bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified Harmony Search Algorithm for Resource-Constrained Parallel Machine Scheduling Problem with Release Dates and Sequence-Dependent Setup Times
Authors: Al-harkan Ibrahim M. , Qamhan Ammar A. , Badwelan Ahmed , Alsamhan Ali , Hidri Lotfi
Published: April 2021

This research focuses on the problem of scheduling a set of jobs on unrelated parallel machines subject to release dates, sequence-dependent setup times, and additional renewable resource constraints. The objective is to minimize the maximum completion time (makespan). To optimize the problem, a modified harmony search (MHS) algorithm was proposed. The parameters of MHS are regulated using full factorial analysis. The MHS algorithm is examined, evaluated, and compared to the best methods known in the literature. Four algorithms were represented from similar works in the literature. A benchmark instance has been established to test the sensitivity and behavior of the problem parameters of the different algorithms. The computational results of the MHS algorithm were compared with those of other metaheuristics. The competitive performance of the developed algorithm is verified, and it was shown to provide a 42% better solution than the others.
Particle Swarm Optimization—An Adaptation for the Control of Robotic Swarms
Authors: Rossides George , Metcalfe Benjamin , Hunter Alan
Published: April 2021

Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The motion of the particles is linked, and the overall behavior of the particle swarm is controlled by several parameters. PSO has been proposed as a control strategy for physical swarms of robots that are localizing a source; the robots are analogous to the virtual particles. However, previous attempts to achieve this have shown that there are inherent problems. This paper addresses these problems by introducing a modified version of PSO, as well as introducing new guidelines for parameter selection. The proposed algorithm links the parameters to the velocity and acceleration of each robot, and demonstrates obstacle avoidance. Simulation results from both MATLAB and Gazebo show close agreement and demonstrate that the proposed algorithm is capable of effective control of a robotic swarm and obstacle avoidance.
A Low-Cost and Robust Landsat-Based Approach to Study Forest Degradation and Carbon Emissions from Selective Logging in the Venezuelan Amazon
Authors: Pacheco-Angulo Carlos , Plata-Rocha Wenseslao , Serrano Julio , Vilanova Emilio , Monjardin-Armenta Sergio , González Alvaro , Camargo Cristopher
Published: April 2021

Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).
Tidal Flat Extraction and Change Analysis Based on the RF-W Model: A Case Study of Jiaozhou Bay, East China
Authors: Yan Jinfeng , Zhao Shiyi , Su Fenzhen , Du Jiaxue , Feng Pengfei , Zhang Shixun
Published: April 2021

Coastal tidal flats are important ecological resources. As the dividing line between marine and terrestrial ecosystems, tidal flats provide a large number of ecosystem services. However, with the excessive development of coastal areas, tidal flat resources have been drastically reduced, leading to the deterioration of coastal ecosystems. There is an urgent need to acquire accurate information on the changes in tidal flat resources. This research proposes a tidal flat extraction model (RF-W model) that combines the random forest (RF) method and waterline method, which aims to improve the accuracy of tidal flat extraction. This method can effectively eliminate the shortcomings of the RF method in determining the lower boundary of tidal flats and those of the waterline method in distinguishing river channels and tidal flats. The tidal flat extraction of Qingdao Jiaozhou Bay in 2020 is performed as an example of the model. The results show that the user’s and producer’s accuracies of the RF-W model were both the highest, indicating that the improved model can accurately extract tidal flat information. Then, we used the RF-W model to extract tidal flat information for Jiaozhou Bay in seven periods (1990, 1995, 2000, 2005, 2010, 2015, and 2020) and to study the spatiotemporal changes in the tidal flats and influencing factors from 1990 to 2020. The tidal flat area of Jiaozhou Bay showed an overall downward trend before 2015, and the area decreased by 21.9 km2, with a reduction in the rate of approximately 1.1%/year. After 2015, the tidal flat area rebounded slightly. The overall change in Jiaozhou Bay showed reclamation and expansion toward the sea. The reduction in the sand content of the rivers entering the sea, reclamation and cultivation, and land development were the main factors contributing to the reduction in the tidal flat area in Jiaozhou Bay. In addition, sea level rise due to climate warming is a long-term potential factor.
Discrepancies between Global Forest Net Primary Productivity Estimates Derived from MODIS and Forest Inventory Data and Underlying Factors
Authors: Park Jin Han , Gan Jianbang , Park Chan
Published: April 2021

