bluuebunny/crossref_metadata_2025 huggingface dataset:
TANVEER: Tri-Angular Nearest Vector-Based Energy Efficient Routing for IoT-Enabled Acoustic Sensor and Actor Networks (I-ASANs)
Authors: Draz Umar , Yasin Sana , Irfan Muhammad , Ali Tariq , Ali Amjad , Glowacz Adam , Brumercik Frantisek , Glowacz Witold
Published: May 2021

The Internet of Things (IoT) is an emerging technology in underwater communication because of its potential to monitor underwater activities. IoT devices enable a variety of applications such as submarine and navy defense systems, pre-disaster prevention, and gas/oil exploration in deep and shallow water. The IoT devices have limited power due to their size. Many routing protocols have been proposed in applications, as mentioned above, in different aspects, but timely action and energy make these a challenging task for marine research. Therefore, this research presents a routing technique with three sub-sections, Tri-Angular Nearest Vector-Based Energy Efficient Routing (TANVEER): Layer-Based Adjustment (LBA-TANVEER), Data Packet Delivery (DPD-TANVEER), and Binary Inter Nodes (BIN-TANVEER). In TANVEER, the path is selected between the source node and sonobuoys by computing the angle three times with horizontal, vertical, and diagonal directions by using the nearest vector-based approach to avoid the empty nodes/region. In order to deploy the nodes, the LBA-TANVEER is used. Furthermore, for successful data delivery, the DPD-TANVEER is responsible for bypassing any empty nodes/region occurrence. BIN-TANVEER works with new watchman nodes that play an essential role in the path/data shifting mechanism. Moreover, achievable empty regions are also calculated by linear programming to minimize energy consumption and throughput maximization. Different evaluation parameters perform extensive simulation, and the coverage area of the proposed scheme is also presented. The simulated results show that the proposed technique outperforms the compared baseline scheme layer-by-layer angle-based flooding (L2-ABF) in terms of energy, throughput, Packet Delivery Ratio (PDR) and a fraction of empty regions.
Assembly Assistance System with Decision Trees and Ensemble Learning
Authors: Sorostinean Radu , Gellert Arpad , Pirvu Bogdan-Constantin
Published: May 2021

This paper presents different prediction methods based on decision tree and ensemble learning to suggest possible next assembly steps. The predictor is designed to be a component of a sensor-based assembly assistance system whose goal is to provide support via adaptive instructions, considering the assembly progress and, in the future, the estimation of user emotions during training. The assembly assistance station supports inexperienced manufacturing workers, but it can be useful in assisting experienced workers, too. The proposed predictors are evaluated on the data collected in experiments involving both trainees and manufacturing workers, as well as on a mixed dataset, and are compared with other existing predictors. The novelty of the paper is the decision tree-based prediction of the assembly states, in contrast with the previous algorithms which are stochastic-based or neural. The results show that ensemble learning with decision tree components is best suited for adaptive assembly support systems.
Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring
Authors: Laref Rachid , Losson Etienne , Sava Alexandre , Siadat Maryam
Published: May 2021

This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO2]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO2] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.
A Secure and Scalable Smart Home Gateway to Bridge Technology Fragmentation
Authors: Simeoni Ezequiel , Gaeta Eugenio , García-Betances Rebeca I. , Raggett Dave , Medrano-Gil Alejandro M. , Carvajal-Flores Diego F. , Fico Giuseppe , Cabrera-Umpiérrez María Fernanda , Arredondo Waldmeyer María Teresa
Published: May 2021

Internet of Things (IoT) technologies are already playing an important role in our daily activities as we use them and rely on them to increase our abilities, connectivity, productivity and quality of life. However, there are still obstacles to achieving a unique interface able to transfer full control to users given the diversity of protocols, properties and specifications in the varied IoT ecosystem. Particularly for the case of home automation systems, there is a high degree of fragmentation that limits interoperability, increasing the complexity and costs of developments and holding back their real potential of positively impacting users. In this article, we propose implementing W3C’s Web of Things Standard supported by home automation ontologies, such as SAREF and UniversAAL, to deploy the Living Lab Gateway that allows users to consume all IoT devices from a smart home, including those physically wired and using KNX® technology. This work, developed under the framework of the EC funded Plan4Act project, includes relevant features such as security, authentication and authorization provision, dynamic configuration and injection of devices, and devices abstraction and mapping into ontologies. Its deployment is explained in two scenarios to show the achieved technology’s degree of integration, the code simplicity for developers and the system’s scalability: one consisted of external hardware interfacing with the smart home, and the other of the injection of a new sensing device. A test was executed providing metrics that indicate that the Living Lab Gateway is competitive in terms of response performance.
Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach
Authors: Biagi Lyvia , Bertachi Arthur , Giménez Marga , Conget Ignacio , Bondia Jorge , Martín-Fernández Josep Antoni , Vehí Josep
Published: May 2021

