bluuebunny/crossref_metadata_2025 huggingface dataset:
Financial and Social Factors Influencing the Use of Unconventional Water Systems in Single-Family Houses in Eight European Countries
Authors: Stec Agnieszka , Słyś Daniel
Published: January 2022

A modern model of water management should take into account, first of all, its responsible consumption of both tap water and water from unconventional sources. With this in mind, a study was conducted to determine the financial efficiency of rainwater harvesting systems (RWHSs) and greywater recycling systems (GWRSs) in residential buildings located in eight European countries. At the first stage, volumetric reliability was determined for different tank capacities for actual precipitation data. An economic analysis was carried out for six variants in which rainwater and greywater were used in various combinations for toilet flushing, washing, and garden watering. The implementation of alternative water systems was found to be financially unprofitable in four cities: Warsaw, Bratislava, Budapest, and Stockholm. For these cities, the variant with the lowest life cycle cost (LCC) level was always Variant 0, with conventional installations. The opposite situation was observed in the other four locations (Lisbon, Madrid, Rome, and Prague), where Variant 0 was not found to be financially profitable for any of the calculation cases analyzed. Additionally, a survey was conducted to determine the effect of social aspects, which is often the greatest barrier to the implementation of new or unknown technologies. In most of the countries surveyed, rainwater is more acceptable to society as an alternative water source than greywater. For hygiene reasons, the use of these two systems for washing clothes was of greatest concern.
Acknowledgment to Reviewers of Robotics in 2021
Authors:   Robotics Editorial Office
Published: January 2022

Rigorous peer-reviews are the basis of high-quality academic publishing [...]
A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and Transferability
Authors: Qin Rongjun , Liu Tao
Published: January 2022

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.
A Postprocessing Method Based on Regions and Boundaries Using Convolutional Neural Networks and a New Dataset for Building Extraction
Authors: Yang Haiping , Xu Meixia , Chen Yuanyuan , Wu Wei , Dong Wen
Published: January 2022

Deep convolutional neural network (DCNN)-based methods have shown great improvements in building extraction from high spatial resolution remote sensing images. In this paper, we propose a postprocessing method based on DCNNs for building extraction. Specifically, building regions and boundaries are learned simultaneously or separately by DCNNs. The predicted building regions and boundaries are then combined by the postprocessing method to produce the final building regions. In addition, we introduce a manually labeled dataset based on high spatial resolution images for building detection, the XIHU building dataset. This dataset is then used in the experiments to evaluate our methods. Besides the WHU building dataset, East Asia (WHUEA) is also included. Results demonstrate that our method that combines the results of DeepLab and BDCN shows the best performance on the XIHU building dataset, which achieves 0.78% and 23.30% F1 scores, and 1.13% and 28.45% intersection-over-union (IoU) improvements compared with DeepLab and BDCN, respectively. Additionally, our method that combines the results of Mask R-CNN and DexiNed performs best on the WHUEA dataset. Moreover, our methods outperform the state-of-the-art multitask learning network, PMNet, on both XIHU and WHUEA datasets, which indicates that the overall performance can be improved although building regions and boundaries are learned in the training stage.
Spatial Dispersion and Non-Negative Matrix Factorization of SAR Backscattering as Tools for Monitoring Snow Depth Evolution in Mountain Areas: A Case Study at Central Pyrenees (Spain)
Authors: Amoruso Antonella , Crescentini Luca , Costa Riccardo
Published: January 2022

Accurate knowledge of snow cover extent, depth (SD), and water equivalent is essential for studying the global water cycle, climate, and energy–mass exchange in the Earth–atmosphere system, as well as for water resources management. Ratio between SAR cross- and co-polarization backscattering (σVH/σVV) was used to monitor SD during snowy months in mountain areas; however, published results refer to short periods and show lack of correlation during non-snowy months. We analyze Sentinel-1A images from a study area in Central Pyrenees to generate and investigate (i) time series of σVH/σVV spatial dispersion, (ii) spatial distribution of pixelwise σVH/σVV temporal standard deviation, and (iii) fundamental modes of σVH/σVV evolution by non-negative matrix factorization. The spatial dispersion evolution and the first mode are highly correlated (correlation coefficients larger than 0.9) to SD evolution during the whole seven-year-long period, including snowy and non-snowy months. The local incidence angle strongly affects how accurately σVH/σVV locally follows the first mode; thus, areas where it predominates are orbit-dependent. When combining ascending- and descending-orbit images in a single data matrix, the first mode becomes predominant almost everywhere snow pack persists during winter. Capability of our approach to reproduce SD evolution makes it a very effective tool.
Spatiotemporal Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT Observations
Authors: Li Luman , Lei Liping , Song Hao , Zeng Zhaocheng , He Zhonghua
Published: January 2022