The net primary productivity (NPP) of a forest is an important indicator of its potential for the provision of ecosystem services such as timber, carbon, and biodiversity. However, accurately and consistently quantifying global forest NPP remains a challenge in practice. We converted carbon stock changes using the Global Forest Resources Assessment (FRA) data and carbon losses associated with disturbances and timber removals into an NPP equivalent measurement (FRA NPP*) and compared it with the NPP derived from the MODIS satellite data (MOD17 NPP) for the world’s forests. We found statistically significant differences between the two NPP estimates, with the FRA NPP* being lower than the MOD17 NPP; the differences were correlated with forest cover, normalized difference vegetation index (NDVI), and GDP per capita in countries, and may also stem from the NPP estimation methods and scopes. While the former explicitly accounts for carbon losses associated with timber removals and disturbances, the latter better reflects the principles of photosynthesis. The discrepancies between the two NPP estimates increase in countries with a low income or low forest cover, calling for enhancing their forest resource assessment capacity. By identifying the discrepancies and underlying factors, we also provide new insights into the relationships between the MOD17 NPP and global forest carbon stock estimates, motivating and guiding future research to improve the robustness of quantifying global forest NPP and carbon sequestration potential.
A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR
Authors: Ma Kaisen , Xiong Yujiu , Jiang Fugen , Chen Song , Sun Hua
Published: April 2021

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.
Modeling Mean Radiant Temperature Distribution in Urban Landscapes Using DART
Authors: Dissegna Maria Angela , Yin Tiangang , Wu Hao , Lauret Nicolas , Wei Shanshan , Gastellu-Etchegorry Jean-Philippe , Grêt-Regamey Adrienne
Published: April 2021

The microclimatic conditions of the urban environment influence significantly the thermal comfort of human beings. One of the main human biometeorology parameters of thermal comfort is the Mean Radiant Temperature (Tmrt), which quantifies effective radiative flux reaching a human body. Simulation tools have proven useful to analyze the radiative behavior of an urban space and its impact on the inhabitants. We present a new method to produce detailed modeling of Tmrt spatial distribution using the 3-D Discrete Anisotropic Radiation Transfer model (DART). Our approach is capable to simulate Tmrt at different scales and under a range of parameters including the urban pattern, surface material of ground, walls, roofs, and properties of the vegetation (coverage, shape, spectral signature, Leaf Area Index and Leaf Area Density). The main advantages of our method are found in (1) the fine treatment of radiation in both short-wave and long-wave domains, (2) detailed specification of optical properties of urban surface materials and of vegetation, (3) precise representation of the vegetation component, and (4) capability to assimilate 3-D inputs derived from multisource remote sensing data. We illustrate and provide a first evaluation of the method in Singapore, a tropical city experiencing strong Urban Heat Island effect (UHI) and seeking to enhance the outdoor thermal comfort. The comparison between DART modelled and field estimated Tmrt shows good agreement in our study site under clear-sky condition over a time period from 10:00 to 19:00 (R2 = 0.9697, RMSE = 3.3249). The use of a 3-D radiative transfer model shows promising capability to study urban microclimate and outdoor thermal comfort with increasing landscape details, and to build linkage to remote sensing data. Our methodology has the potential to contribute towards optimizing climate-sensitive urban design when combined with the appropriate tools.
Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin
Authors: Ye Shuchao , Feng Huihui , Zou Bin , Ding Ying , Zhu Sijia , Li Feng , Dong Guotao
Published: April 2021

The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m2 to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m2, 0.21 W/m2, and −0.41 W/m2 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems.
On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine
Authors: Swetnam Tyson L. , Yool Stephen R. , Roy Samapriya , Falk Donald A.
Published: April 2021

