bluuebunny/crossref_metadata_2025 huggingface dataset:
Kinematic Analysis of the Postural Demands in Professional Soccer Match Play Using Inertial Measurement Units
Authors: Oliva-Lozano José M. , Maraver Elisa F. , Fortes Víctor , Muyor José M.
Published: October 2020

The development of wearable sensors has allowed the analysis of trunk kinematics in match play, which is necessary for a better understanding of the postural demands of the players. The aims of this study were to analyze the postural demands of professional soccer players by playing position. A longitudinal study for 13 consecutive microcycles, which included one match per microcycle, was conducted. Wearable sensors with inertial measurement units were used to collect the percentage (%) of playing time spent and G-forces experienced in different trunk inclinations and the inclination required for different speeds thresholds. The inclination zone had a significant effect on the time percentage spent on each zone (p < 0.001, partial eta-squared (ηp2 = 0.85) and the G-forces experienced by the players (p < 0.001, ηp2 = 0.24). Additionally, a significant effect of the speed variable on the trunk inclination zones was found, since trunk flexion increased with greater speeds (p < 0.001; ηp2 = 0.73), except for midfielders. The players spent most of the time in trunk flexion between 20° and 40°; the greatest G-forces were observed in trunk extension zones between 0° and 30°, and a linear relationship between trunk inclination and speed was found. This study presents a new approach for the analysis of players’ performance. Given the large volumes of trunk flexion and the interaction of playing position, coaches are recommended to incorporate position-specific training drills aimed to properly prepare the players for the perception-action demands (i.e., visual exploration and decision-making) of the match, as well as trunk strength exercises and other compensatory strategies before and after the match.
Temporal and Spatial Focusing in SPAD-Based Solid-State Pulsed Time-of-Flight Laser Range Imaging
Authors: Kostamovaara Juha , Jahromi Sahba S. , Keränen Pekka
Published: October 2020

The relation between signal and background noise strengths in single-photon avalanche diode (SPAD)-based pulsed time-of-flight 3-D range imaging is analyzed on the assumption that the SPAD detector is operating in the single photon detection mode. Several practical measurement cases using a 256-pixel solid-state pulsed time-of-flight (TOF) line profiler are presented and analyzed in the light of the resulting analysis. It is shown that in this case it is advantageous to concentrate the available optical average power in short, intensive pulses and to focus the optical energy in spatial terms. In 3-D range imaging, this could be achieved by using block-based illumination instead of the regularly used flood illumination. One modification of this approach could be a source that would illuminate the system FOV only in narrow laser stripes. It is shown that a 256-pixel SPAD-based pulsed TOF line profiler following these design principles can achieve a measurement range of 5–10 m to non-cooperative targets at a rate of ~10 lines/s under bright sunlight conditions using an average optical power of only 260 µW.
Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions
Authors: Adesipo Adegbite , Fadeyi Oluwaseun , Kuca Kamil , Krejcar Ondrej , Maresova Petra , Selamat Ali , Adenola Mayowa
Published: October 2020

Attention has shifted to the development of villages in Europe and other parts of the world with the goal of combating rural–urban migration, and moving toward self-sufficiency in rural areas. This situation has birthed the smart village idea. Smart village initiatives such as those of the European Union is motivating global efforts aimed at improving the live and livelihood of rural dwellers. These initiatives are focused on improving agricultural productivity, among other things, since most of the food we eat are grown in rural areas around the world. Nevertheless, a major challenge faced by proponents of the smart village concept is how to provide a framework for the development of the term, so that this development is tailored towards sustainability. The current work examines the level of progress of climate smart agriculture, and tries to borrow from its ideals, to develop a framework for smart village development. Given the advances in technology, agricultural development that encompasses reduction of farming losses, optimization of agricultural processes for increased yield, as well as prevention, monitoring, and early detection of plant and animal diseases, has now embraced varieties of smart sensor technologies. The implication is that the studies and results generated around the concept of climate smart agriculture can be adopted in planning of villages, and transforming them into smart villages. Hence, we argue that for effective development of the smart village framework, smart agricultural techniques must be prioritized, viz-a-viz other developmental practicalities.
Semicircular Patch-Embedded Vivaldi Antenna for Miniaturized UWB Radar Sensors
Authors: Seo Jungwoo , Kim Jae Hee , Oh Jungsuek
Published: October 2020