Methane (CH4) is one of the most important greenhouse gases causing the global warming effect. The mapping data of atmospheric CH4 concentrations in space and time can help us better to understand the characteristics and driving factors of CH4 variation as to support the actions of CH4 emission reduction for preventing the continuous increase of atmospheric CH4 concentrations. In this study, we applied a spatiotemporal geostatistical analysis and prediction to develop an approach to generate the mapping CH4 dataset (Mapping-XCH4) in 1° grid and three days globally using column averaged dry air mole fraction of CH4 (XCH4) data derived from observations of the Greenhouse Gases Observing Satellite (GOSAT) from April 2009 to April 2020. Cross-validation for the spatiotemporal geostatistical predictions showed better correlation coefficient of 0.97 and a mean absolute prediction error of 7.66 ppb. The standard deviation is 11.42 ppb when comparing the Mapping-XCH4 data with the ground measurements from the total carbon column observing network (TCCON). Moreover, we assessed the performance of this Mapping-XCH4 dataset by comparing with the XCH4 simulations from the CarbonTracker model and primarily investigating the variations of XCH4 from April 2009 to April 2020. The results showed that the mean annual increase in XCH4 was 7.5 ppb/yr derived from Mapping-XCH4, which was slightly greater than 7.3 ppb/yr from the ground observational network during the past 10 years from 2010. XCH4 is larger in South Asia and eastern China than in the other regions, which agrees with the XCH4 simulations. The Mapping-XCH4 shows a significant linear relationship and a correlation coefficient of determination (R2) of 0.66, with EDGAR emission inventories over Monsoon Asia. Moreover, we found that Mapping-XCH4 could detect the reduction of XCH4 in the period of lockdown from January to April 2020 in China, likely due to the COVID-19 pandemic. In conclusion, we can apply GOSAT observations over a long period from 2009 to 2020 to generate a spatiotemporally continuous dataset globally using geostatistical analysis. This long-term Mpping-XCH4 dataset has great potential for understanding the spatiotemporal variations of CH4 concentrations induced by natural processes and anthropogenic emissions at a global and regional scale.
Research on Comfort Evaluation Model of Urban Residents’ Public Space Lighting Integrating Public Perception and Nighttime Light Remote Sensing Data
Authors: Wei Shengrong , Jiao Weili , Liu Huichan , Long Tengfei , Liu Yongkun , Ji Ping , Hou Ruixia , Zhang Naijing , Xiao Yundan
Published: January 2022

With the development of a sustainable lifestyle, people are paying more and more attention to the comfort of their own living environment, including public space lighting, which is immediately accessible to residents. The demand on its quality and comfort has thus gained attention recently. However, there is still a lack of related research on public space lighting comfort evaluation models that combine nighttime light remote sensing data and field measurement data, and link lighting attributes with the comfort of residents. This research uses nighttime light remote sensing data to select typical test areas, measures the lighting data of the survey points on the spot, develops an intelligent WeChat applet that collects public perception data, analyzes different lighting parameters, and builds a public space lighting comfort model based on the structural equation model analysis method. The results show that the factor that the areas with high light intensity are more comfortable than the areas with low light intensity. In areas with high light intensity, people pay more attention to the uniformity, security and comfort of the light, while in areas with low light intensity or high blue light, people’s perception of glare will be more obvious. This research can provide a basis for the intelligent optimization of public space lighting from the perspective of public preference.
Interannual Variability of Water Level in Two Largest Lakes of Europe
Authors: Kostianoy Andrey G. , Lebedev Sergey A. , Kostianaia Evgeniia A. , Prokofiev Yaan A.
Published: January 2022

Regional climate change affects the state of inland water bodies and their water balance, which is determined by a number of hydrometeorological and hydrogeological factors. An integral characteristic of changes in the water balance is the behavior of the level of lakes and reservoirs, which not only largely determines the physical and ecological state of water bodies, but also significantly affects the coastal infrastructure and socio-economic development of the region. This paper investigates the interannual variability of the level of the Ladoga and Onega lakes, the largest lakes in Europe located in the northwest of Russia, according to satellite altimetry data for 1993–2020. For this purpose, we used three specialized altimetry databases: DAHITI, G-REALM, and HYDROWEB. Water level data from these altimetry databases were compared with in-situ records at water level gauge stations. Information on air temperature (1945–2019) and precipitation (1966–2019) acquired at three meteostations located at Ladoga and Onega lakes was used to investigate interannual trends in the regional climate change. Finally, we discuss the potential impact of the lake level rise and regional climate warming on the infrastructure and operability of railways in this region.
Extending the Validity of Squeeze Film Damping Models with Lower Aspect Ratios
Authors: Xu Xiang , Fang Weidong , Bai Jian , Chen Jiaxiao , Yao Yuan , Lu Qianbo
Published: January 2022