In this work we explore three methods for quantifying ecosystem vegetation responses spatially and temporally using Google’s Earth Engine, implementing an Ecosystem Moisture Stress Index (EMSI) to monitor vegetation health in agricultural, pastoral, and natural landscapes across the entire era of spaceborne remote sensing. EMSI is the multitemporal standard (z) score of the Normalized Difference Vegetation Index (NDVI) given as I, for a pixel (x,y) at the observational period t. The EMSI is calculated as: zxyt = (Ixyt − µxyT)/σxyT, where the index value of the observational date (Ixyt) is subtracted from the mean (µxyT) of the same date or range of days in a reference time series of length T (in years), divided by the standard deviation (σxyT), during the same day or range of dates in the reference time series. EMSI exhibits high significance (z > |2.0 ± 1.98σ|) across all geographic locations and time periods examined. Our results provide an expanded basis for detection and monitoring: (i) ecosystem phenology and health; (ii) wildfire potential or burn severity; (iii) herbivory; (iv) changes in ecosystem resilience; and (v) change and intensity of land use practices. We provide the code and analysis tools as a research object, part of the findable, accessible, interoperable, reusable (FAIR) data principles.
The UVSQ-SAT/INSPIRESat-5 CubeSat Mission: First In-Orbit Measurements of the Earth’s Outgoing Radiation
Authors: Meftah Mustapha , Boutéraon Thomas , Dufour Christophe , Hauchecorne Alain , Keckhut Philippe , Finance Adrien , Bekki Slimane , Abbaki Sadok , Bertran Emmanuel , Damé Luc , Engler Jean-Luc , Galopeau Patrick , Gilbert Pierre , Lapauw Laurent , Sarkissian Alain , Vieau André-Jean , Lacroix Patrick , Caignard Nicolas , Arrateig Xavier , Hembise Fanton d’Andon Odile , Mangin Antoine , Carta Jean-Paul , Boust Fabrice , Mahé Michel , Mercier Christophe
Published: April 2021

UltraViolet & infrared Sensors at high Quantum efficiency onboard a small SATellite (UVSQ-SAT) is a small satellite at the CubeSat standard, whose development began as one of the missions in the International Satellite Program in Research and Education (INSPIRE) consortium in 2017. UVSQ-SAT is an educational, technological and scientific pathfinder CubeSat mission dedicated to the observation of the Earth and the Sun. It was imagined, designed, produced and tested by LATMOS in collaboration with its academic and industrial partners, and the French-speaking radioamateur community. About the size of a Rubik’s Cube and weighing about 2 kg, this satellite was put in orbit in January 2021 by the SpaceX Falcon 9 launch vehicle. After briefly introducing the UVSQ-SAT mission, this paper will present the importance of measuring the Earth’s radiation budget and its energy imbalance and the scientific objectives related to its various components. Finally, the first in-orbit observations will be shown (maps of the solar radiation reflected by the Earth and of the outgoing longwave radiation at the top of the atmosphere during February 2021). UVSQ-SAT is one of the few CubeSats worldwide with a scientific goal related to climate studies. It represents a research in remote sensing technologies for Climate observation and monitoring.
Simulation Study of a Frame-Based Motion Correction Algorithm for Positron Emission Imaging
Authors: Espinós-Morató Héctor , Cascales-Picó David , Vergara Marina , Hernández-Martínez Ángel , Benlloch Baviera José María , Rodríguez-Álvarez María José
Published: April 2021

Positron emission tomography (PET) is a functional non-invasive imaging modality that uses radioactive substances (radiotracers) to measure changes in metabolic processes. Advances in scanner technology and data acquisition in the last decade have led to the development of more sophisticated PET devices with good spatial resolution (1–3 mm of full width at half maximum (FWHM)). However, there are involuntary motions produced by the patient inside the scanner that lead to image degradation and potentially to a misdiagnosis. The adverse effect of the motion in the reconstructed image increases as the spatial resolution of the current scanners continues improving. In order to correct this effect, motion correction techniques are becoming increasingly popular and further studied. This work presents a simulation study of an image motion correction using a frame-based algorithm. The method is able to cut the acquired data from the scanner in frames, taking into account the size of the object of study. This approach allows working with low statistical information without losing image quality. The frames are later registered using spatio-temporal registration developed in a multi-level way. To validate these results, several performance tests are applied to a set of simulated moving phantoms. The results obtained show that the method minimizes the intra-frame motion, improves the signal intensity over the background in comparison with other literature methods, produces excellent values of similarity with the ground-truth (static) image and is able to find a limit in the patient-injected dose when some prior knowledge of the lesion is present.
A Task-Driven Feedback Imager with Uncertainty Driven Hybrid Control
Authors: Mudassar Burhan A. , Saha Priyabrata , Wolf Marilyn , Mukhopadhyay Saibal
Published: April 2021

Deep Neural Network (DNN) systems tend to produce overconfident or uncalibrated outputs. This poses problems for active sensor systems that have a DNN module as the main feedback controller. In this paper, we study a closed-loop feedback smart camera from the lens of uncertainty estimation. The uncertainty of the task output is used to characterize and facilitate the feedback operation. The DNN uncertainty in the feedback system is estimated and characterized using both sampling and non-sampling based methods. In addition, we propose a closed-loop control that incorporates uncertainty information when providing feedback. We show two modes of control, one that prioritizes false positives and one that prioritizes false negatives, and a hybrid approach combining the two. We apply the uncertainty-driven control to the tasks of object detection, object tracking, and action detection. The hybrid system improves object detection and tracking accuracy on the CAMEL dataset by 1.1% each respectively. For the action detection task, the hybrid approach improves accuracy by 1.4%.
Simulator for Interactive and Effective Organization of Things in Edge Cluster Computing
Authors: Kim Woojae , Jung Inbum
Published: April 2021