A microstrip-to-slot line-fed miniaturized Vivaldi antenna using semicircular patch embedment is proposed in this study. The conventional Vivaldi antenna has ultrawide bandwidth, but suffers from low gain in the low-frequency band. The proposed antenna topology incorporates the embedment of semicircular patch elements into the side edge of the antenna. This enables the phases of electric fields at both ends of the antenna to be out of phase. Since the distance between the two ends are λL/2 where λL is the wavelength at a low operating frequency, this antenna topology can achieve the constructive addition of electrical fields at the radiating end, leading to gain enhancement at the chosen low frequency. In comparison with the conventional Vivaldi antenna, the proposed antenna has a wider bandwidth from 2.84 to 9.83 GHz. Moreover, the simulated result shows a gain enhancement of 5 dB at low frequency. This cannot be realized by the conventional low-band impedance matching techniques only relying on slotted topologies. The measured results of this proposed antenna with a size of 45 × 40 × 0.8 mm3 are in good agreement with the simulated results.
Policy-Gradient and Actor-Critic Based State Representation Learning for Safe Driving of Autonomous Vehicles
Authors: Gupta Abhishek , Khwaja Ahmed Shaharyar , Anpalagan Alagan , Guan Ling , Venkatesh Bala
Published: October 2020

In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method takes the learning loss into account under deterministic as well as stochastic policy gradient. Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. Our proposed technique exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. To ensure the effectiveness of the scheme over a sustained period of time, we employ a reward-penalty based system where a negative reward is associated with an unfavourable action and a positive reward is awarded for favourable actions. The results obtained through simulations on DonKey simulator show the effectiveness of our proposed method by examining the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.
EMG-Free Monitorization of the Acoustic Startle Reflex with a Mobile Phone: Implications of Sound Parameters with Posture Related Responses
Authors: Gowen Christopher L. , Khwaounjoo Prashanna , Cakmak Yusuf O.
Published: October 2020

(1) Background: Acute acoustic (sound) stimulus prompts a state of defensive motivation in which unconscious muscle responses are markedly enhanced in humans. The orbicularis oculi (OO) of the eye is an easily accessed muscle common for acoustic startle reaction/response/reflex (ASR) investigations and is the muscle of interest in this study. Although the ASR can provide insights about numerous clinical conditions, existing methodologies (Electromyogram, EMG) limit the usability of the method in real clinical conditions. (2) Objective: With EMG-free muscle recording in mind, our primary aim was to identify and investigate potential correlations in the responses of individual and cooperative OO muscles to various acoustic stimuli using a mobile and wire-free system. Our secondary aim was to investigate potential altered responses to high and also relatively low intensity acoustics at different frequencies in both sitting and standing positions through the use of biaural sound induction and video diagnostic techniques and software. (3) Methods: This study used a mobile-phone acoustic startle response monitoring system application to collect blink amplitude and velocity data on healthy males, aged 18–28 community cohorts during (n = 30) in both sitting and standing postures. The iPhone X application delivers specific sound parameters and detects blinking responses to acoustic stimulus (in millisecond resolution) to study the responses of the blinking reflex to acoustic sounds in standing and sitting positions by using multiple acoustic test sets of different frequencies and amplitudes introduced as acute sound stimuli (<0.5 s). The single acoustic battery of 15 pure-square wave sounds consisted of frequencies and amplitudes between 500, 1000, 2000, 3000, and 4000 Hz scales using 65, 90, and 105 dB (e.g., 3000 Hz_90 dB). (4) Results: Results show that there was a synchronization of amplitude and velocity between both eyes to all acoustic startles. Significant differences (p = 0.01) in blinking reaction time between sitting vs. standing at the high intensity (105 dB) 500 Hz acoustic test set was discovered. Interestingly, a highly significant difference (p < 0.001) in response times between test sets 500 Hz_105 dB and 4000 Hz_105 dB was identified. (5) Conclusions: To our knowledge, this is the first mobile phone-based acoustic battery used to detect and report significant ASR responses to specific frequencies and amplitudes of sound stimulus with corresponding sitting and standing conditions. The results from this experiment indicate the potential significance of using the specific frequency, amplitude, and postural conditions (as never before identified) which can open new horizons for ASR to be used for diagnosis and monitoring in numerous clinical and remote or isolated conditions.
kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation
Authors: De Martini Daniele , Gadd Matthew , Newman Paul
Published: October 2020

This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions.
Low-Cost Road-Surface Classification System Based on Self-Organizing Maps
Authors: Andrades Ignacio Sánchez , Castillo Aguilar Juan J. , García Juan M. Velasco , Carrillo Juan A. Cabrera , Lozano Miguel Sánchez
Published: October 2020

Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.
A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback
Authors: Lind Carl Mikael , Diaz-Olivares Jose Antonio , Lindecrantz Kaj , Eklund Jörgen
Published: October 2020

Work-related musculoskeletal disorders are a major concern globally affecting societies, companies, and individuals. To address this, a new sensor-based system is presented: the Smart Workwear System, aimed at facilitating preventive measures by supporting risk assessments, work design, and work technique training. The system has a module-based platform that enables flexibility of sensor-type utilization, depending on the specific application. A module of the Smart Workwear System that utilizes haptic feedback for work technique training is further presented and evaluated in simulated mail sorting on sixteen novice participants for its potential to reduce adverse arm movements and postures in repetitive manual handling. Upper-arm postures were recorded, using an inertial measurement unit (IMU), perceived pain/discomfort with the Borg CR10-scale, and user experience with a semi-structured interview. This study shows that the use of haptic feedback for work technique training has the potential to significantly reduce the time in adverse upper-arm postures after short periods of training. The haptic feedback was experienced positive and usable by the participants and was effective in supporting learning of how to improve postures and movements. It is concluded that this type of sensorized system, using haptic feedback training, is promising for the future, especially when organizations are introducing newly employed staff, when teaching ergonomics to employees in physically demanding jobs, and when performing ergonomics interventions.
Measuring the Uncertainty of Predictions in Deep Neural Networks with Variational Inference
Authors: Steinbrener Jan , Posch Konstantin , Pilz Jürgen
Published: October 2020

We present a novel approach for training deep neural networks in a Bayesian way. Compared to other Bayesian deep learning formulations, our approach allows for quantifying the uncertainty in model parameters while only adding very few additional parameters to be optimized. The proposed approach uses variational inference to approximate the intractable a posteriori distribution on basis of a normal prior. By representing the a posteriori uncertainty of the network parameters per network layer and depending on the estimated parameter expectation values, only very few additional parameters need to be optimized compared to a non-Bayesian network. We compare our approach to classical deep learning, Bernoulli dropout and Bayes by Backprop using the MNIST dataset. Compared to classical deep learning, the test error is reduced by 15%. We also show that the uncertainty information obtained can be used to calculate credible intervals for the network prediction and to optimize network architecture for the dataset at hand. To illustrate that our approach also scales to large networks and input vector sizes, we apply it to the GoogLeNet architecture on a custom dataset, achieving an average accuracy of 0.92. Using 95% credible intervals, all but one wrong classification result can be detected.
A New Quaternion-Based Kalman Filter for Human Body Motion Tracking Using the Second Estimator of the Optimal Quaternion Algorithm and the Joint Angle Constraint Method with Inertial and Magnetic Sensors
Authors: Duan Yingbo , Zhang Xiaoyue , Li Zhibing
Published: October 2020

Human body motion tracking is a key technique in robotics, virtual reality and other human–computer interaction fields. This paper proposes a novel simple-structure Kalman filter to improve the accuracy of human body motion tracking, named the Second EStimator of the Optimal Quaternion Kalman Filter (E2QKF). The new algorithm is the combination of the Second Estimator of the Optimal Quaternion (ESOQ-2) algorithm, the linear Kalman filter and the joint angle constraint method. In the proposed filter, the ESOQ-2 algorithm is used to produce an observation quaternion by preprocessing accelerometer and magnetometer measurements. The compensation for the accelerometer added in the ESOQ-2 algorithm is to eliminate the influence of human body motion acceleration included in the results. The state vector of the filter is the quaternion, which is calculated with gyroscope measurements, and the Kalman filter is to calculate the optimal quaternion by fusing the state quaternion and the observation quaternion. Therefore, the filter becomes a simple first-order linear system model, which avoids the linearization error of measurement equations and reduces the computational complexity. Furthermore, the joint angle constraint is considered in the proposed algorithm, which makes the results more accurate. To verify the accuracy of the proposed algorithm, inertial/magnetic sensors are used to perform the upper limb motion experiment, and the result of E2QKF (without joint angle constraint) is compared with an optical motion capture system and two traditional methods. Test results demonstrate the effectiveness of the proposed filter: the root mean square error (RMSE) of E2QKF is less than 2.0° and the maximum error is less than 4.6°. The result of E2QKF (with joint angle constraint) is compared with E2QKF (without joint angle constraint). Test results demonstrate the superiority of E2QKF (with joint angle constraint): the joint angle constraint method can further improve the accuracy of human body motion tracking.
Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks
Authors: Lozano Domínguez José Manuel , Al-Tam Faroq , Mateo Sanguino Tomás de J. , Correia Noélia
Published: October 2020

