottom reflectance data or any other priori knowledge. By linking different pixels in the same image and utilizing the strong linear relationship between their water depths and the spectral related parameters, the global situation of the radiative transfer model was constrained, and an exponential relationship between the log-transformed ratio of the blue–green band reflectance and the bottom reflectance was established. The proposed model was checked by comparing the Hydrolight input bottom reflectance with that inverted from Hydrolight simulated spectrum, resulting in correlating well. Our method has successfully performed using WorldView-2 and Landsat-8 in Midway Island in the North Pacific Ocean, with the cross- and indirectly checking and obtained reliable and robust results. In addition, we assessed the potential of the quantitative bottom reflectance in benthic classification and inversion ranges under different bottom reflectance. These results indicated that compared with those methods relying on in situ data or hyperspectral imagery, our algorithm is more likely to efficiently improve the parameterization of bottom reflectance, which can be very useful for benthic habitat mapping and transferred to large-scale regions in clean reef waters, as well as monitor time-series dynamics of oceanic bottom types to forecast coral reef bleaching.
Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)
Authors: Zhang Wenmin , Brandt Martin , Prishchepov Alexander V. , Li Zhaofu , Lyu Chunguang , Fensholt Rasmus
Published: March 2021

Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.
Keypoint-Based Robotic Grasp Detection Scheme in Multi-Object Scenes
Authors: Li Tong , Wang Fei , Ru Changlei , Jiang Yong , Li Jinghong
Published: March 2021

Robot grasping is an important direction in intelligent robots. However, how to help robots grasp specific objects in multi-object scenes is still a challenging problem. In recent years, due to the powerful feature extraction capabilities of convolutional neural networks (CNN), various algorithms based on convolutional neural networks have been proposed to solve the problem of grasp detection. Different from anchor-based grasp detection algorithms, in this paper, we propose a keypoint-based scheme to solve this problem. We model an object or a grasp as a single point—the center point of its bounding box. The detector uses keypoint estimation to find the center point and regress to all other object attributes such as size, direction, etc. Experimental results demonstrate that the accuracy of this method is 74.3% in the multi-object grasp dataset VMRD, and the performance on the single-object scene Cornell dataset is competitive with the current state-of-the-art grasp detection algorithm. Robot experiments demonstrate that this method can help robots grasp the target in single-object and multi-object scenes with overall success rates of 94% and 87%, respectively.
A Sensor-Based Multichannel FES System to Control Knee Joint and Reduce Stance Phase Asymmetry in Post-Stroke Gait
Authors: Sijobert Benoît , Azevedo Christine , Pontier Joanna , Graf Sahara , Fattal Charles
Published: March 2021

Most of the studies using functional electrical stimulation (FES) in gait rehabilitation have been focused on correcting the drop foot syndrome. Using FES to control the knee joint in individuals with central nervous system (CNS) disorders could also play a key role in gait recovery: spasticity decrease, higher range of motion, positive effect on balance, limiting hyperextension and flexion in stance phase, reducing joint overload, etc. In stance phase, an accurate timing and a fine tuning of stimulation parameters are however required to provide a proper control of the knee stimulation while ensuring a safe and efficient support. In this study, 11 participants were equipped with inertial measurements units (IMU) and foot pressure insoles after supratentorial ischemic or hemorrhagic stroke, informing on knee angle and gait events used to online adapt FES during a 10 m walking protocol. Asymmetry of stance time and weight bearing were monitored as well as gait quality and physiological cost through a series of relevant markers. Vertical trunk motion has been significantly reduced during gait with FES (p-value = 0.038). Despite no significant improvement of stance phase asymmetry has been found, this preliminary work shows evidence of promising technical and rehabilitative potentials of a sensor-based multichannel FES system to control knee joint in post-stroke gait.
Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
Authors: Yeong De Jong , Velasco-Hernandez Gustavo , Barry John , Walsh Joseph
Published: March 2021

With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.
Bio-Based Sensors for Smart Food Packaging—Current Applications and Future Trends
Authors: Rodrigues Carolina , Souza Victor Gomes Lauriano , Coelhoso Isabel , Fernando Ana Luísa
Published: March 2021