Edge computing is intended to process events that occur at the endpoint of the Internet of Things (IoT) network quickly and intelligently. Edge regions must be organized effectively to facilitate cooperation so that the intention of edge computing can be realized. However, inevitably, many human and material resources are required in the process of arranging things in the edge area to confirm the appropriateness of the thing operation. To address this problem, we proposed a simulator that created a virtual space for edge computing and provided an interactive role and effective organization for edge things. The proposed simulator was aimed at Raspberry Pi as the physical hardware target. To prove the accuracy of the proposed simulator, the similarity between the proposed simulator and the physical target Raspberry Pi was evaluated based on three metrics while executing several applications. In the experiment, several edge-computing service applications were performed in various cluster architecture types formed by the proposed simulator. To support effective resource usage and fast real-time response for edge computing, the proposed simulator identified a suitable number of things in forming the edge cluster.
Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
Authors: Luo Yawen , Chen Yuhua
Published: April 2021

Unmanned Aerial Vehicles (UAVs, also known as drones) have become increasingly appealing with various applications and services over the past years. Drone-based remote sensing has shown its unique advantages in collecting ground-truth and real-time data due to their affordable costs and relative ease of operability. This paper presents a 3D placement scheme for multi-drone sensing/monitoring platforms, where a fleet of drones are sent for conducting a mission in a given area. It can range from environmental monitoring of forestry, survivors searching in a disaster zone to exploring remote regions such as deserts and mountains. The proposed drone placing algorithm covers the entire region without dead zones while minimizing the number of cooperating drones deployed. Naturally, drones have limited battery supplies which need to cover mechanical motions, message transmissions and data calculation. Consequently, the drone energy model is explicitly investigated and dynamic adjustments are deployed on drone locations. The proposed drone placement algorithm is 3D landscaping-aware and it takes the line-of-sight into account. The energy model considers inter-communications within drones. The algorithm not only minimizes the overall energy consumption, but also maximizes the whole drone team’s lifetime in situations where no power recharging facilities are available in remote/rural areas. Simulations show the proposed placement scheme has significantly prolonged the lifetime of the drone fleet with the least number of drones deployed under various complex terrains.
Energy Harvesting Mechanisms in a Smart City—A Review
Authors: Akin-Ponnle Ajibike Eunice , Carvalho Nuno Borges
Published: April 2021

The issue of how to power the deployed Internet of Things (IoT) nodes with ubiquitous and long lasting energy in order to ensure uninterruptible optimisation of smart cities is of utmost concern. This among other challenges has continued to gear efforts toward energy harvesting research. With the outbreak of COVID-19 pandemic and the lockdown that nearly paralysed activities of everyday living in many nations of the world, option of human remote interaction to enforce social distancing became imperative. Hence, the world is witnessing a renewed awareness of the importance of IoT devices, as integral components of smart city, especially for the essence of survival in the face of lockdown. Energy harvesting is a possible solution that could enable IoT nodes to scavenge self-sustaining energy from environmental ambient sources. In this paper, we have reviewed most sources within city that energy could be harvested from, as reported by researchers in literature. In addition, we have submitted that energy sources can be application specific, such that, since there are many free sources in the city as presented in this review, energy should be scavenged within close proximity of need for various IoT devices or wireless sensor networks (WSNs), for smart city automation.
BraveNet Upstander Social Network against Second Order of Sexual Harassment
Authors: Puigvert Lidia , Vidu Ana , Melgar Patricia , Salceda Marifa
Published: April 2021

Gender-based violence and domestic violence constitute a huge problem all across countries and continents. The COVID-19 outbreak and the lockdown produced as a consequence of it have contributed to escalating this problem. Many national organisms reported an increase in the data on domestic violence during confinement. Bystander intervention often constitutes one of the most effective mechanisms of attention. The problem is that bystanders do not always dare to intervene. This article aims to provide knowledge on the reasons for this lack of intervention and its connection to domestic violence, while presenting measures to encourage intervention and victim support, offering protection to those most in need during this pandemic. The research was conducted through questionnaires distributed online among social entities in charge of providing care to women suffering from domestic violence during the lockdown. The results have shown that most of these entities have had to intervene in providing support to women during the lockdown. In conclusion, the case of the Unitary Platform Against Gender Violence and the entities, which are members of the platform, acted in situations of domestic violence produced during confinement, based on the mutual support provided by being a group of entities that have the support of the Platform.
Effects of Climatic Conditions, Season and Environmental Factors on CO2 Concentrations in Naturally Ventilated Primary Schools in Chile
Authors: Diaz Muriel , Cools Mario , Trebilcock Maureen , Piderit-Moreno Beatriz , Attia Shady
Published: April 2021