Squeeze film air damping is a significant factor in the design of MEMS devices owing to its great impact on the dynamic performance of vibrating structures. However, the traditional theoretical results of squeeze film air damping are derived from the Reynolds equation, wherein there exists a deviation from the true results, especially in low aspect ratios. While expensive efforts have been undertaken to prove that this deviation is caused by the neglect of pressure change across the film, a quantitative study has remained elusive. This paper focuses on the investigation of the finite size effect of squeeze film air damping and conducts numerical research using a set of simulations. A modified expression is extended to lower aspect ratio conditions from the original model of squeeze film air damping. The new quick-calculating formulas based on the simulation results reproduce the squeeze film air damping with a finite size effect accurately with a maximum error of less than 1% in the model without a border effect and 10.185% in the compact model with a border effect. The high consistency between the new formulas and simulation results shows that the finite size effect was adequately considered, which offers a previously unattainable precise damping design guide for MEMS devices.
Electrostatic-Fluid-Structure 3D Numerical Simulation of a MEMS Electrostatic Comb Resonator
Authors: Yu Zhanqing , Chen Shiping , Mou Ya , Hu Fade
Published: January 2022

The reliability and stability of MEMS electrostatic comb resonators have become bottlenecks in practical applications. However, there are few studies that comprehensively consider the nonlinear dynamic behavior characteristics of MEMS systems and devices in a coupled field so that the related simulation accuracy is low and cannot meet the needs of design applications. In this paper, to avoid the computational complexity and the uncertainty of the results of three-field direct coupling and take into the damping nonlinearity caused by coupled fields, a novel electrostatic-fluid-structure three-field indirect coupling method is proposed. Taking an actual microcomb resonant electric field sensor as an example, an electrostatic-fluid-structure multiphysics coupling 3D finite element simulation model is established. After considering the influence of nonlinear damping concerning the large displacement of the structure and the microscale effect, multifield coupling dynamics research is carried out using COMSOL software. The multiorder eigenmodes, resonant frequency, vibration amplitude, and the distribution of fluid load of the microresonator are calculated and analyzed. The simulated data of resonance frequency and displacement amplitude are compared with the measured data. The results show that the fluid load distribution of the microelectrostatic comb resonator along the thickness direction is high in the middle and low on both sides. The viscous damping of the sensor under atmospheric pressure is mainly composed of the incompressible flow damping of the comb teeth, which is an order of magnitude larger than those of other parts. Compared with the measured data, it can be concluded that the amplitude and resonance frequency of the microresonator considering the nonlinear damping force and residual thermal stress are close to the experimental values (amplitude error: 15.47%, resonance frequency error: 12.48%). This article provides a reference for studies on the dynamic characteristics of electrostatically driven MEMS devices.
A Dynamic Algorithm for Interference Management in D2D-Enabled Heterogeneous Cellular Networks: Modeling and Analysis
Authors: Kamruzzaman Md , Sarkar Nurul I. , Gutierrez Jairo
Published: January 2022

To supporting a wider and diverse range of applications, device-to-device (D2D) communication is a key enabler in heterogeneous cellular networks (HetCNets). It plays an important role in fulfilling the performance and quality of service (QoS) requirements for 5G networks and beyond. D2D-enabled cellular networks enable user equipment (UE) to communicate directly, without any or with a partial association with base stations (eNBs). Interference management is one of the critical and complex issues in D2D-enabled HetCNets. Despite the wide adoption of D2D communications, there are very few researchers addressing the problems of mode selection (MS), as well as resource allocation for mutual interference in three-tier cellular networks. In this paper, we first identify and analyze three key factors, namely outage probability, signal-to-interference and noise ratio (SINR), and cell density that influence the performance of D2D-enabled HetCNets. We then propose a dynamic algorithm based on a distance-based approach to minimize the interference and to guarantee QoS for both cellular and D2D communication links. Results obtained show that outage probability is improved by 35% and 49% in eNB and SCeNB links, respectively, when compared with traditional neighbor-based methods. The findings reported in this paper provide some insights into interference management in D2D communications that can help network researchers and engineers contribute to further developing next-generation cellular networks.
Torque Ripple Suppression of Brushless DC Motor Drive System Based on Improved Harmonic Injection Active Disturbance Rejection Control
Authors: He Jinglun , Yan Changxiang , Wang Xiaodong
Published: January 2022

The positioning accuracy and speed stability of the brushless DC motor (BLDC motor), as the drive element of the optomechanically scanned system (OMSS), are closely interrelated to the final imaging quality of the system. Active disturbance rejection control (ADRC) with strong anti-interference ability, fast response and good robustness is one of the extensively used control strategies. However, the performance of ADRC working in a complicated environment will be limited due to the controller structure, parameter tuning and the influence of multi-source nonlinear disturbance. Therefore, an improved ADRC method is proposed, which can switch between ‘point-to-point control mode’ and ‘stable speed control mode’ according to the system requirements. To further suppress the torque ripple and improve the control performance of the system, an improved harmonic injection scheme is added, and the parameters of improved ADRC are tuned by a slime mould algorithm based on a Levy flight operator (LF-SMA). The stability of the proposed ADRC is proved by Lyapunov stability theory. The experimental results show that the proposed control scheme could be available to reduce the torque ripple of the system.
Undifferenced Kinematic Precise Orbit Determination of Swarm and GRACE-FO Satellites from GNSS Observations
Authors: Luo Peng , Jin Shuanggen , Shi Qiqi
Published: January 2022