Intelligent food packaging is emerging as a novel technology, capable of monitoring the quality and safety of food during its shelf-life time. This technology makes use of indicators and sensors that are applied in the packaging and that detect changes in physiological variations of the foodstuffs (due to microbial and chemical degradation). These indicators usually provide information, e.g., on the degree of freshness of the product packed, through a color change, which is easily identified, either by the food distributor and the consumer. However, most of the indicators that are currently used are non-renewable and non-biodegradable synthetic materials. Because there is an imperative need to improve food packaging sustainability, choice of sensors should also reflect this requirement. Therefore, this work aims to revise the latest information on bio-based sensors, based on compounds obtained from natural extracts, that can, in association with biopolymers, act as intelligent or smart food packaging. Its application into several perishable foods is summarized. It is clear that bioactive extracts, e.g., anthocyanins, obtained from a variety of sources, including by-products of the food industry, present a substantial potential to act as bio-sensors. Yet, there are still some limitations that need to be surpassed before this technology reaches a mature commercial stage.
Integrating a Potentiometer into a Knee Brace Shows High Potential for Continuous Knee Motion Monitoring
Authors: Büttner Christin , Milani Thomas L. , Sichting Freddy
Published: March 2021

Continuous monitoring of knee motion can provide deep insights into patients’ rehabilitation status after knee injury and help to better identify their individual therapeutic needs. Potentiometers have been identified as one possible sensor type for continuous monitoring of knee motion. However, to verify their use in monitoring real-life environments, further research is needed. We aimed to validate a potentiometer-embedded knee brace to measure sagittal knee kinematics during various daily activities, as well as to assess its potential to continuously monitor knee motion. To this end, the sagittal knee motion of 32 healthy subjects was recorded simultaneously by an instrumented knee brace and an optoelectronic reference system during activities of daily living to assess the agreement between these two measurement systems. To evaluate the potentiometer’s behavior during continuous monitoring, knee motion was continuously recorded in a subgroup (n = 9) who wore the knee brace over the course of a day. Our results show a strong agreement between the instrumented knee brace and reference system across all investigated activities as well as stable sensor behavior during continuous tracking. The presented potentiometer-based sensor system demonstrates strong potential as a device for measuring sagittal knee motion during daily activities as well as for continuous knee motion monitoring.
Predictive Factors of Recovery after an Acute Lateral Ankle Sprain: A Longitudinal Study
Authors: Terrier Philippe , Piotton Sébastien , Punt Ilona M. , Ziltener Jean-Luc , Allet Lara
Published: March 2021

A prominent feature of ankle sprains is their variable clinical course. The difficulty of providing a reliable early prognosis may be responsible for the substantial rate of poor outcomes after an ankle sprain. The aim of the present study was to evaluate the prognostic value of objective clinical measures, pain, and functional scores for ankle sprain recovery. Fifty-two participants suffering from lateral ankle sprain were included. Sprain status was assessed four weeks following injury and included evaluations of ankle range of motion, strength, function, and pain. Seven months following injury, a second assessment classified the patients into recovered and non-recovered groups using ankle ability measures. Following a predictor pre-selection procedure, logistic regressions evaluated the association between the four-week predictors and the seven-month recovery status. Twenty-seven participants (52%) fully recovered and 25 did not (48%). The results of the logistic regressions showed that walking pain was negatively associated with the probability of recovering at seven months (odds ratio: 0.71, 95% CI: 0.53–0.95). Pain four weeks after ankle sprain had relevant predictive value for long-term recovery. Special attention should be paid to patients reporting persistent pain while walking four weeks following sprain to reduce the risk of chronicity.
SDG’s Quality Education Approach: Comparative Analysis of Natural Sciences Curriculum Guidelines between Taiwan and Colombia
Authors: Molina José , Hai Nguyen Viet , Cheng Ping-Han , Chang Chun-Yen
Published: March 2021

In this comparative analysis, the cases of Taiwan and Colombia display two paths for designing a natural sciences curriculum related to quality education goals. Their differences are based on their central concepts, definitions of learning stages, delimitations of cross-subjects and cores of scientific knowledge, and alignment with international assessments. The core practices in Taiwanese curriculum guidelines are to develop inquiries, research, and experimentations to promote scientific literacy and citizenship. In contrast, the core Colombian practices construct explanations based on scientific ideas, gathering information, and using evidence. Between both countries, there is also a concordance over practices related to obtaining and communicating information. These results show the importance of curriculum policy factors such as epistemological definitions, diagnostics and representations of social expectations, alignment instruments, curriculum definitions, and design criteria of teaching processes. The differences between national curriculum policies are identified in a comparative strategy of contexts, medium-term processes to reform the national education systems or schooling features. Curriculum guidelines respond to their intellectual traditions, theoretical and pedagogical influences, and current requirements of policies. These criteria allow for the identification of cooperative issues in specific areas of science education between both countries, such as teacher education, technological and pedagogical knowledge, and curriculum alignment.
COVID-19 Pandemic, Sustainability of Macroeconomy, and Choice of Monetary Policy Targets: A NK-DSGE Analysis Based on China
Authors: Zhang Xinping , Zhang Yimeng , Zhu Yunchan
Published: March 2021