Between the ages of 6 and 18, children spend between 30 and 42 h a week at school, mostly indoors, where indoor environmental quality is usually deficient and does not favor learning. The difficulty of delivering indoor air quality (IAQ) in learning facilities is related to high occupancy rates and low interaction levels with windows. In non-industrialized countries, as in the cases presented, most classrooms have no mechanical ventilation, due to energy poverty and lack of normative requirements. This fact heavily impacts the indoor air quality and students’ learning outcomes. The aim of the paper is to identify the factors that determine acceptable CO2 concentrations. Therefore, it studies air quality in free-running and naturally ventilated primary schools in Chile, aiming to identify the impact of contextual, occupant, and building design factors, using CO2 concentration as a proxy for IAQ. The monitoring of CO2, temperature, and humidity revealed that indoor air CO2 concentration is above 1400 ppm most of the time, with peaks of 5000 ppm during the day, especially in winter. The statistical analysis indicates that CO2 is dependent on climate, seasonality, and indoor temperature, while it is independent of outside temperature in heated classrooms. The odds of having acceptable concentrations of CO2 are bigger when indoor temperatures are high, and there is a need to ventilate for cooling.
A GIS-MCDA-Based Suitability Analysis for Meeting Targets 6.3 and 6.5 of the Sustainable Development Goals
Authors: Mentzafou Angeliki , Blagojević Momčilo , Dimitriou Elias
Published: April 2021

Among the Sustainable Development Goals (SDGs) established in the 2030 Agenda, goals 6.3, regarding clean water and improve of water quality, and 6.5, regarding integrated water resources management, highlight the need for the implementation of successful environmental water quality monitoring programs of transboundary river waterbodies. In the present study, the designation of high priority areas for water quality monitoring of Drin transboundary watershed is performed using a suitability model, a GIS-based multicriteria decision analysis (GIS-MCDA) approach that takes into consideration the most important conditioning factors that impose pressures on rivers. Based on the results, the methodological approach used manages to sufficiently delimit the areas with increased need for water quality monitoring in the Drin watershed, and the validation procedure produces a correlation coefficient of 0.454 (statistically significant at a 0.01 level). Limitations arise in the case of a lack of detailed information or inaccurate input data and due to the inconsistency among the input data and the different methodological approaches regarding the information collection of each country involved. These restrictions foreground the need for cooperation between the countries involved regarding the exchange of scientific knowledge and common legislation, so as to achieve integrated, effective, and sustainable management of water resources of the area.
Sustainable Supply Chain Decisions under E-Commerce Platform Marketplace with Competition
Authors: Wang Junbin , Gao Xuan , Wang Zhiguo
Published: April 2021

Motivated by the industrial observation that the e-commerce platform marketplaces (e.g., Amazon) are increasingly launching sustainable strategies, this study aims to build an analytical framework to guide managers on making sustainable decisions. This study builds a stylized game-theoretical model in the sustainable supply chain context, where the competitive traditional product manufacturers sell their products through the platform’s marketplace, while the platform decides whether to introduce the green products and the pricing strategy. We find that, when the evaluation difference for the green product is sufficiently low, the introduction of the green product by the platform benefits the manufacturers (or third-party sellers). Interestingly, a higher platform fee makes a higher likelihood of a win-win situation between the platform and manufacturers. Moreover, when consumers value green products sufficiently higher than traditional products, the traditional products’ manufacturers can also benefit from the green product entry.
The Impact of Gender and Years of Teaching Experience on College Teachers’ Digital Competence: An Empirical Study on Teachers in Gansu Agricultural University
Authors: Zhao Yu , Pinto Llorente Ana María , Sánchez Gómez María Cruz , Zhao Liping
Published: April 2021

