bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensor Based on Molecularly Imprinted Polymer Membranes and Smartphone for Detection of Fusarium Contamination in Cereals
Authors: Sergeyeva Tetyana , Yarynka Daria , Dubey Larysa , Dubey Igor , Piletska Elena , Linnik Rostyslav , Antonyuk Maksym , Ternovska Tamara , Brovko Oleksandr , Piletsky Sergey , El’skaya Anna
Published: August 2020

The combination of the generic mobile technology and inherent stability, versatility and cost-effectiveness of the synthetic receptors allows producing optical sensors for potentially any analyte of interest, and, therefore, to qualify as a platform technology for a fast routine analysis of a large number of contaminated samples. To support this statement, we present here a novel miniature sensor based on a combination of molecularly imprinted polymer (MIP) membranes and a smartphone, which could be used for the point-of-care detection of an important food contaminant, oestrogen-like toxin zearalenone associated with Fusarium contamination of cereals. The detection is based on registration of natural fluorescence of zearalenone using a digital smartphone camera after it binds to the sensor recognition element. The recorded image is further processed using a mobile application. It shows here a first example of the zearalenone-specific MIP membranes synthesised in situ using “dummy template”-based approach with cyclododecyl 2, 4-dihydroxybenzoate as the template and 1-allylpiperazine as a functional monomer. The novel smartphone sensor system based on optimized MIP membranes provides zearalenone detection in cereal samples within the range of 1–10 µg mL−1 demonstrating a detection limit of 1 µg mL−1 in a direct sensing mode. In order to reach the level of sensitivity required for practical application, a competitive sensing mode is also developed. It is based on application of a highly-fluorescent structural analogue of zearalenone (2-[(pyrene-l-carbonyl) amino]ethyl 2,4-dihydroxybenzoate) which is capable to compete with the target mycotoxin for the binding to zearalenone-selective sites in the membrane’s structure. The competitive mode increases 100 times the sensor’s sensitivity and allows detecting zearalenone at 10 ng mL−1. The linear dynamic range in this case comprised 10–100 ng mL−1. The sensor system is tested and found effective for zearalenone detection in maize, wheat and rye flour samples both spiked and naturally contaminated. The developed MIP membrane-based smartphone sensor system is an example of a novel, inexpensive tool for food quality analysis, which is portable and can be used for the “field” measurements and easily translated into the practice.
Gaussia Luciferase as a Reporter for Quorum Sensing in Staphylococcus aureus
Authors: Blower Isobel , Tong Carmen , Sun Xiaohui , Murray Ewan , Luckett Jeni , Chan Weng , Williams Paul , Hill Philip
Published: August 2020

Gaussia luciferase (GLuc) is a secreted protein with significant potential for use as a reporter of gene expression in bacterial pathogenicity studies. To date there are relatively few examples of its use in bacteriology. In this study we show that GLuc can be functionally expressed in the human pathogen Staphylococcus aureus and furthermore show that it can be used as a biosensor for the agr quorum sensing (QS) system which employs autoinducing peptides to control virulence. GLuc was linked to the P3 promoter of the S. aureusagr operon. Biosensor strains were validated by evaluation of chemical agent-mediated activation and inhibition of agr. Use of GLuc enabled quantitative assessment of agr activity. This demonstrates the utility of Gaussia luciferase for in vitro monitoring of agr activation and inhibition.
Weather Classification Using an Automotive LIDAR Sensor Based on Detections on Asphalt and Atmosphere
Authors: Vargas Rivero Jose Roberto , Gerbich Thiemo , Teiluf Valentina , Buschardt Boris , Chen Jia
Published: August 2020

A semi-/autonomous driving car requires local weather information to identify if it is working inside its operational design domain and adapt itself accordingly. This information can be extracted from changes in the detections of a light detection and ranging (LIDAR) sensor. These changes are caused by modifications in the volumetric scattering of the atmosphere or surface reflection of objects in the field of view of the LIDAR. In order to evaluate the use of an automotive LIDAR as a weather sensor, a LIDAR is placed outdoor in a fixed position for a period of 9 months covering all seasons. As target, an asphalt region from a parking lot is chosen. The collected sensor raw data is labeled depending on the occurring weather conditions as: clear, rain, fog and snow, and the presence of sunlight: with or without background radiation. The influence of different weather types and background radiations on the measurement results is analyzed and different parameters are chosen in order to maximize the classification accuracy. The classification is done per frame in order to provide fast update rates while still keeping an F1 score higher than 80%. Additionally, the field of view is divided into two regions: atmosphere and street, where the influences of different weather types are most notable. The resulting classifiers can be used separately or together increasing the versatility of the system. A possible way of extending the method for a moving platform and alternatives to virtually simulate the scene are also discussed.
High-Efficiency Inscription of Fiber Bragg Grating Array with High-Energy Nanosecond-Pulsed Laser Talbot Interferometer
Authors: Zhang Zhe , Xu Baijie , He Jun , Hou Maoxiang , Bao Weijia , Wang Yiping
Published: August 2020

A high-energy nanosecond-pulsed ultraviolet (UV) laser Talbot interferometer for high-efficiency, mass production of fiber Bragg grating (FBG) array was experimentally demonstrated. High-quality FBG arrays were successfully inscribed in both H2-free and H2-loaded standard single-mode fibers (SMFs) with high inscription efficiency and excellent reproducibility. Compared with the femtosecond pulse that had a coherent length of several tens of micrometers, a longer coherent length (~10 mm) of the employed laser rendered a wider FBG wavelength versatility over 700 nm band (1200–1900 nm) without the need for optical path difference (OPD) compensation. Dense FBG array with center wavelength separation of ~0.4 nm was achieved and more than 1750 FBGs with separated center wavelength could be inscribed in a single H2-free or H2-loaded SMF in theory, which is promising for mass production of FBG arrays in industry. Moreover, precise focusing of laser beam was superfluous for the proposed system due to the high energy density of pulse. The proposed FBG inscription system was promising for industrialization production of dense FBG arrays.
Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications
Authors: Moens Pieter , Bracke Vincent , Soete Colin , Vanden Hautte Sander , Nieves Avendano Diego , Ooijevaar Ted , Devos Steven , Volckaert Bruno , Van Hoecke Sofie
Published: August 2020