Low Earth Orbit (LEO) satellites can be used for remote sensing and gravity field recovery, while precise orbit determination (POD) is vital for LEO satellite applications. However, there are some systematic errors when using the LEO satellite orbits released by different agencies in multi-satellite-based applications, e.g., Swarm and Gravity Recovery and Climate Experiment-Follow-On (GRACE-FO), as different GNSS precise orbit and clock products are used as well as processing strategies and software. In this paper, we performed undifferenced kinematic PODs for Swarm and GRACE-FO satellites simultaneously over a total of 14 days by using consistent International Global Navigation Satellite System (GNSS) Service (IGS) precise orbit and clock products. The processing strategy based on an undifferenced ionosphere-free combination and a least squares method was applied for Swarm and GRACE-FO satellites. Furthermore, the quality control for the kinematic orbits was adopted to mitigate abrupt position offsets. Moreover, the accuracy of the kinematic orbits solution was evaluated by carrier phase residual analysis and Satellite Laser Ranging (SLR) observations, as well as comparison with official orbits. The results show that the kinematic orbits solution is better than 4 cm, according to the SLR validation. With quality control, the accuracy of the kinematic orbit solution is improved by 2.49 % for the Swarm-C satellite and 6.98 % for the GRACE-D satellite when compared with their precise orbits. By analyzing the accuracy of the undifferenced kinematic orbit solution, the reliability of the LEO orbit determination is presented in terms of processing strategies and quality control procedures.
Acceleration-Based Method of Ice Quality Assessment in the Sport of Curling
Authors: Dzikowski Bartosz , Weremczuk Jerzy , Pachwicewicz Marek
Published: January 2022

Despite the significant influence of ice conditions on results in the sport of curling, players and ice technicians lack a measurement device that would objectively measure ice quality during a curling competition. This paper presents such a new measurement method by using a device consisting an inertial measurement unit (IMU) attached to the handle of the curling stone and data processing software. IMU is used to measure the vibration of curling stone during its movement on the surface of the ice. The acceleration signal is recorded, and then the software calculates the value of so-called R parameter in frequency domain. The value of R allows one to determine if an ice sheet had been pebbled and if the shape of pebbles is suitable for the game of curling. The presented system was tested in various ice conditions—on both freshly prepared and used ice. Ice technicians and players may use the proposed system to decide whether the ice surface is suitable for play or if it should be remade.
Intrinsic Blue Fluorescence of 2.0G PAMAM-DCM Polymer Dots and Its Applications for Fe3+ Sensing
Authors: Wang Xin , Shi Weiguang , Wang Yuda , Cheng Dan , Liu Jiahui , Xu Shihan , Liu Wei , Dong Biao , Sun Jiao
Published: January 2022

A typical and environment-friendly fluorescent polyamine-amine (PAMAM) features good compatibility and unique surface modification, while it is restricted by a low fluorescence property performance and an unclear fluorescence mechanism. In this work, we prepared blue fluorescent PAMAM polymer dots (PDs) via a simple hydrothermal method based on dichloromethane (DCM) and 2.0G PAMAM. The quantum yield achieved was 32.1%, which was 25 times stronger than that of 2.0G PAMAM due to the lone-pair electron leap of the amine groups, the aggregation of carbonyl groups, as well as the crosslinking induced by DCM inside the PAMAM. In addition, the fluorescent 2.0G PAMAM-DCM PDs show a great Fe3+ sensing property with the detection limit of 56.6 nM, which is much lower than the safety limits (5.36 μM) in drinking water, indicating its great potential for Fe3+ detection in aqueous media.
Inertial Sensor Algorithm to Estimate Walk Distance
Authors: Shah Vrutangkumar V. , Curtze Carolin , Sowalsky Kristen , Arpan Ishu , Mancini Martina , Carlson-Kuhta Patricia , El-Gohary Mahmoud , Horak Fay B. , McNames James
Published: January 2022