The time spent in glucose ranges is a common metric in type 1 diabetes (T1D). As the time in one day is finite and limited, Compositional Data (CoDa) analysis is appropriate to deal with times spent in different glucose ranges in one day. This work proposes a CoDa approach applied to glucose profiles obtained from six T1D patients using continuous glucose monitor (CGM). Glucose profiles of 24-h and 6-h duration were categorized according to the relative interpretation of time spent in different glucose ranges, with the objective of presenting a probabilistic model of prediction of category of the next 6-h period based on the category of the previous 24-h period. A discriminant model for determining the category of the 24-h periods was obtained, achieving an average above 94% of correct classification. A probabilistic model of transition between the category of the past 24-h of glucose to the category of the future 6-h period was obtained. Results show that the approach based on CoDa is suitable for the categorization of glucose profiles giving rise to a new analysis tool. This tool could be very helpful for patients, to anticipate the occurrence of potential adverse events or undesirable variability and for physicians to assess patients’ outcomes and then tailor their therapies.
Sensorless LC Filter Implementation for Permanent Magnet Machine Drive Using Observer-Based Voltage and Current Estimation
Authors: Liang Chia-Ming , Lin Yi-Jen , Chen Jyun-You , Chen Guan-Ren , Yang Shih-Chin
Published: May 2021

For pulse width modulation (PWM) inverter drives, an LC filter can cascade to a permanent magnet (PM) machine at inverter output to reduce PWM-reflected current harmonics. Because the LC filter causes resonance, the filter output current and voltage are required for the sensorless field-oriented control (FOC) drive. However, existing sensors and inverters are typically integrated inside commercial closed-form drives; it is not possible for these drives to obtain additional filter output signals. To resolve this integration issue, this paper proposes a sensorless LC filter state estimation using only the drive inside current sensors. The design principle of the LC filter is first introduced to remove PWM current harmonics. A dual-observer is then proposed to estimate the filter output current and voltage for the sensorless FOC drive. Compared to conventional model-based estimation, the proposed dual-observer demonstrates robust estimation performance under parameter error. The capacitor parameter error shows a negligible influence on the proposed observer estimation. The filter inductance error only affects the capacitor current estimation at high speed. The performance of the sensorless FOC drive using the proposed dual-observer is comparable to the same drive using external sensors for filter voltage and current measurement. All experiments are verified by a PM machine with only 130 μH phase inductance.
Development and Validation of an ISA100.11a Simulation Model for Accurate Industrial WSN Planning and Deployment
Authors: Padrah Zoltan , Pastrav Andra , Palade Tudor , Ratiu Ovidiu , Puschita Emanuel
Published: May 2021

During the planning, design, and optimization of an industrial wireless sensor network (IWSN), the proposed solutions need to be validated and evaluated. To reduce the time and expenses, highly accurate simulators can be used for these tasks. This paper presents the development and experimental validation of an ISA100.11a simulation model for industrial wireless sensor networks (IWSN). To achieve high simulation accuracy, the ISA100.11a software stack running on two types of certified devices (i.e., an all-in-one gateway and a field device) is integrated with the ns-3 simulator. The behavior of IWSNs is analyzed in four different types of test scenarios: (1) through simulation using the proposed ISA100.11a simulation model, (2) on an experimental testbed using ISA100.11a certified devices, (3) in a Gateway-in-the-loop Hardware-in-the-loop (HIL) scenario, and (4) in a Node-in-the-loop HIL scenario. Moreover, the scalability of the proposed simulation model is evaluated. Several metrics related to the timing of events and communication statistics are used to evaluate the behavior and performance of the tested IWSNs. The results analysis demonstrates the potential of the proposed model to accurately predict IWSN behavior.
A Review of the Use of Carbon Nanotubes and Graphene-Based Sensors for the Detection of Aflatoxin M1 Compounds in Milk
Authors: Gao Jingrong , He Shan , Nag Anindya , Wong Jonathan Woon Chung
Published: May 2021