With the development of Information and Communication Technologies (ICT) and the emergence of the Corona virus (COVID-19,) our way of life and even our education have been affected. The education approach has changed from traditional tutorials to online education. As a result, it has been a necessary for not only students but also teachers to improve their digital competence. The aim of this study was to describe in-service teachers’ self-perceptions of digital competence in the context of higher education and analyze the impact of gender and years of teaching experience on college teachers’ digital competence. For this purpose, a quantitative methodology has been used. A sample of 536 in-service teachers from Gansu Agricultural University, China, completed a questionnaire on digital competence. The data were collected in the academic year of 2019–2020. The results show that the sample considered themselves positively in information and data literacy, communication and collaboration, security and problem solving, while they self-evaluated their digital content creation negatively. Regarding the variables studied, significant differences were found in favor of male college teachers in the perception of digital competence. In relation to the teaching experience, teachers with less teaching experience thought themselves better in the areas of communication and collaboration, digital content creation, security and problem solving.
Esports Spectating Motives and Streaming Consumption: Moderating Effect of Game Genres and Live-Streaming Types
Authors: Ma Shang-Chun , Byon Kevin K. , Jang Wooyoung (William) , Ma Shang-Min , Huang Tsung-Nan
Published: April 2021

Previous studies have paid little attention to spectators’ consumption behaviors and motives for watching different types of esports live-streaming and game genres. This study, therefore, investigates spectator motives and consumption behaviors based on the interaction effects of live-streaming types and game genres. Convenience sampling was conducted to collect 312 responses from Taiwanese individuals via the Professional Technology Temple. The measurement tools include the motivation scale for sport consumption, esports streaming consumption behaviors, and two moderators (i.e., game genres and live-streaming types). The moderating effects were examined using the PROCESS macro. The results showed that esports spectating motives and consumption behaviors are determined by different types of live-streaming and game genres. A matrix of esports spectator segments was developed to illustrate the findings and managerial implications. The study’s findings broaden our understanding of esports consumption behaviors and can contribute to the fast-growing esports marketing literature. In addition, the results are expected to help practitioners better segment their consumer groups to develop more tailored marketing programs.
Scientifically Informed Solidarity: Changing Anti-Immigrant Prejudice about Universal Access to Health
Authors: De Botton Lena , Ramos Raul , Soler-Gallart Marta , Suriñach Jordi
Published: April 2021

Currently, anti-immigrant sentiment has emerged again in European countries, as witnessed, for example, by the rise of xenophobic parties in many member states. This is a prejudice that is not new but that intensifies in certain circumstances, such as the economic crisis. This change in attitudes towards immigration has an impact on the preferences of citizens regarding the universal access to public resources and rights. The results of this article come from the analysis of certain variables of the Transnational European Solidarity Survey (TESS) conducted during 2016 in 13 member countries of the European Union. Specifically, two packages of variables are analysed regarding the degree of solidarity in relation to the access to public health services for immigrants before and after receiving scientific information about the collective benefits of the provision of health for the entire population, including undocumented immigrants. While there is much literature that analyses how scientific literacy in health and education issues improves the situation of vulnerable groups, few studies have analysed how scientific knowledge helps to modify the prejudices and discriminatory attitudes of the general population, thus contributing to the improvement of the health of the entire population.
Improved YOLOv4 Marine Target Detection Combined with CBAM
Authors: Fu Huixuan , Song Guoqing , Wang Yuchao
Published: April 2021

Marine target detection technology plays an important role in sea surface monitoring, sea area management, ship collision avoidance, and other fields. Traditional marine target detection algorithms cannot meet the requirements of accuracy and speed. This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. Marine target detection datasets were collected and produced and marine targets were divided into ten categories, including speedboat, warship, passenger ship, cargo ship, sailboat, tugboat, and kayak. Aiming at the problem of insufficient detection accuracy of YOLOv4’s self-built marine target dataset, a convolutional attention module is added to the YOLOv4 network to increase the weight of useful features while suppressing the weight of invalid features to improve detection accuracy. The experimental results show that the improved YOLOv4 has higher detection accuracy than the original YOLOv4, and has better detection results for small targets, multiple targets, and overlapping targets. The detection speed meets the real-time requirements, verifying the effectiveness of the improved algorithm.
Combining a Genetic Algorithm and a Fuzzy System to Optimize User Centricity in Autonomous Vehicle Concept Development
Authors: Schockenhoff Ferdinand , Zähringer Maximilian , Brönner Matthias , Lienkamp Markus
Published: April 2021