The “total distance walked” obtained during a standardized walking test is an integral component of physical fitness and health status tracking in a range of consumer and clinical applications. Wearable inertial sensors offer the advantages of providing accurate, objective, and reliable measures of gait while streamlining walk test administration. The aim of this study was to develop an inertial sensor-based algorithm to estimate the total distance walked using older subjects with impaired fasting glucose (Study I), and to test the generalizability of the proposed algorithm in patients with Multiple Sclerosis (Study II). All subjects wore two inertial sensors (Opals by Clario-APDM Wearable Technologies) on their feet. The walking distance algorithm was developed based on 108 older adults in Study I performing a 400 m walk test along a 20 m straight walkway. The validity of the algorithm was tested using a 6-minute walk test (6MWT) in two sub-studies of Study II with different lengths of a walkway, 15 m (Study II-A, n = 24) and 20 m (Study II-B, n = 22), respectively. The start and turn around points were marked with lines on the floor while smaller horizontal lines placed every 1 m served to calculate the manual distance walked (ground truth). The proposed algorithm calculates the forward distance traveled during each step as the change in the horizontal position from each foot-flat period to the subsequent foot-flat period. The total distance walked is then computed as the sum of walk distances for each stride, including turns. The proposed algorithm achieved an average absolute error rate of 1.92% with respect to a fixed 400 m distance for Study I. The same algorithm achieved an absolute error rate of 4.17% and 3.21% with respect to an averaged manual distance for 6MWT in Study II-A and Study II-B, respectively. These results demonstrate the potential of an inertial sensor-based algorithm to estimate a total distance walked with good accuracy with respect to the manual, clinical standard. Further work is needed to test the generalizability of the proposed algorithm with different administrators and populations, as well as larger diverse cohorts.
HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps
Authors: Hamza Alyaa A. , Abdel Halim Islam Tharwat , Sobh Mohamed A. , Bahaa-Eldin Ayman M.
Published: January 2022

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on utilizing static or dynamic analysis separately. However, the hybrid analysis is the best for obtaining accurate results. The SAS provides an effective outcome according to many criteria related to the analysis process, such as analysis type, characteristics, sensitivity, and analysis techniques. This paper proposes a new hybrid (static and dynamic) SAS based on the model-checking technique and deep learning, called an HSAS-MD analyzer, which focuses on the holistic analysis perspective of IoT apps. It aims to analyze the data of IoT apps by (1) converting the source code of the target applications to the format of a model checker that can deal with it; (2) detecting any abnormal behavior in the IoT application; (3) extracting the main static features from it to be tested and classified using a deep-learning CNN algorithm; (4) verifying app behavior by using the model-checking technique. HSAS-MD gives the best results in detecting malware from malicious smart Things applications compared to other SASs. The experimental results of HSAS-MD show that it provides 95%, 94%, 91%, and 93% for accuracy, precision, recall, and F-measure, respectively. It also gives the best results compared with other analyzers from various criteria.
Water Inrush Hazard in Shijingshan Tunnel during Construction, Zhuhai, Guangdong, China
Authors: Kong He-Qi , Zhao Lin-Shuang , Zhang Ning
Published: January 2022

This short communication reports a water inrush hazard during the construction of the Shijingshan Tunnel in Zhuhai, Guangdong, China. On 15 July 2021, a water inrush accident occurred at the construction site 1.16 km away from the tunnel entrance, resulting in 14 casualties. The purpose of this study is to investigate and discuss the water inrush process in this atypical case. The principal causes of this tunneling accident include the special geological conditions at the construction site, high hydraulic conductivity at the part that connects the underground water system and the overlying water body (the Jida Reservoir), and the impact of heavy rainfall. Moreover, four significant suggestions are proposed to prevent the possible occurrence of water inrush disaster: (1) assessing the risk level in areas prone to water inrush; (2) getting a comprehensive geological prediction based on borehole data and artificial intelligent methods; (3) establishing a monitoring system during the tunneling process; (4) strengthening the self-protection skills of construction workers.
Enhancement of the Green Extraction of Bioactive Molecules from Olea europaea Leaves
Authors: Valinger Davor , Kušen Matea , Benković Maja , Jurina Tamara , Panić Manuela , Radojčić Redovniković Ivana , Kljusurić Jasenka Gajdoš , Tušek Ana Jurinjak
Published: January 2022

Olive leaves are a rich source of polyphenols that have beneficial antihypertensive, hypocholesterolemic, cardioprotective, and anti-inflammatory effects. The aim of this study was to compare the efficiency of conventional extraction (CE), microwave-assisted extraction (MWE), and microwave–ultrasound-assisted extraction (MWUE) for the extraction of bioactive molecules from olive leaves using water as a solvent and to define the optimal extraction conditions for all three methods used. CE conditions (temperature, time, magnetic stirrer rotational rate and particle diameter) and MWE extraction and MWUE conditions (microwave power, time, particle diameter, and temperature) were optimized using response surface methodology (RSM) based on the Box–Behnken experimental design. The total polyphenol content and antioxidant activity of all prepared extracts was analyzed and compared. The results showed that MWUE provided the highest amount of total polyphenols (Total Polyphenolic Content (TPC) = 273.779 ± 4.968 mgGAE gd.m.−1) and the highest antioxidant activity, which was about 3.1 times higher than CE. Optimal extraction conditions were determined to be 80 °C, 15 min, 200 μm, and 750 min-1 for CE, 700 W, 7.5 min, 300 μm, and 80 °C for MWE, and 800 W, 5 min, 100 μm, and 60 °C for MWUE. Considering the maximum amount of total polyphenols extracted, the results suggest that MWUE is the most effective green extraction process that extracted the highest amount of polyphenols and could be used by the food industry for commercial exploitation of currently unprofitable plant bioactive sources.
Expanding Antineoplastic Drugs Surface Monitoring Profiles: Enhancing of Zwitterionic Hydrophilic Interaction Methods
Authors: Dugheri Stefano , Mucci Nicola , Squillaci Donato , Bucaletti Elisabetta , Cappelli Giovanni , Trevisani Lucia , Valsecchi Cecile , Consonni Viviana , Gosetti Fabio , Ballabio Davide , Arcangeli Giulio
Published: January 2022