This paper presents a comprehensive review of the detection of aflatoxin compounds using carbon allotrope-based sensors. Although aflatoxin M1 and its derivative aflatoxin B1 compounds have been primarily found in milk and other food products, their presence above a threshold concentration causes disastrous health-related anomalies in human beings, such as growth impairment, underweight and even carcinogenic and immunosuppressive effects. Among the many sensors developed to detect the presence of these compounds, the employment of certain carbon allotropes, such as carbon nanotubes (CNTs) and graphene, has been highly preferred due to their enhanced electromechanical properties. These conductive nanomaterials have shown excellent quantitative performance in terms of sensitivity and selectivity for the chosen aflatoxin compounds. This paper elucidates some of the significant examples of the CNTs and graphene-based sensors measuring Aflatoxin M1 (ATM1) and Aflatoxin B1 (AFB1) compounds at low concentrations. The fabrication technique and performance of each of the sensors are shown here, as well as some of the challenges existing with the current sensors.
Sensing Applications in Aircrafts Using Polymer Optical Fibres
Authors: Lallana Pedro C. , Aldabaldetreku Gotzon , López Alicia , Montero David S. , Durana Gaizka , Mateo Javier , Losada M. Ángeles , Zubia Joseba , Vázquez Carmen
Published: May 2021

We report on recent advances in the use of inexpensive polymer optical fibres (POFs) for sensing applications in avionics. The sensors analysed in this manuscript take advantage of the unique properties of polymers, such as high flexibility, elasticity, and sensitivity, and they range from strain, elongation, and vibration interrogators to level and temperature meters, leading to cost-effective techniques for structural health monitoring in aircraft structures. We also highlight recent power-supply methods using Power-over-POF in order to feed sensors remotely, and we discuss the constraints imposed by connectors on the performance of POF networks in aircrafts.
OctoPath: An OcTree-Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots
Authors: Trăsnea Bogdan , Ginerică Cosmin , Zaha Mihai , Măceşanu Gigel , Pozna Claudiu , Grigorescu Sorin
Published: May 2021

Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab’s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method.
Multi-Spectral Fusion and Denoising of Color and Near-Infrared Images Using Multi-Scale Wavelet Analysis
Authors: Su Haonan , Jung Cheolkon , Yu Long
Published: May 2021

We formulate multi-spectral fusion and denoising for the luminance channel as a maximum a posteriori estimation problem in the wavelet domain. To deal with the discrepancy between RGB and near infrared (NIR) data in fusion, we build a discrepancy model and introduce the wavelet scale map. The scale map adjusts the wavelet coefficients of NIR data to have the same distribution as the RGB data. We use the priors of the wavelet scale map and its gradient as the contrast preservation term and gradient denoising term, respectively. Specifically, we utilize the local contrast and visibility measurements in the contrast preservation term to transfer the selected NIR data to the fusion result. We also use the gradient of NIR wavelet coefficients as the weight for the gradient denoising term in the wavelet scale map. Based on the wavelet scale map, we perform fusion of the RGB and NIR wavelet coefficients in the base and detail layers. To remove noise, we model the prior of the fused wavelet coefficients using NIR-guided Laplacian distributions. In the chrominance channels, we remove noise guided by the fused luminance channel. Based on the luminance variation after fusion, we further enhance the color of the fused image. Our experimental results demonstrated that the proposed method successfully performed the fusion of RGB and NIR images with noise reduction, detail preservation, and color enhancement.
Physical Activity during Weekdays and Weekends in Persons with Multiple Sclerosis
Authors: Sagawa Yoshimasa , Watelain Eric , Moulin Thierry , Decavel Pierre
Published: May 2021