This paper studies the impact of the COVID-19 pandemic on the sustainability of Chinese economic growth, government debt, and income inequality by constructing a new Keynesian dynamic stochastic general equilibrium (NK-DSGE) model. The choice of monetary policy targets is then analyzed to hedge the impact of the pandemic. We find that: (1) the aggregate demand and labor demand shocks caused by the COVID-19 pandemic posed serious challenges to the sustainable development of the economy and debt, and increased social inequality; (2) when the impact of the pandemic is mainly reflected in the recession in aggregate demand, monetary policy should pay more attention to the target of price stability; (3) when the impact of the pandemic is mainly reflected in a decline in labor demand, monetary policy should focus more on the target of economic growth; (4) when the pandemic has a significant impact on both aggregate demand and labor demand, a monetary policy which focuses more on the target of economic growth is conducive to minimizing welfare losses. Targeted policy implications, such as selecting monetary policy targets according to different manifestations of the impact of the COVID-19 pandemic and placing emphasis on monetary policy tools to stimulate consumption, alleviate unemployment, and alleviate social inequality, are suggested to improve the sustainability of the Chinese economy.
Justice in (English) Water Infrastructure: A Systematic Review
Authors: Shrimpton Elisabeth A. , Hunt Dexter , Rogers Chris D.F.
Published: March 2021

This paper reports on a systematic review of the literature around governance and water infrastructure in England to analyse data on the application, or absence, of justice themes. It finds that, unlike in other sectors, justice thinking is far from embedded in the water sector here and whilst there are signs of a discussion there is a lack of sophistication and coherence around the debate. More positively, the research suggests that the concept of justice can be used as a tool or framework to help air and address these complex issues and in doing so is an advance on the concept of sustainability. By exploring the issues in this way, the study reveals a wealth of opportunities to use justice-thinking to improve infrastructure decision making. It is suggested a justice approach is the next step as our thinking matures beyond sustainability, improving the decisions we make for people and planet.
Contribution of Rhizobium–Legume Symbiosis in Salt Stress Tolerance in Medicago truncatula Evaluated through Photosynthesis, Antioxidant Enzymes, and Compatible Solutes Accumulation
Authors: Irshad Annie , Rehman Rana Naveed Ur , Abrar Muhammad Mohsin , Saeed Qudsia , Sharif Rahat , Hu Tianming
Published: March 2021

The effects of salt stress on the growth, nodulation, and nitrogen (N) fixation of legumes are well known, but the relationship between symbiotic nitrogen fixation (SNF) driven by rhizobium–legume symbiosis and salt tolerance in Medicago truncatula is not well studied. The effects of the active nodulation process on salt stress tolerance of Medicago truncatula were evaluated by quantifying the compatible solutes, soluble sugars, and antioxidants enzymes, as well as growth and survival rate of plants. Eight weeks old plants, divided in three groups: (i) no nodules (NN), (ii) inactive nodules (IN), and (iii) active nodules (AN), were exposed to 150 mM of NaCl salt stress for 0, 8, 16, 24, 32, 40, and 48 h in hydroponic system. AN plants showed a higher survival rate (30.83% and 38.35%), chlorophyll contents (37.18% and 44.51%), and photosynthesis compared to IN and NN plants, respectively. Improved salt tolerance in AN plants was linked with higher activities of enzymatic and nonenzymatic antioxidants and higher K+ (20.45% and 39.21%) and lower Na+ accumulations (17.54% and 24.51%) when compared with IN and NN plants, respectively. Additionally, higher generation of reactive oxygen species (ROS) was indicative of salt stress, causing membrane damage as revealed by higher electrolyte leakage and lipid peroxidation. All such effects were significantly ameliorated in AN plants, showing higher compatible solutes (proline, free amino acids, glycine betaine, soluble sugars, and proteins) and maintaining higher relative water contents (61.34%). This study advocates positive role of Rhizobium meliloti inoculation against salt stress through upregulation of antioxidant system and a higher concentration of compatible solutes.
Bridging versus Bonding Social Capital in Entrepreneurs’ Networks: The Case of Post-Conflict Western Balkans
Authors: Kopren Ana , Westlund Hans
Published: March 2021