Antineoplastic drugs are a wide and heterogeneous group of substances that, as universally known, can cause highly severe toxic effects to whoever is exposed. From an occupational safety point of view, surface contaminations inside preparation and administration units are a growing issue and therefore require the development and implementation of sensible and fast monitoring methods. The unlikelihood of a unique all-embracing chromatography, able to correctly retain and separate each analyte led to the need to create an orthogonal normal phase analysis, which might be able to fill the gaps in the more common reversed-phase ones. An existing hydrophilic interaction method has thus been expanded to 6 other drugs and applied to real samples after an evaluation of its performances. The experimental data were then used to evaluate the possibility of estimating reliable relationships between the chromatographic retention and the chemical-structural features of the drugs under analysis.
Determination of Antibiotic Residues in Aquaculture Products by Liquid Chromatography Tandem Mass Spectrometry: Recent Trends and Developments from 2010 to 2020
Authors: Xiao Yueting , Liu Shuyu , Gao Yuan , Zhang Yan , Zhang Qinghe , Li Xiuqin
Published: January 2022

The issue of antibiotic residues in aquaculture products has aroused much concern over the last decade. The residues can remain in food and enter the human body through the food chain, posing great risks to public health. For the safety of foods and products, many countries have issued maximum residue limits and banned lists for antibiotics in aquaculture products. Liquid chromatography tandem mass spectrometry (LC/MS/MS) has been widely used for the determination of trace antibiotic residues due to its high sensitivity, selectivity and throughput. However, considering its matrix effects during quantitative measurements, it has high requirements for sample pre-treatment, instrument parameters and quantitative method. This review summarized the application of LC/MS/MS in the detection of antibiotic residues in aquaculture products in the past decade (from 2010 to 2020), including sample pre-treatment techniques such as hydrolysis, derivatization, extraction and purification, mass spectrometry techniques such as triple quadrupole mass spectrometry and high-resolution mass spectrometry as well as status of matrix certified reference materials (CRMs) and matrix effect.
The Politics of Protest and Gender: Women Riding the Wings of Resistance
Authors: Matthews Tasia
Published: January 2022

The #MeToo movement, from its creation by activist Tarana Burke back in 2006 to its explosion on social media during the 2017 Harvey Weinstein sexual assault allegations, has continuously propagated images of gendered resistance from around the globe. In South Korea, Poland, Mexico, Bangladesh, and more, large numbers of women protest a variety of gendered topics: from unjust rulings in cases of domestic violence, to the lack of reproductive rights, to femicide, to inaction by law enforcement on cases of stalking, harassment, or sexual assault, and more. These images clearly demonstrate that public resistance is dominated by women, even in societies that are seen to traditionally subjugate women—though this is not new, and women have always been involved in resistance even when there was no way to document their participation. However, in countries where conservative institutions, public opinion, and government policy that contribute to gender inequality are paired with punitive action for opposition, women face a higher risk of being punished, ostracized, or brutalized for their resistance. In Thailand, a military state with perhaps the strictest lèse majesté laws in the world, activists are frequently fined, imprisoned, kept under surveillance, disappeared, or forced to flee. Despite this, Thailand experiences frequent surges of public resistance, dominated by youth and overwhelmingly by women. Since February 2020, a large portion of the Thai population, consisting primarily of students, has taken to public demonstrations demanding a fair democracy and constitutional reform, joined together in exasperation over an uncertain future, a crippling economy, an untouchable elite, and a rigged election. In this now years-long movement, fueled by global support and sophisticated protest tactics learned from watching Hong Kong, we observe the inclusion of gendered protest topics and demands by Thai women and girls. This paper demonstrates how Thai women utilize the movement to demand progress in gendered areas by examining examples taken throughout the 2020 pro-democracy protest movement, with the overall objective of contributing to understanding the relationship between public resistance and feminism.
Making the Best Out of a Crisis: Russia’s Health Diplomacy during COVID-19
Authors: Giusti Serena , Tafuro Ambrosetti Eleonora
Published: January 2022