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.
Performance Evaluation of Real-Time Precise Point Positioning with Both BDS-3 and BDS-2 Observations
Authors: Pan Lin , Li Xuanping , Yu Wenkun , Dai Wujiao , Kuang Cuilin , Chen Jun , Chen Fade , Xia Pengfei
Published: October 2020

For time-critical precise applications, one popular technology is the real-time precise point positioning (PPP). In recent years, there has been a rapid development in the BeiDou Navigation Satellite System (BDS), and the constellation of global BDS (BDS-3) has been fully deployed. In addition to the regional BDS (BDS-2) constellation, the real-time stream CLK93 has started to support the BDS-3 constellation, indicating that the real-time PPP processing involving BDS-3 observations is feasible. In this study, the global positioning performance of real-time PPP with BDS-3/BDS-2 observations is initially evaluated using the datasets from 147 stations. In the east, north and upward directions, positioning accuracy of 1.8, 1.2 and 2.5 cm in the static mode, and of 6.7, 5.1 and 10.4 cm in the kinematic mode can be achieved for the BDS-3/BDS-2 real-time PPP, respectively, while the corresponding convergence time with a threshold of 10 cm is 32.9, 23.7 and 32.8 min, and 66.9, 42.9 and 69.1 min in the two modes in the three directions, respectively. To complete this, the availability of BDS-3/BDS-2 constellations, the quality of BDS-3/BDS-2 real-time precise satellite products, and the BDS-3/BDS-2 post-processed PPP solutions are also analyzed. For comparison, the results for the GPS are also presented.
A Case Study on the Development of a Data Privacy Management Solution Based on Patient Information
Authors: Verri Lucca Arielle , Augusto Silva Luís , Luchtenberg Rodrigo , Garcez Leonardo , Mao Xuzeng , García Ovejero Raúl , Miguel Pires Ivan , Luis Victória Barbosa Jorge , Reis Quietinho Leithardt Valderi
Published: October 2020

Data on diagnosis of infection in the general population are strategic for different applications in the public and private spheres. Among them, the data related to symptoms and people displacement stand out, mainly considering highly contagious diseases. This data is sensitive and requires data privacy initiatives to enable its large-scale use. The search for population-monitoring strategies aims at social tracking, supporting the surveillance of contagions to respond to the confrontation with Coronavirus 2 (COVID-19). There are several data privacy issues in environments where IoT devices are used for monitoring hospital processes. In this research, we compare works related to the subject of privacy in the health area. To this end, this research proposes a taxonomy to support the requirements necessary to control patient data privacy in a hospital environment. According to the tests and comparisons made between the variables compared, the application obtained results that contribute to the scenarios applied. In this sense, we modeled and implemented an application. By the end, a mobile application was developed to analyze the privacy and security constraints with COVID-19.
Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia
Authors: Ahamed Farhad , Shahrestani Seyed , Cheung Hon
Published: October 2020

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, and selecting distinctive features based on dynamic ranking, a classification model is built. We compare and contrast several machine learning approaches for developing a reliable and efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.
pH Colorimetric Sensor Arrays: Role of the Color Space Adopted for the Calculation of the Prediction Error
Authors: Pastore Andrea , Badocco Denis , Bogialli Sara , Cappellin Luca , Pastore Paolo
Published: October 2020

A pH colorimetric sensor array was prepared and characterized by combining tetrabromophenol blue (TBB) and bromothymol blue (BB) embedded in organically modified silicate (OrMoSil) spots polyvinylidene fluoride (PVDF)-supported. The signal was based on the Hue profile (H). The individual calibrations of TBB and BB showed precisions with minimum values of 0.012 pH units at pH = 2.196 for TBB and 0.018 at pH = 6.692 for BB. The overall precision of 10 spots of the mixture TBB/BB increased in the pH range of 1.000–8.000 from a minimum value of pH precision of 0.009 at pH = 2.196 to 0.012 at pH = 6.692, with the worst value of 0.279 pH units at pH = 4.101. The possibility to produce an array with much more than 10 spots allows for improving precision. The H analytical performance was compared to those of other color spaces such as RGB, Lab, and XYZ. H was the best one, with prediction error in the range of 0.016 to 0.021 pH units, at least three times lower than the second-best (x coordinate), with 0.064 pH units. These results were also confirmed by the calculation of the main experimental contributions to the pH prediction error, demonstrating the consistency of the proposed calculation approach.
Polymeric Waste from Recycling Refrigerators as an Aggregate for Self-Compacting Concrete
Authors: Silva Lucas Ramon Roque da , Silva Josimara Aparecida da , Francisco Matheus Brendon , Ribeiro Vander Alkmin , de Souza Michel Henry Bacelar , Capellato Patricia , Souza Marcelo Anderson , Claret dos Santos Valquíria , Cesar Gonçalves Paulo , de Lourdes Noronha Motta Melo Mirian
Published: October 2020