The megatrends of individualization and sharing will dramatically change our consumer behavior. The needs of a product’s users will be central input for its development. Current development processes are not suitable for this product development; thus, we propose a combination of a genetic algorithm and a fuzzy system for user-centered development. We execute our new methodological approach on the example of autonomous vehicle concepts to demonstrate its implementation and functionality. The genetic algorithm minimizes the required number of vehicle concepts to satisfy the mobility needs of a user group, and the fuzzy system transfers user needs into vehicle-related properties, which are currently input for vehicle concept development. To present this method, we use a typical family and their potential mobility behavior. Our method optimizes their minimal number of vehicle concepts to satisfy all mobility needs and derives the properties of the vehicle concepts. By integrating our method into the entire vehicle concept development process, autonomous vehicles can be designed user-centered in the context of the megatrends of individualization and sharing. In summary, our method enables us to derive an optimized number of products for qualitatively described, heterogeneous user needs and determine their product-related properties.
RSSI Probability Density Functions Comparison Using Jensen-Shannon Divergence and Pearson Distribution
Authors: Lionis Antonios , Peppas Konstantinos P. , Nistazakis Hector E. , Tsigopoulos Andreas
Published: April 2021

The performance of a free-space optical (FSO) communications link suffers from the deleterious effects of weather conditions and atmospheric turbulence. In order to better estimate the reliability and availability of an FSO link, a suitable distribution needs to be employed. The accuracy of this model depends strongly on the atmospheric turbulence strength which causes the scintillation effect. To this end, a variety of probability density functions were utilized to model the optical channel according to the strength of the refractive index structure parameter. Although many theoretical models have shown satisfactory performance, in reality they can significantly differ. This work employs an information theoretic method, namely the so-called Jensen–Shannon divergence, a symmetrization of the Kullback–Leibler divergence, to measure the similarity between different probability distributions. In doing so, a large experimental dataset of received signal strength measurements from a real FSO link is utilized. Additionally, the Pearson family of continuous probability distributions is also employed to determine the best fit according to the mean, standard deviation, skewness and kurtosis of the modeled data.
Environmental Risk of Pesticides for Fish in Small- and Medium-Sized Streams of Switzerland
Authors: Werner Inge , Schneeweiss Anke , Segner Helmut , Junghans Marion
Published: April 2021

This study assessed the acute and chronic risk of pesticides, singly and as mixtures, for fish using comprehensive chemical data of four monitoring studies conducted in small- and medium-sized streams of Switzerland between 2012 and 2018. Pesticides were ranked based on single substance risk quotients and relative contribution to mixture risk. Concentrations of the pyrethroid insecticides, λ-cyhalothrin, cypermethrin and deltamethrin, and the fungicides, carbendazim and fenpropimorph, posed acute or chronic single substance risks. Risk quotients of eighteen additional pesticides were equal or greater than 0.1, and thirteen of those contributed ≥30% to mixture risk. Relatively few substances dominated the mixture risk in most water samples, with chronic and acute maximum cumulative ratios never exceeding 5 and 7, respectively. A literature review of toxicity data showed that concentrations of several pesticides detected in Swiss streams were sufficient to cause direct sublethal effects on fish in laboratory studies. Based on the results of our study, we conclude that pesticides detected in Swiss streams, especially pyrethroid insecticides, fungicides and pesticide mixtures, pose a risk to fish health and can cause direct sublethal effects at environmental concentrations. Sensitive life stages of species with highly specialized life history traits may be particularly vulnerable; however, the lack of toxicity data for non-model species currently prevents a conclusive assessment across species.
Degradation Products of Polychlorinated Biphenyls and Their In Vitro Transformation by Ligninolytic Fungi
Authors: Šrédlová Kamila , Šírová Kateřina , Stella Tatiana , Cajthaml Tomáš
Published: April 2021

Metabolites of polychlorinated biphenyls (PCBs)—hydroxylated PCBs (OH-PCBs), chlorobenzyl alcohols (CB-OHs), and chlorobenzaldehydes (CB-CHOs)—were incubated in vitro with the extracellular liquid of Pleurotus ostreatus, which contains mainly laccase and low manganese-dependent peroxidase (MnP) activity. The enzymes were able to decrease the amount of most of the tested OH-PCBs by > 80% within 1 h; the removal of more recalcitrant OH-PCBs was greatly enhanced by the addition of the laccase mediator syringaldehyde. Conversely, glutathione substantially hindered the reaction, suggesting that it acted as a laccase inhibitor. Hydroxylated dibenzofuran and chlorobenzoic acid were identified as transformation products of OH-PCBs. The extracellular enzymes also oxidized the CB-OHs to the corresponding CB-CHOs on the order of hours to days; however, the mediated and nonmediated setups exhibited only slight differences, and the participating enzymes could not be determined. When CB-CHOs were used as the substrates, only partial transformation was observed. In an additional experiment, the extracellular liquid of Irpex lacteus, which contains predominantly MnP, was able to efficiently transform CB-CHOs with the aid of glutathione; mono- and di-chloroacetophenones were detected as transformation products. These results demonstrate that extracellular enzymes of ligninolytic fungi can act on a wide range of PCB metabolites, emphasizing their potential for bioremediation.
Lipid-Based Nanoparticles in the Clinic and Clinical Trials: From Cancer Nanomedicine to COVID-19 Vaccines
Authors: Thi Thai Thanh Hoang , Suys Estelle J. A. , Lee Jung Seok , Nguyen Dai Hai , Park Ki Dong , Truong Nghia P.
Published: April 2021