The article considers how Russia has reacted to the pandemic, especially in terms of foreign policy. Although internally the management of the pandemic has led to a further limitation of citizens’ freedoms, externally it has been exploited to improve the country’s image and strengthen its leverage through tactical activism and political generosity. Russia’s strategy has been articulated in two phases: first, immediate aid to countries in need in order to channel the idea of a benevolent state, directly or indirectly discrediting other countries or organisations; second, the geopolitical use of vaccines. The article stresses the relationship between science and foreign policy and analyses Russia’s health diplomacy strategy, underscoring its opportunities and challenges through the analysis of two case studies (Italy and Belarus). From a methodological perspective, the article mainly refers to foreign policy analysis (FPA), using concepts such as soft power, health diplomacy, and geopolitics.
The Nitration of Proteins, Lipids and DNA by Peroxynitrite Derivatives-Chemistry Involved and Biological Relevance
Authors: Pérez de la Lastra José Manuel , Juan Celia Andrés , Plou Francisco J. , Pérez-Lebeña Eduardo
Published: January 2022

In recent years, much interest has been generated by the idea that nitrosative stress plays a role in the aetiology of human diseases, such as atherosclerosis, inflammation, cancer, and neurological diseases. The chemical changes mediated by reactive nitrogen species (RNS) are detrimental to cell function, because they can cause nitration, which can alter the structures of cellular proteins, DNA, and lipids, and hence, impair their normal function. One of the most potent biological nitrosative agents is peroxynitrite (ONOO−), which is produced when nitric oxide (•NO) and superoxide (•O2−) are combined at extremely rapid rates. Considering the plethora of oxidations by peroxynitrite, this makes peroxynitrite the most prevalent nitrating species responsible for protein, DNA, and lipids nitration in vivo. There is biochemical evidence to suggest that the interactions of the radicals NO and superoxide result in the formation of a redox system, which includes the reactions of nitrosation and nitration, and is a component of the complex cellular signalling network. However, the chemistry involved in the nitration process with peroxynitrite derivatives is poorly understood, particularly for biological molecules, such as DNA, proteins, and lipids. Here, we review the processes involved in the nitration of biomolecules, and provide a mechanistic explanation for the chemical reactions of NOS and nitrosative stress. This study reveals that these processes are based on a surprisingly simple and straightforward chemistry, with a fascinating influence on cellular physiology and pathology.
Abscisic Acid Machinery Is under Circadian Clock Regulation at Multiple Levels
Authors: Pizzio Gastón A.
Published: January 2022

Abscisic acid (ABA) is recognized as the key hormonal regulator of plant stress physiology. This phytohormone is also involved in plant growth and development under normal conditions. Over the last 50 years the components of ABA machinery have been well characterized, from synthesis to molecular perception and signaling; knowledge about the fine regulation of these ABA machinery components is starting to increase. In this article, we review a particular regulation of the ABA machinery that comes from the plant circadian system and extends to multiple levels. The circadian clock is a self-sustained molecular oscillator that perceives external changes and prepares plants to respond to them in advance. The circadian system constitutes the most important predictive homeostasis mechanism in living beings. Moreover, the circadian clock has several output pathways that control molecular, cellular and physiological downstream processes, such as hormonal response and transcriptional activity. One of these outputs involves the ABA machinery. The circadian oscillator components regulate expression and post-translational modification of ABA machinery elements, from synthesis to perception and signaling response. The circadian clock establishes a gating in the ABA response during the day, which fine tunes stomatal closure and plant growth response.
Parents’ Willingness to Allow Their Unaccompanied Children to Use Emerging and Future Travel Modes
Authors: Koppel Sjaan , McDonald Hayley , Peiris Sujanie , Zou Xin , Logan David B.
Published: January 2022

This study investigated parents’ willingness to allow their unaccompanied child(ren) to use emerging and future travel modes (e.g., rideshare vehicles and automated vehicles). An online survey was completed by 631 Australian respondents (M = 39.2 years, SD = 10.5 years, Male: 36.6%) who reported that they currently lived with one or more children (17 or below). Approximately one-third (37.9%) of the respondents reported a willingness to allow their child to use a rideshare vehicle alone and more than half of the respondents (57.2%) reported a willingness to allow their child to use an automated vehicle alone. Respondents who expressed willingness to allow their child to use a rideshare vehicle alone were more likely to express a willingness to use an automated vehicle alone (79.1%) compared to respondents who were unwilling to use a rideshare vehicle (43.9%), χ2(1) = 75.158, p < 0.001, Phi = 0.345. Two separate logistic regression models revealed key similarities and differences related to respondents’ willingness to allow their unaccompanied child to use both transport modes. Respondents’ willingness to allow their unaccompanied child to use a rideshare vehicle was significantly related to their previous use of a rideshare vehicle with their child, having an optimistic view of technology, annual mileage, their aberrant driving behaviours, and their desire for route-control and assurance features within the rideshare vehicle, χ2(7) = 159.594, p < 0.001. Respondents’ willingness to allow their child to use an automated vehicle alone was significantly related to awareness of automated vehicles, education level, positive views towards technology, seeing technology to be innovative, and requirements for route control features within the automated vehicle, χ2(6) = 113.325, p < 0.001. Despite the potential for emerging or future travel modes to provide additional personal transportation options, these results suggest that Australian parents are unwilling to allow their unaccompanied child to use these modes of transport. These findings will have significant implications for transport planning, particularly in growing communities where pressures on parents to transport their child(ren) to activities and events with minimal adult supervision is increasing.
Food Citizenship as an Agroecological Tool for Food System Re-Design
Authors: Tittarelli Fabio , Saba Anna , Di Pierro Marta , Ciaccia Corrado
Published: January 2022