The inadequate disposal of household appliances by consumers and industries have annually been generating enormous amounts of polymeric waste (PW). So, the interest in reuse of PW in civil construction has increased. The production of new cementitious materials, such as concrete with PW, proves to be a promising solution to inappropriate disposal of this waste. In this study, self-compacting concrete (SCC) was developed with partial replacement of the coarse aggregates by polymeric waste (PW) from the recycling of refrigerators. In the SCC reference mixture, Portland cement, silica fume, sand, gravel and superplasticizer were used. The study also grouped the gravel as replaced by 5%, 10%, 15% and 20% of PW. In order to analyze the samples, the following tests were used: spreading, viscosity, passing ability, compressive strength, tensile strength, microstructure, modulus of elasticity, specific gravity, absorption, voids index and electrical resistivity. The SCC found showed adequate homogeneity and viscosity, staying within the normative parameters. The mechanical resistance was above 20 MPa; specific mass between 1870 to 2260 kg/m3; modulus of elasticity ranged from 34 to 14 GPa; and electrical resistivity between 319 to 420 ohm.m. Due to the mechanical resistance, the SCC with PW can be used for structural purposes and densely reinforced structures such as pillars, beams and foundation elements.
Geoadditive Quantile Regression Model for Sewer Pipes Deterioration Using Boosting Optimization Algorithm
Authors: Balekelayi Ngandu , Tesfamariam Solomon
Published: October 2020

Proactive management of wastewater pipes requires the development of deterioration models that support maintenance and inspection prioritization. The complexity and the lack of understanding of the deterioration process make this task difficult. A semiparametric Bayesian geoadditive quantile regression approach is applied to estimate the deterioration of wastewater pipe from a set of covariates that are allowed to affect linearly and nonlinearly the response variable. Categorical covariates only affect linearly the response variable. In addition, geospatial information embedding the unknown and unobserved influential covariates is introduced as a surrogate covariate that capture global autocorrelations and local heterogeneities. Boosting optimization algorithm is formulated for variable selection and parameter estimation in the model. Three geoadditive quantile regression models (5%, 50% and 95%) are developed to evaluate the band of uncertainty in the prediction of the pipes scores. The proposed model is applied to the wastewater system of the city of Calgary. The results show that an optimal selection of covariates coupled with appropriate representation of the dependence between the covariates and the response increases the accuracy in the estimation of the uncertainty band of the response variable. The proposed modeling approach is useful for the prioritization of inspections and provides knowledge for future installations. In addition, decision makers will be informed of the probability of occurrence of extreme deterioration events when the identified causal factors, in the 5% and 95% quantiles, are observed on the field.
Parameters Affecting Noise Emitted by Ships Moving in Port Areas
Authors: Nastasi Marco , Fredianelli Luca , Bernardini Marco , Teti Luca , Fidecaro Francesco , Licitra Gaetano
Published: October 2020

Only recently has noise been considered in the assessment of the sustainability of port infrastructures, after decades of unawareness. INTERREG Maritime projects unveiled problems that have been neglected so far, such as the lack of proper regulation and noise exposure assessments for citizens. While it is true that a port area includes a huge variety of possible noise sources, very few of them have been characterized from an acoustical point of view. INTERREG projects have boosted research in the field, and previous studies have dealt with noise produced by moving ships in ports. The present work starts from a previous measurement campaign used to obtain broadband and 1/3-octave-band noise emissions of moving ships, and it aims to explain their uncertainties. More than a month’s worth of continuous acoustic measurements and video recordings were deeply analyzed in order to obtain an input database that is as precise as possible. A multiple regression analysis was performed in order to understand the influence that parameters such as minimum distance, speed, and draught have on ships’ noise emissions, which were calculated using pass-by measurements, with a special focus on ferries. The minimum distance of each ship’s passage from the microphone was measured using a video recording with an innovative methodology, providing results with 3 m of uncertainty. Knowing which parameter is more influential would help in planning proper measurements for monitoring or for drafting correct guidelines. Draught was determined to be uninfluential in ferries’ noise emissions, while the minimum distance and speed relations were estimated and accounted for in the calculation of a refined sound power level. From a spectrum point of view, the frequencies from 500 Hz to 2.5 kHz were determined to be those that contributed the most to the noise produced by the transit of a ship, and they vary with speed. With the studied corrections, different ferry models resulted in similar noise emissions. The standard deviation of noise emitted was reduced by 0.5 dB (A), and the average was also improved by positioning the ships’ flow at the correct average minimum distance. Furthermore, the right placement of a source is also important in the acoustic mapping phase for a correct evaluation of the propagation of noise at a distance. The use of more precise input data is important for improving the output of acoustic propagation models during the assessment of port noise in the surrounding areas.
Interactions among Environmental Training, Environmental Strategic Planning and Personnel Controls in Radical Environmental Innovation
Authors: Lunkes Rogério João , Rosa Fabricia Silva da , Monteiro Januário José , Bortoluzzi Daiane Antonini
Published: October 2020