The assessment of the functional performance status of persons with multiple sclerosis (PwMS) is a useful tool to optimize healthcare. This concept does not seem to be extensively explored in this population. This study aimed to determine the level of activity of PwMS during weekdays and weekends, and to establish associations between clinical parameters. Forty-one PwMS and 16 healthy persons participated in this study. Their physical activity in real-life conditions was assessed with an accelerometer. For the clinical evaluations, the quality of life, fatigue, gait, and balance were assessed. The level of activity between PwMS for weekdays, weekends, Saturdays, and Sundays was significantly reduced compared with the reference group (p = 0.001–0.00001, d = 0.95–1.76). PwMS had a constant level of activity throughout the week, whereas the reference group increased its level of activity on Saturdays (p = 0.04, d = 0.69). The level of activity was correlated in descending order with multiple sclerosis disability, body mass index, gait velocity, six-minute walk test, and timed up and go test. This study showed that PwMS had a stable level of activity throughout the week, contrary to healthy persons. It could be necessary to develop programs to facilitate physical activity and participation during the weekdays, but especially during weekends.
A Low-Cost Instrumentation System for Seismic Hazard Assessment in Urban Areas
Authors: Papanikolaou Vassilis K. , Karakostas Christos Z. , Theodoulidis Nikolaos P.
Published: May 2021

The development and application of a low-cost instrumentation system for seismic hazard assessment in urban areas are described in the present study. The system comprises a number of autonomous triaxial accelerographs, designed and manufactured in house and together with dedicated software for device configuration, data collection and further postprocessing. The main objective is to produce a detailed view of strong motion variability in urban areas, for at least light intensity strong motion events. The overall cost of the developed devices is at least ten times lower than the respective commercial units, hence their deployment as an ultra-dense network over the area of interest can be significantly cost-effective. This approach is considered an efficient complement to traditional microzonation procedures, which are typically based on relatively few actual recordings and the application of theoretical methodologies to assess the strong motion distribution. The manufactured devices adopt micro-electro-mechanical (MEMS) digital sensor technology for recording acceleration, whereas the accompanying software suite provides various configuration options, quick browsing, analyzing and exporting of the recorded events, as well as GIS type functionality for seamlessly producing explicit seismic hazard maps of the considered area. The evaluation of system performance was based on shaking table and real field comparisons against high accuracy commercial accelerographs. The study concludes with a real application of the proposed system in the form of an ultra-dense network installed at the city of Lefkada, an earthquake prone urban area in Greece, and the following compilation of explicit shakemaps.
EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee
Authors: Coker Jordan , Chen Howard , Schall Mark C. , Gallagher Sean , Zabala Michael
Published: May 2021

Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm’s prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy.
An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification
Authors: Jin Bo , Fu Chunling , Jin Yong , Yang Wei , Li Shengbin , Zhang Guangyao , Wang Zheng
Published: May 2021

Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions. In recent years, unsupervised feature selection algorithms have attracted considerable attention in the field of gene selection as they can find the most discriminating subsets of genes, namely the potential information in biological data. Recent research also shows that maintaining the important structure of data is necessary for gene selection. However, most current feature selection methods merely capture the local structure of the original data while ignoring the importance of the global structure of the original data. We believe that the global structure and local structure of the original data are equally important, and so the selected genes should maintain the essential structure of the original data as far as possible. In this paper, we propose a new, adaptive, unsupervised feature selection scheme which not only reconstructs high-dimensional data into a low-dimensional space with the constraint of feature distance invariance but also employs ℓ2,1-norm to enable a matrix with the ability to perform gene selection embedding into the local manifold structure-learning framework. Moreover, an effective algorithm is developed to solve the optimization problem based on the proposed scheme. Comparative experiments with some classical schemes on real tumor datasets demonstrate the effectiveness of the proposed method.
SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms
Authors: Anzolin Alessandra , Toppi Jlenia , Petti Manuela , Cincotti Febo , Astolfi Laura
Published: May 2021

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user’s needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.
A Tubular Flexible Triboelectric Nanogenerator with a Superhydrophobic Surface for Human Motion Detecting
Authors: Wang Jianwei , Zhao Zhizhen , Zeng Xiangwen , Liu Xiyu , Hu Youfan
Published: May 2021

The triboelectric nanogenerator (TENG) is a newly arisen technology for mechanical energy harvesting from the environment, such as raindrops, wind, tides, and so on. It has attracted widespread attention in flexible electronics to serve as self-powered sensors and energy-harvesting devices because of its flexibility, durability, adaptability, and multi-functionalities. In this work, we fabricated a tubular flexible triboelectric nanogenerator (TF-TENG) with energy harvesting and human motion monitoring capabilities by employing polydimethylsiloxane (PDMS) as construction material, and fluorinated ethylene propylene (FEP) films coated with Cu as the triboelectric layer and electrode, serving in a free-standing mode. The tube structure has excellent stretchability that can be stretched up to 400%. Modifying the FEP films to obtain a superhydrophobic surface, the output performance of TF-TENG was increased by at least 100% compared to an untreated one. Finally, as the output of TF-TENG is sensitive to swing angle and frequency, demonstration of real-time monitoring of human motion state was realized when a TF-TENG was worn on the wrist.
The Role of Local Citizen Energy Communities in the Road to Carbon-Neutral Power Systems: Outcomes from a Case Study in Portugal
Authors: Algarvio Hugo
Published: May 2021