The modern agrifood system has a strong socio-economic and health impact on stakeholders, from producers to consumers. According to agroecological studies, a key factor for the food system to change is a stronger connection between farmers and consumers and the implementation of Food Citizenship. In this paper, we present the results of preliminary research on the consumer approach to vegetable and fruit purchase in Italy. Our main aim was to group consumers according to the major criteria guiding their food purchase patterns, to outline the main criteria underpinning their choices and assess their degree of environmental, economic and social awareness. Cluster analysis was able to pick out two groups with significant statistical differences, i.e., 55.4% of the sample showed, through their consumption patterns, deeper environmental and social concerns. Specifically, when comparing food purchasing patterns, ethical choices, biodiversity and global warming were the most influential factors to discriminate the two clusters. Moreover, organic food consumption confirmed its status as an acknowledged way to reduce the environmental impact of the modern agrifood system for which respondents are willing to pay a higher price compared to conventional food. Nonetheless, the whole sample of respondents perceived organic food as expensive and not good for value.
Treated Unconventional Waters Combined with Different Irrigation Strategies Affect 1H NMR Metabolic Profile of a Monovarietal Extra Virgin Olive Oil
Authors: Angilè Federica , Vivaldi Gaetano Alessandro , Girelli Chiara Roberta , Del Coco Laura , Caponio Gabriele , Lopriore Giuseppe , Fanizzi Francesco Paolo , Camposeo Salvatore
Published: January 2022

The agricultural sector is facing a decrease in water supply and water quality at a global level and this is a problem that strictly affects all the Mediterranean olive growing areas. The aim of this work was to evaluate, for the first time, by NMR Spectroscopy and multivariate data analysis the metabolic profiling of the oils produced under different irrigation schemes. Arbosana olive oils were obtained from the use of saline reclaimed water (RW) and treated municipal wastewater (DW), combined with: full irrigation (FI) and regulated deficit irrigation (RDI). The results show a higher relative content of saturated fatty acids in EVOOs obtained from RDI strategy, regardless of the water source. Moreover, an increase in unsaturated fatty acids, a ω6/ω3 ratio content was observed in EVOOs obtained from RW when compared with DW water. Furthermore, the RW–RDI showed an increase in secoiridoid derivatives and hydroperoxides with respect to DW–RDI. A sustainable irrigation management, by combining a deficit irrigation strategy and saline reclaimed water source, could be crucial in order to overcome the problem of water scarcity and to guarantee the olive oil nutraceutical properties. The 1H NMR-based metabolomic approach proved a powerful and versatile tool for this specific investigation.
Teaching Sustainable Development Goals to University Students: A Cross-Country Case-Based Study
Authors: Vasconcelos Clara , Silva Joana , Calheiros Cristina S. C. , Mikusiński Grzegorz , Iwińska Katarzyna , Skaltsa Ioanna G. , Krakowska Katarzyna
Published: January 2022

With the purpose of analysing if case-based teaching methodology could improve higher education students’ knowledge about sustainable development, a quasi-experimental study was conducted in an international project (PASSION—Partnership for Sustainable Development and Social Innovation) and in five countries (Greece, Poland, Portugal and Sweden). Cases were interdisciplinary and focused on sustainable development goals aligned with common worldwide dilemmas like, for example, food consumption, organic farm or nature protection. Our sample consisted of 104 university students aged between 18 and 58 years, where 53% were female. Standardized pre- and post-tests were administered, and a significant improvement in sustainable development knowledge was observed considering three main questions in the pooled dataset (t103 = −7.324; p = 0.000). Regarding the four cases applications where results were more significant, an in-depth analysis was conducted, allowing the project team to better understand participants answers to multiple-choice and open questions which are analysed and discussed individually. Our results demonstrate that the case-based teaching improved the knowledge of sustainable development goals, which is expected to induce positive changes in pro-environmental behaviours. We also point out the potential of case-based teaching applied in a cross-country manner. The scheme is particularly valuable in dealing with complex sustainability dilemmas that show better their global perspectives.