The purpose of this study was to analyze the effect of environmental training on the relationship between environmental strategic planning and personnel controls in radical environmental innovation. To collect the data, we designed a questionnaire for companies in the Brazil Stock Exchange (B3-Brazil, Bolsa, and Balcão). Data from 150 companies were analyzed using structural equation modeling and fuzzy-set qualitative comparative analysis (FsQCA). The results show that environmental strategic planning and personnel controls affect radical environmental innovation. The findings also show that environmental training impacts the relationship between environmental strategic planning and personnel controls in radical environmental innovation. The asymmetric approach reinforces this evidence and suggests that environmental training is a central element that leads to high radical environmental innovation. The study contributes to the literature by showing that environmental training assists in the management of radical environmental innovation by aligning individual and organizational objectives.
Assessing Sustainable Foreign Direct Investment Performance in Malaysia: A Comparison on Policy Makers and Investor Perceptions
Authors: Ling Pick-Soon , Lim Ming K. , Tseng Ming-Lang
Published: October 2020

Sustainable foreign direct investment (SFDI) contributes to the development of the economic, environmental, and social aspects in rational governance practices in Malaysia. Prior studies lack the integration and synthesis of the SFDI attributes from the policymakers and foreign investors’ perceptions. These attributes are measured through the qualitative information and subjective perceptions and need to transform into comparable values. The fuzzy Delphi method is applied to identify the valid set of SFDI attributes and confirms the validity and reliability of these attributes. Moreover, prior studies have not examined the importance and performance of those valid attributes in qualitative information. The fuzzy importance and performance analysis is proposed to assess the attributes’ importance and performance level. The results show that financial, macroeconomic, and institutional policy aspects are among the most important SFDI attributes, together with environmental and social aspects. This study identifies the discrepancies between policymakers and foreign investors and suggests that the financial aspect is the priority of foreign investors that needs to be concentrated for improvements; meanwhile, the institutional and policies and social aspects in performance level are presented as a big contradistinction. The theoretical and policy implications are discussed.
Role of Foreign and Domestic Institutional Investors in Corporate Sustainability: Focusing on R&D Investment
Authors: Shin Ilhang , Park Sorah
Published: October 2020

This paper examines the effects of ownership by foreign and domestic institutional investors on corporate sustainability by focusing on the level of research and development (R&D) investment. Long-term investment in R&D is crucial for companies that seek to generate sustainable growth. Ordinary least-squares regression is performed on a sample of Korean listed companies. The main test with both foreign and domestic institutional ownership is based on a study period from 2001 to 2004. The results indicate that firms with higher levels of foreign institutional ownership exhibit greater levels of corporate R&D activities, while the ownership by domestic institutions has no significant influence on firms’ R&D investment. An additional test with foreign institutional ownership data is based on an extended study period from 2001 to 2014, and shows that foreign institutional ownership is positively related to firms’ R&D investment. This result survives the two-stage instrumental variable approach used to address endogeneity factors in foreign institutional ownership. Taken together, these findings suggest that foreign institutions can effectively monitor managerial myopia and promote corporate innovations.
Microalgae from Biorefinery as Potential Protein Source for Siberian Sturgeon (A. baerii) Aquafeed
Authors: Bongiorno Tiziana , Foglio Luciano , Proietti Lorenzo , Vasconi Mauro , Lopez Annalaura , Pizzera Andrea , Carminati Domenico , Tava Aldo , Vizcaíno Antonio Jesús , Alarcón Francisco Javier , Ficara Elena , Parati Katia
Published: October 2020