Global warming contributes to the worldwide goal of a sustainable carbon-neutral society. Currently, hydroelectric, wind and solar power plants are the most competitive renewable technologies. They are limited to the primary resource availability, but while hydroelectric power plants (HPPs) can have storage capacity but have several geographical limitations, wind and solar power plants have variable renewable energy (VRE) with stochastic profiles, requiring a substantially higher investment when equipped with battery energy storage systems. One of the most affordable solutions to compensate the stochastic behaviour of VRE is the active participation of consumers with demand response capability. Therefore, the role of citizen energy communities (CECs) can be important towards a carbon-neutral society. This work presents the economic and environmental advantages of CECs, by aggregating consumers, prosumers and VRE at the distribution level, considering microgrid trades, but also establishing bilateral agreements with large-scale VRE and HPPs, and participating in electricity markets. Results from the case-study prove the advantages of CECs and self-consumption. Currently, CECs have potential to be carbon-neutral in relation to electricity consumption and reduce consumers’ costs with its variable term until 77%. In the future, electrification may allow CECs to be fully carbon-neutral, if they increase their flexibility portfolio.
No-Till and Solid Digestate Amendment Selectively Affect the Potential Denitrification Activity in Two Mediterranean Orchard Soils
Authors: Monti Michele , Badagliacca Giuseppe , Romeo Maurizio , Gelsomino Antonio
Published: May 2021

Improved soil managements that include reduced soil disturbance and organic amendment incorporation represent valuable strategies to counteract soil degradation processes that affect Mediterranean tree cultivations. However, changes induced by these practices can promote soil N loss through denitrification. Our research aimed to investigate the short-term effects of no-tillage and organic amendment with solid anaerobic digestate on the potential denitrification in two Mediterranean orchard soils showing contrasting properties in terms of texture and pH. Denitrifying enzyme activity (DEA) and selected soil variables (available C and N, microbial biomass C, basal respiration) were monitored in olive and orange tree orchard soils over a five-month period. Our results showed that the application of both practices increased soil DEA, with dynamics that varied according to the soil type. Increased bulk density, lowered soil aeration, and a promoting effect on soil microbial community growth were the main DEA triggers under no-tillage. Conversely, addition of digestate promoted DEA by increasing readily available C and N with a shorter effect in the olive grove soil, due to greater sorption and higher microbial efficiency, and a long-lasting consequence in the orange orchard soil related to a larger release of soluble substrates and their lower microbial use efficiency.
Single and Binary Fe- and Al-hydroxides Affect Potential Phosphorus Mobilization and Transfer from Pools of Different Availability
Authors: Gypser Stella , Schütze Elisabeth , Freese Dirk
Published: May 2021

Phosphorus (P) fixation is a global problem for soil fertility and negatively impacts agricultural productivity. This study characterizes P desorption of already fixed P by using KCl, KNO3, histidine, and malic acid as inorganic and organic compounds, which are quite common in soil. Goethite, gibbsite, and ferrihydrite, as well as hydroxide mixtures with varying Fe- and Al-ratio were selected as model substances of crystalline and amorphous Fe- and Al-hydroxides. Especially two- and multi-component hydroxide systems are common in soils, but they have barely been included in desorption studies. Goethite showed the highest desorption in the range from 70.4 to 81.0%, followed by gibbsite with values in the range from 50.7 to 42.6%. Ferrihydrite had distinctive lower desorption in the range from 11.8 to 1.9%. Within the group of the amorphous Fe-Al-hydroxide mixtures, P desorption was lowest at the balanced mixture ratio for 1 Fe: 1 Al, increased either with increasing Fe or Al amount. Precipitation and steric effects were concluded to be important influencing factors. More P was released by crystalline Fe-hydroxides, and Al-hydroxides of varying crystallinity, but desorption using histidine and malic acid did not substantially influence P desorption compared to inorganic constituents.
Earthquake Evacuation Choice and Management in a Developing Archipelagic Country—A Case Study of Surigao City, Philippines
Authors: Calumba Sherwin Roy , Rith Monorom , Fillone Alexis M.
Published: May 2021