This article examines the value and effect of social capital deriving from cross-ethnic business cooperation on social networks and society in the aftermath of war. The study contributes to a deeper understanding of the social function entrepreneurship conveys beyond its economic role. Based on Putnam’s bridging and bonding social capital theory and Granovetter’s theory on weak ties, the article studies ties between entrepreneurs originating from different ethnic groups in ethnically divided post-conflict societies. This study highlights the capacity of entrepreneurs to produce and generate social capital across ethnic lines. The field study includes surveys and interviews in Bosnia and Herzegovina, Kosovo, and North Macedonia. The results show that the majority of entrepreneurs cooperate and employ across ethnic lines. Cross-ethnic business relationships formed between entrepreneurs create benefits not only for entrepreneurs but also for building social capital across ethnic lines, thus forming valuable, yet depleted, social capital in post-conflict societies. Voluntary relationships between managers and business owners set a valuable model of weak ties facilitating associative behaviour among divided ethnic groups. Business exchange and relationships create social values that transcend a simple financial transaction. Value is in the form of friendship and trust, a reliance that maintains entrepreneurs’ relationships and contributes to the social capital.
The Planetary Wellbeing Initiative: Pursuing the Sustainable Development Goals in Higher Education
Authors: Antó Josep M. , Martí José Luis , Casals Jaume , Bou-Habib Paul , Casal Paula , Fleurbaey Marc , Frumkin Howard , Jiménez-Morales Manel , Jordana Jacint , Lancelotti Carla , Llavador Humberto , Mélon Lela , Solé Ricard , Subirada Francesc , Williams Andrew
Published: March 2021

We live in a time of pressing planetary challenges, many of which threaten catastrophic change to the natural environment and require massive and novel coordinated scientific and societal efforts on an unprecedented scale. Universities and other academic institutions have the opportunity and responsibility to assume a leading role in an era when the destiny of the planet is precisely in the hands of human beings. Drawing on the Planetary Health project promoted by the Rockefeller Foundation and The Lancet, Pompeu Fabra University launched in 2018 the Planetary Wellbeing Initiative, a long-term institutional strategy also animated by the United Nations Sustainable Development Goals (SDGs). Planetary Wellbeing might be defined as the highest attainable standard of wellbeing for human and non-human beings and their social and natural systems. Developing the potential of these new concepts involves a substantial theoretical and empirical effort in many different fields, all of them interrelated by the crosscutting challenges of global complexity, interdisciplinarity, and urgency. Close collaboration of science, humanities, and culture is more desperately needed now than ever before in the history of humankind.
Soil Salinity Type Effects on the Relationship between the Electrical Conductivity and Salt Content for 1:5 Soil-to-Water Extract
Authors: Ismayilov Amin I. , Mamedov Amrakh I. , Fujimaki Haruyuki , Tsunekawa Atsushi , Levy Guy J.
Published: March 2021

Soil salinity severely affects soil ecosystem quality and crop production in semi-arid and arid regions. A vast quantity of data on soil salinity has been collected by research organizations of the Commonwealth of Independent States (CIS, formerly USSR) and many other countries over the last 70 years, but using them in the current international network (irrigation and reclamation strategy) is complicated. This is because in the CIS countries salinity was expressed by total soluble salts as a percentage on a dry-weight basis (total soluble salts, TSS, %) and eight salinity types (chemistry) determined by the ratios of the anions and cations (Cl−, SO42−, HCO3−, and Na+, Ca2+, Mg2+) in diluted soil water extract (soil/water = 1:5) without assessing electrical conductivity (EC). Measuring the EC (1:5) is more convenient, yet EC is not only affected by the concentration but also characteristics of the ions and the salinity chemistry. The objective of this study was to examine the relationship between EC and TSS of soils in a diluted extract (1:5) for eight classic salinity types. We analyzed extracts (1:5) of 1100 samples of a clayey soil (0–20 cm) collected from cultivated semi-arid and arid regions for EC, TSS, soluble cations (Na+, Ca2+, Mg2+), and anions (HCO3−, Cl−, SO42−). Results revealed that (i) the variation in the proportional relationships (R2 ≥ 0.91–0.98) between EC (0.12–5.6 dS m−1) and TSS (0.05–2.5%) could be related to salinity type, and (ii) the proportionality coefficient of the relationships (2.2 2–3.16) decreased in the following order of salinity type: SO4 < Cl(SO4)–HCO3 < Cl(HCO3)–SO4 < SO4 (HCO3)–Cl < Cl. The findings suggest that once the salinity type of the soil is established, EC (1:5) values can be safely used for the evaluation of the soil salinity degree in the irrigated land in the context of sustainable soil and crop management.
SCN: A Novel Shape Classification Algorithm Based on Convolutional Neural Network
Authors: Zhang Chaoyan , Zheng Yan , Guo Baolong , Li Cheng , Liao Nannan
Published: March 2021

Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.