The demand for aquafeed is expected to increase in the coming years and new ingredients will be needed to compensate for the low fish meal and oil availability. Microalgae represent a promising matrix for the future aquafeed formulation, however, the high production cost hinders its application. The use of microalgae from biorefinery would reduce the disposal costs for microalgae production. The present study aimed to (i) verify the growth of microalgae on digestate coming from pig farming and (ii) evaluate their potential valorization as dietary ingredient in aquafeed according to a Circular Bioeconomy approach. For these purposes, a microalgae biomass was produced on an outdoor raceway reactor supplied with digestate and used for partial replacement (10% of the diet) in aquafeed for Siberian sturgeon fingerlings (Acipenser baerii). The results obtained confirm the feasibility for growing microalgae on digestate with satisfactory productivity (6.2 gDM m−2 d−1), nutrient removal efficiency and Chemical Oxygen Demand reduction; moreover, the feeding trial carried out showed similar results between experimental and control groups (p > 0.05), in term of growth performance, somatic indices, fillet nutritional composition and intestinal functionality, to indicate that microalgae from biorefinery could be used as protein source in Siberian sturgeon aquafeed.
Validity of the Portable Ultrasound BodyMetrix™ BX-2000 for Measuring Body Fat Percentage
Authors: Kang Seoungki , Park Jeong-Hui , Seo Myong-Won , Jung Hyun Chul , Kim Yong Ik , Lee Jung-Min
Published: October 2020

BodyMetrix™ BX-2000 (IntelaMetrix, Livermore, CA, USA) has been introduced as one of the alternatives and portable methods to estimate body fat percentage. However, inconsistent results between protocols built-in the BodymetrixTM may be compelling the question of its validity. Thus, this study first investigated the possible errors between protocols and evaluated the validity of body fat percentage (BF%) compared to the gold standard method (dual-energy X-ray absorptiometry, DEXA). One hundred and five collegiate males, aged 20.01 ± 2.11 years, body height, 174.81 ± 6.01 cm, body mass, 73.26 ± 13.60 kg, and body mass index, 23.91 ± 3.77 kg·m−2 participated in the present study. Participants’ body fat percentage was estimated by built-in nine different protocols in the BodyMetrix™ BX-2000 using A-MODE ultrasound. Pearson correlation (r), Mean absolute percentage errors (MAPEs), Bland & Altman plots, and Equivalence testing were used to examine the validity of each protocol by comparing it to the criterion measure (i.e., DEXA). The results indicated good potential for almost all of the protocols in correlation (Min: r = 0.79, Max: r = 0.92)., MAPEs (Min: 20.0%, Max: 33.8%), and Bland-Altman (Min diff: 16.7, Max diff: 41.4). Particularly, the estimated BF% from protocol 7 (4-sites by Durnin & Wormersley) and protocol 9 (9-sites Parllo) were completed within the equivalence zone (±10% of the mean). The estimates measured by protocol 7 and protocol 9 identified as the most valid methods for estimating BF% using a BodyMetrix™ BX-2000, compared to the DEXA. Our findings provide valuable information when applying in young male individuals, but future studies with other populations such as female or adolescents may be required to suggest a valid protocol within the instrument.
Aquatic Macroinvertebrate Indicators in the Zawgyi Irrigation Channels and a River in the Central Dry Zone of Myanmar
Authors: Ko Nyein Thandar , Suter Phil , Conallin John , Rutten Martine , Bogaard Thom
Published: October 2020

Rivers and wetlands in Myanmar provide essential services to people in terms of transportation, agriculture, fisheries and a myriad of other ecosystem services, all of which are dependent on a healthy ecosystem. Irrigation channels are also an important part of the infrastructure for daily water use in Myanmar. The objective of this research is to describe the aquatic ecosystem of irrigation channels using aquatic macroinvertebrate communities. The research focused on the taxonomic composition of the aquatic macroinvertebrates of the Zawgyi River and the associated irrigation channels in central Myanmar, east of the city of Mandalay. Significant differences between the river and channels, and among individual channels, were shown using an analysis of similarity: Bray–Curtis similarity, a multivariate equivalent of the univariate statistical method of analysis of variance: ANOSIM and an analysis of similarity percentages: SIMPER by Plymouth Routines in Multivariate Ecological Research: PRIMER v6 software. The initial findings suggest that there is a clear separation between macroinvertebrate communities at the morpho-species level of identification between river and irrigation channels, while there is less separation between functional feeding groups (FFG) between them. The lower taxonomic level of discrimination at the family level using a water quality index showed no significant difference between river and channels. The preliminary field results indicate that a recently modified biomonitoring index method could be applied in Myanmar to assess the ecological water quality of the modified river, as well as human-made channels.
The Market Price Premium for Buildings Seismic Retrofitting
Authors: D’Alpaos Chiara , Bragolusi Paolo
Published: October 2020