The Philippines is a developing archipelagic country in Southeast Asia. The country is susceptible to multiple natural disasters, specifically earthquakes. This implies the significance of understanding earthquake evacuation choice in order to design effective planning and management of evacuation to minimize chaos, damage cost, and the loss of lives. This study investigated the determinants of earthquake evacuation and proposed earthquake evacuation planning and management in the Philippines, featuring the case study of Surigao City. The study used the primary dataset of 1055 observations gathered in 2019 and applied the nested logit model (NLM) to investigate the potential factors of earthquake evacuation decisions. We considered three output variables: evacuation choice, evacuation duration, and travel mode choice. We found that residents were more likely to evacuate their homes upon receiving an earthquake warning and move to a public shelter or open space. Additionally, respondents were more inclined to leave their homes when their houses suffered from moderate to severe/complete damage or when electricity and water supply were cut-off. Respondents were most likely to walk to evacuation centers as the majority of residents initially moved to the nearest open space immediately after an earthquake and stayed in an open space for less than 6 hours. No correlation was found between personal and household income factors with evacuation choice and travel mode choice. Furthermore, the study used the Analytical Hierarchy Process (AHP) to determine areas suitable for earthquake evacuation using insights from local government officials and planners. The areas identified for earthquake evacuation were developed to support evacuation planning and management.
How Do Energy-Economy Models Compare? A Survey of Model Developers and Users in Canada
Authors: Rhodes Ekaterina , Craig Kira , Hoyle Aaron , McPherson Madeleine
Published: May 2021

Governments at all levels rely on energy-economy models to design climate policy portfolios. Models vary in their purposes and methodologies, yet there is limited research comparing model characteristics and identifying models suitable for specific policy questions. We conduct a web-based survey of energy-economy model users and developers (n = 14) in Canada’s public, private, and non-profit sectors, to systematically compare seventeen models against the following characteristics: Technology representations, microeconomic and macroeconomic details, policy representations, treatment of uncertainty, high-resolution spatial and temporal representations, and data transparency. We find that for the most part, models represent technology, micro-, and macroeconomic characteristics according to the typology of bottom-up, top-down, and hybrid models. However, several modelling evolutions have emerged. To varying extents, top-down models can explicitly represent technologies and some bottom-up models incorporate microeconomic (non-financial) characteristics. We find that models differ in the types of policies they can simulate, sometimes underrepresenting performance regulations, government procurement, and research and development programs. All models use at least one method to explore uncertainty, rarely incorporate spatial and temporal representations, and most models lack publicly available methodological documentation. We discuss the implications of our comparative model analysis for climate policy projections and future research.
Living Labs in University-Industry Cooperation as a Part of Innovation Ecosystem: Case Study of South Korea
Authors: Shvetsova Olga A. , Lee Sang-Kon
Published: May 2021

Living labs are an extremely attractive opportunity for Korean companies to enhance collaborative research and transform development activities targeting the complexity of today’s economic changes. However, although there is plenty of technological support for collaboration, Korean companies still lack clear understanding how to improve business processes in the living lab ecosystem. The main problem is that the development of living labs in Korea is in the early stages due to the significant market regulation system and the gap between the innovation system and market regulation. The purpose of this study was to investigate the influence of living labs on the innovation ecosystem of South Korea, therefore the research is focused on the innovation process within university–industry cooperation. The focus is on the structures, objectives, and drivers of the collaborative innovation ecosystem and elements of living labs. The expert method was applied and the interviews were arranged with 30 executives of high-tech start-up domestic projects; the main hypothesis was that different elements of living labs have direct and positive impacts on the development of the innovation ecosystem; additionally. A case study of the local university–industry environment was conducted, and the evaluation showed that there is a positive correlation innovation networks and living lab structures.
The Sustainability of Intangible Heritage in the COVID-19 Era—Resilience, Reinvention, and Challenges in Spain
Authors: Roigé Xavier , Arrieta-Urtizberea Iñaki , Seguí Joan
Published: May 2021