COVID-19 vaccines have been developed with unprecedented speed which would not have been possible without decades of fundamental research on delivery nanotechnology. Lipid-based nanoparticles have played a pivotal role in the successes of COVID-19 vaccines and many other nanomedicines, such as Doxil® and Onpattro®, and have therefore been considered as the frontrunner in nanoscale drug delivery systems. In this review, we aim to highlight the progress in the development of these lipid nanoparticles for various applications, ranging from cancer nanomedicines to COVID-19 vaccines. The lipid-based nanoparticles discussed in this review are liposomes, niosomes, transfersomes, solid lipid nanoparticles, and nanostructured lipid carriers. We particularly focus on the innovations that have obtained regulatory approval or that are in clinical trials. We also discuss the physicochemical properties required for specific applications, highlight the differences in requirements for the delivery of different cargos, and introduce current challenges that need further development. This review serves as a useful guideline for designing new lipid nanoparticles for both preventative and therapeutic vaccines including immunotherapies.
Human Response to Vehicle Vibrations and Acoustics during Transient Road Excitations
Authors: Festa Maurizio , Stalter Frank , Tavornmas Arm , Gauterin Frank
Published: April 2021

Driving over rising and falling edges on roads and pavements, rails, manhole covers, or transverse joints can influence the driving impression regarding the driver’s perception of vibrations and acoustics. To be able to describe this, objective parameters are used to make the subjective ride comfort measurable and scalable. Previous studies have already contributed to the investigation of the subjective perception regarding the interaction of vibrations and acoustics. However, the results were individual. Aimed at improving the quality of objective analysis methods, driving maneuvers were performed in a real vehicle to investigate the interaction of vibrations and acoustics due to transient road excitations. For this purpose, a sound reproduction system was used, which could provide the acoustic environment for the driver to adapt to while driving. With this method, subjective ratings by varying vibrations and acoustics were collected and with reference to objective parameters statistically evaluated. The results showed that both tactile and audible vibrations under transient influences had no significant interactive effects on the driver’s perception.
Eucalyptus Leaf Solution to Replace Metals in the Removal of Cyanobacteria in Wastewater from the Paper Mill Industry
Authors: Hu Zhewei , Jin Shu , Ying Rongrong , Yang Xiaohui , Sun Baoping
Published: April 2021

The frequent occurrence of cyanobacterial blooms, caused by the eutrophication of water bodies, has triggered several ecological issues. Metal-controlled cyanobacteria are resulting in a series of secondary environmental problems and thus limiting environmental sustainability. Whether there is a more environmentally friendly way to replace metals in the removal of cyanobacteria is still unclear. To explore whether common heavy metals inhibit algal growth and whether Eucalyptus leaves (EL) can replace heavy metal ions in controlling algae outbreaks, here, we add Fe3+, Al3+, 3 mol/L of zinc (Zn3), 10 mol/L zinc (Zn10), and EL to a medium containing Cyanobacteria. We determine the medium’s color (456 nm), UV (254 nm), chlorophyll a, turbidity, temperature, pH, total dissolved solids, conductivity, and blue-green algae (BGA) at days 1, 4, 7, 11, 14, 19, and 21. We find that Fe3+, Al3+, Zn3, Zn10, and EL can inhibit chlorophyll synthesis, thereby impeding algae biomass growth due to metal ions’ disruption of the chlorophyll structure. The toxicity of Zn2+ may be higher than that of Fe3+ and Al3+ since it can completely destroy the structure of chlorophyll a. The damage of Zn (10) to chlorophyll a is stronger than that of Zn (3), indicating that high concentrations of metals have a stronger inhibitory effect on algae. The toxicity of EL to algae is lower than that of other metals, but it can significantly inhibit the growth of algae. We suggest the use of Eucalyptus leaves to inhibit algal growth in eutrophic water bodies. Our results provide a scientific basis for an environmentally friendly approach to controlling cyanobacteria outbreaks.