The Italian territory is largely prone to seismic risk and 6 million buildings require seismic retrofitting. In the last three main seismic events (Abruzzo 2009, Emilia Romagna in 2012 and in Lazio in 2016) 633 people died and considerable financial losses such as the structural collapse of buildings and interruption of production activities were incurred. During the period 1944–2017, economic losses caused by seismic events amounted to EUR 212 billion. More than 80% of the entire building stock does not respect seismic design standards provided by Italian regulations (NTC 2018). Seismic retrofitting of buildings may avoid many deaths and financial losses, as well as increase people’s safety. In addition, seismic retrofitting of buildings may also generate an increase in real estate asset value (namely a market price premium), which may accelerate investments. Despite the relevance of this issue, there is a lack of literature, which investigates the key factors in boosting investments and the market price premium for retrofitted buildings in detail. The aim of this paper is to fill this gap with respect to the Italian Real Estate market. To estimate the capitalization effect of benefits produced by seismic retrofitting on property market values, it is fundamental to know how much people are willing to pay for it. As, to our knowledge, there are no available datasets which provide house characteristics, including seismic performances and market prices of Italian real estate assets, we implemented a contingent valuation approach to determine the market price premium for retrofitted assets. In detail, information about the willingness to pay (WTP) an additional price for a seismically retrofitted home (by considering different risk exposure), ceteris paribus was elicited using open-ended questions in a self-administered web interview. In particular, we applied the methodology to a case study, i.e., a contingent scenario related to masonry-detached houses located in a seismic hazard zone. Our results revealed that individuals are willing to pay an additional price for retrofitted assets and the average market price premium ranges from 10% to 52% of the property market price.
Who is Teleworking and Where from? Exploring the Main Determinants of Telework in Europe
Authors: López-Igual Purificación , Rodríguez-Modroño Paula
Published: October 2020

Telework and ICT-based mobile work (TICTM) arrangements have emerged in response to technological changes driven by digitalisation, increasing flexibility within the labour market, and globalisation. As telework becomes more widespread, these flexible models of work are rapidly expanding to new categories of employees, changing the factors traditionally found to be important for telework eligibility. The aim of this study is to gain a deeper understanding of new profiles of teleworkers, examining main factors that increase or decrease the likelihood of different TICTM arrangements. We apply multinomial logistic regression models to a sample of more than 20,000 workers from the 6th European Working Conditions Survey. Our findings confirm the heterogeneity in the profiles of teleworkers, particularly distinguishing by TICTM arrangement. Occasional teleworkers are usually male managers or professionals, but a relevant percentage of highly mobile teleworkers are technicians and associate professionals, while clerical support workers amount to a large group of home-based teleworkers. The majority of occasional and highly mobile teleworkers are still men, but this can no longer be said of home-based teleworkers. The correlations between telework and permanent contracts, full-time jobs, and living in urban areas are weak, showing that TICTM is spreading into more precarious, temporary, and lower-paid jobs, especially among home-based teleworkers and highly mobile teleworkers.
Wireless Charging of Electric Taxis: Understanding the Facilitators and Barriers to Its Introduction
Authors: Oliveira Luis , Ulahannan Arun , Knight Matthew , Birrell Stewart
Published: October 2020

The shift to electric vehicles has brought about the potential to reduce the environmental damage caused by road transport. However, several challenges prevent wider adoption of electric vehicles, such as: a lack of charging facilities, long charging times, limited range, and the inconvenience of cable charging. These barriers are more pronounced for taxis, which generally cover longer distances than regular cars and have fewer opportunities for recharging. This research aims to evaluate wireless charging for range extended electric taxis, as a strategy to minimise these challenges and facilitate the electrification of fleets. A mixed methods approach, combining quantitative vehicle tracking with qualitative interviews and focus groups with drivers and local authority representatives, provided an understanding of ‘facilitators’ and ‘barriers’ to the introduction of wireless chargers in London and Nottingham, UK. Results indicated that current wired charging infrastructure does not facilitate recharging opportunities during taxi working hours, causing longer shifts or lower earnings. Drivers reported running on a range extender petrol engine once the battery is depleted, limiting the environmental benefits of electric taxis. We conclude that wireless chargers could facilitate the increased driving range of existing electric taxis if installed where drivers stop more often. The results support the implementation of opportunistic, short but frequent charging boosts (known as choko-choko) as part of policies to alleviate the barriers to the introduction of wireless charging of electric taxis, and foster more sustainable means of road transportation.