The public health restrictions and social distancing imposed as a consequence of COVID-19 have not only had a profound impact on intangible heritage, they have also prompted resilience, reinvention, and creativity. This analysis of the period provides an insight into the social significance of intangible heritage and its adaptability and ability to evolve, while also raising questions about its sustainability. This article tackles the impact of lockdown and public health restrictions on the festivals included in the UNESCO Representative List of Intangible Heritage in Spain. Employing qualitative and ethnographic methodology, the study analyzes the effects of restrictions on the 18 elements on the UNESCO list and the responses adopted; it also includes case studies on three elements. The article concludes that in the post-COVID-19 period, it will be necessary to rethink the economic and social sustainability of intangible heritage practices and to discover new ways of managing them. It will also be necessary to go back to more local formats that are less crowded and less dependent on tourism. The pandemic has exposed the fragility of intangible heritage, and it is now time to rethink the perhaps excessive growth it has experienced in recent years.
Autonomic Stress Response of Nurse Students in an Objective Structured Clinical Examination (OSCE)
Authors: Sánchez-Conde Paula , Clemente-Suárez Vicente Javier
Published: May 2021

The aim of the present research was to analyze the autonomic stress response of nursing students in a nursing Objective Structured Clinical Evaluation (OSCE). We analyzed, in 41 s-year nursing degree students (20.1 ± 2.3 years), modifications in heart rate variability (HRV) to monitor the autonomic stress response before, during and after the 18 different scenarios of a complete OSCE. Heart rate mean response of nurse students was consistent with an anticipatory anxiety response at the beginning of the OSCE, showing a sympathetic nervous system activation, but HRV parameters show contradictory results. The most stressful OSCE station was the CPR maneuver, the stress response varying according to the station’s demands.
How Can Rail Use for Leisure and Tourism Be Promoted? Using Leisure and Mobility Orientations to Segment Swiss Railway Customers
Authors: Dang Lisa , von Arx Widar
Published: May 2021

This study aims to explore how customer segments are characterised by leisure and mobility orientations. By means of a survey, Swiss rail customers’ socioeconomic and trip profiles as well as attitudes towards taking the rail in their leisure time were collected. Respondents were free to take part in the survey by telephone or online, resulting in 1235 full interviews. A principal component analysis followed by segmentation through cluster analysis was conducted to derive their socio-economic and trip profiles and collect data on their attitudes towards using rail in their leisure time. Five railway customer segments were established, each demonstrating typical leisure orientations and specific attitudes towards the use of rail in leisure time and on tourist trips. This paper contributes to understanding the salient preferences of each segment and provides transport companies with clear indications for developing market-oriented offerings.
An Assessment of Forest Fires and CO2 Gross Primary Production from 1991 to 2019 in Mação (Portugal)
Authors: Fernandez Helena Maria , Granja-Martins Fernando M. , Pedras Celestina M.G. , Fernandes Patrícia , Isidoro Jorge M.G.P.
Published: May 2021

Forest-fire rates have increased in Southern European landscapes. These fires damage forest ecosystems and alter their development. During the last few decades, an increase in fast-growing and highly fuel-bearing plant species such as bush, Eucalyptus globulus Labill., and Pinus pinaster Ait. has been observable in the interior of Portugal. This study aims to verify this assumption by the quantification of the biomass carbon sink in the forests of the Mação municipality. Maps of fire severity and forest biomass evolution after a wildfire event were produced for the period of 1991 to 2019. To quantify carbon retention in this region, this evolution was correlated with gross primary production (GPP) on the basis of satellite imagery from Landsat 5, Landsat 8, and MODIS MYD17A2H. Results show that wildfires in Mação increased in area and severity with each passing decade due to the large accumulation of biomass promoted by the abandonment of rural areas. Before the large fires of 2003, 2017, and 2019, carbon rates reached a daily maximum of 5.4, 5.3, and 4.7 gC/m2/day, respectively, showing a trend of forest-biomass accumulation in the Mação municipality.
The Relation between Collaborative Consumption and Subjective Well-Being: An Analysis of P2P Accommodation
Authors: Toni Martina , Renzi Maria Francesca , Di Pietro Laura , Guglielmetti Mugion Roberta , Mattia Giovanni
Published: May 2021

The present paper investigates the relationships between Collaborative Consumption (CC) and well-being. Specifically, the study aims to understand the antecedents of subjective well-being (SWB) in peer-to-peer (P2P) accommodation services. The research adopts a mixed-method approach by integrating qualitative (focus groups) and quantitative (survey) methods with sequential logic. A conceptual model was developed and validated through structural equation modelling (SEM). The model confirms that the SWB, which has three indirect antecedents (the social, environmental and economic dimensions), was positively influenced by the attitude towards P2P accommodation services, and the relation between this construct and SWB emphasizes the originality of the study. This research contributes to the current sharing economy debate, unveiling theoretical advances on the link between collaborative services and well-being.
Impact of the COVID-19 Pandemic on the Energy Use at the University of Almeria (Spain)
Authors: Chihib Mehdi , Salmerón-Manzano Esther , Chourak Mimoun , Perea-Moreno Alberto-Jesus , Manzano-Agugliaro Francisco
Published: May 2021

The COVID-19 pandemic has caused chaos in many sectors and industries. In the energy sector, the demand has fallen drastically during the first quarter of 2020. The University of Almeria campus also declined the energy consumption in 2020, and through this study, we aimed to measure the impact of closing the campus on the energy use of its different facilities. We built our analysis based upon the dataset collected during the year 2020 and previous years; the patterns evolution through time allowed us to better understand the energy performance of each facility during this exceptional year. We rearranged the university buildings into categories, and all the categories reduced their electricity consumption share in comparison with the previous year of 2019. Furthermore, the portfolio of categories presented a wide range of ratios that varied from 56% to 98%, the library category was found to be the most influenced, and the research category was found to be the least influenced. This opened questions like why some facilities were influenced more than others? What can we do to reduce the energy use even more when the facilities are closed? The university buildings presented diverse structures that revealed differences in energy performance, which explained why the impact of such an event (COVID-19 pandemic) is not necessarily relevant to have equivalent variations. Nevertheless, some management deficiencies were detected, and some energy savings measures were proposed to achieve a minimum waste of energy.
Analysis of the Impact of Ride-Hailing Services on Motor Vehicles Crashes in Madrid
Authors: Flor María , Ortuño Armando , Guirao Begoña , Casares Jairo
Published: May 2021

In most cities, discretionary passenger transport by car is predominantly supplied by taxi services. These services face competition from new digital platforms (UBER, Cabify, etc.) that connect users with the services offered by authorized drivers with a license for rented vehicles with drivers (VTC). However, very little is known about the impacts that these services produce in cities where they operate. So far, most studies on this issue have focused on cities of the United States of America, and they broadly found a positive impact in terms of road safety. Road safety has become one of the priority focuses for ensuring social welfare, to the point of being integrated into the Sustainable Development Goals as a primary value to achieve sustainable, safe and responsible mobility. Within this context, the objective of this paper is to analyze the impact of ride-hailing platforms on the frequency of traffic accidents with at least one fatally or seriously injured person in the municipality of Madrid from 2014 to 2018. To do this, a regression analysis has been carried out using a random effects negative binomial regression (RENB). The results of the model show that Uber and Cabify services are associated with a decrease in fatal and serious accidents in Madrid.
Corrosion Resistance of Open Die Forged Austenitic Stainless Steel Samples Prepared with Different Surfaces
Authors: Keran Zdenka , Stojanović Ivan , Novak Amalija Horvatić , Runje Biserka , Razumić Andrej , Vidović Denis
Published: May 2021

The use of corrosion-resistant metal materials in highly aggressive environments contributes to the preservation of the environment because it reduces the use of protective agents and coatings. Most metal objects are produced by some metal-forming process. It is well-known that plastic deformation affects the corrosion resistance of different metal materials in different ways. As a rule, austenitic stainless steels show a positive impact of plastic deformation on corrosion resistance, especially when hot deformed with protective surface oxide layers. However, most research carried out on these metals involves a carefully prepared surface which is either finely ground or polished. This paper investigates the corrosion resistance of cold-formed (open die forged) austenitic stainless steel in three different surface states for three different degrees of deformation. In doing so, we simulate possible damage to the treated surface and evaluate the stability of the material with respect to corrosion. Good corrosion resistance is shown for all three stages of deformation and for all three surface states, with some differences in the obtained results. Although the polished surface shows the highest corrosion resistance, as expected, the other two surfaces also demonstrate good results when exposed to aggressive environments. All of the results were statistically processed and presented. The results demonstrate the high usability of such materials in corrosion-aggressive environments with minimal danger of corrosion and minimal need to include additional surface protection agents, even against possible surface damage.