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.
A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
Authors: Ali Tariq , Irfan Muhammad , Shaf Ahmad , Saeed Alwadie Abdullah , Sajid Ahthasham , Awais Muhammad , Aamir Muhammad
Published: August 2020

Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic medium is used in underwater communication and radio frequency is mostly used for wireless sensor networks to make communication more reliable. Therefore, some challenging tasks still exist in I-UWSN, i.e., selection of multiple nodes’ reliable paths towards the sink nodes; and efficient topology of the network. In this research, the novel routing protocol, namely Time Based Reliable Link (TBRL), for dynamic topology is proposed to support smart city. TBRL works in three phases. In the first phase, it discovers the topology of each node in network area using a topology discovery algorithm. In the second phase, the reliability of each established link has been determined while using two nodes reliable model for a smart environment. This reliability model reduces the chances of horizontal and higher depth level communication between nodes and selects next reliable forwarders. In the third phase, all paths are examined and the most reliable path is selected to send data packets. TBRL is simulated with the help of a network simulator tool (NS-2 AquaSim). The TBRL is compared with other well known routing protocols, i.e., Depth Based Routing (DBR) and Reliable Energy-efficient Routing Protocol (R-ERP2R), to check the performance in terms of end to end delay, packet delivery ratio, and energy consumption of a network. Furthermore, the reliability of TBRL is compared with 2H-ACK and 3H-RM. The simulation results proved that TBRL performs approximately 15% better as compared to DBR and 10% better as compared to R-ERP2R in terms of aforementioned performance metrics.
Characterization of the Noise Induced by Stimulated Brillouin Scattering in Distributed Sensing
Authors: Kadum Jaffar Emad , Feng Cheng , Schneider Thomas
Published: August 2020

The excess noise due to stimulated Brillouin scattering (SBS) in gain and loss-based Brillouin optical time-domain analyzers (BOTDA) has been investigated theoretically and experimentally for the first time to the best of our knowledge. This investigation provides a full insight to the SBS-induced noise distribution, which mainly comes from phase-to-intensity conversion noise and the beating noise between the probe wave and spontaneous Brillouin scattering. A complete theoretical model, which is in good agreement with the experimental results, is presented to describe the noise. The results show that a loss-based BOTDA setup gives a better noise performance than a gain-based one in both time and frequency domain. The SBS-induced noise has been characterized in dependence on the pump and probe power and the spatial resolution.
Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification
Authors: Wilby Mark Richard , Vinagre Díaz Juan José , Fernández Pozo Rubén , Rodríguez González Ana Belén , Vassallo José Manuel , Sánchez Ávila Carmen
Published: August 2020

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.
Smart Socks and In-Shoe Systems: State-of-the-Art for Two Popular Technologies for Foot Motion Analysis, Sports, and Medical Applications
Authors: Drăgulinescu Andrei , Drăgulinescu Ana-Maria , Zincă Gabriela , Bucur Doina , Feieș Valentin , Neagu Dumitru-Marius
Published: August 2020

The present paper reviews, for the first time, to the best of our knowledge, the most recent advances in research concerning two popular devices used for foot motion analysis and health monitoring: smart socks and in-shoe systems. The first one is representative of textile-based systems, whereas the second one is one of the most used pressure sensitive insole (PSI) systems that is used as an alternative to smart socks. The proposed methods are reviewed for smart sock use in special medical applications, for gait and foot pressure analysis. The Pedar system is also shown, together with studies of validation and repeatability for Pedar and other in-shoe systems. Then, the applications of Pedar are presented, mainly in medicine and sports. Our purpose was to offer the researchers in this field a useful means to overview and select relevant information. Moreover, our review can be a starting point for new, relevant research towards improving the design and functionality of the systems, as well as extending the research towards other areas of applications using sensors in smart textiles and in-shoe systems.
Smartphone App with an Accelerometer Enhances Patients’ Physical Activity Following Elective Orthopedic Surgery: A Pilot Study
Authors: van Dijk-Huisman Hanneke C. , Weemaes Anouk T.R. , Boymans Tim A.E.J. , Lenssen Antoine F. , de Bie Rob A.
Published: August 2020

Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55–51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14–8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients’ PA levels and functional recovery during hospitalization.
Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging
Authors: Medeiros André Dantas de , Silva Laércio Junio da , Ribeiro João Paulo Oliveira , Ferreira Kamylla Calzolari , Rosas Jorge Tadeu Fim , Santos Abraão Almeida , Silva Clíssia Barboza da
Published: August 2020

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.
A Waveform Image Method for Discriminating Micro-Seismic Events and Blasts in Underground Mines
Authors: Wei Hui , Shu Weiwei , Dong Longjun , Huang Zhongying , Sun Daoyuan
Published: August 2020

The discrimination of micro-seismic events (events) and blasts is significant for monitoring and analyzing micro-seismicity in underground mines. To eliminate the negative effects of conventional discrimination methods, a waveform image discriminant method was proposed. Principal component analysis (PCA) was applied to extract the raw features of events and blasts through their waveform images that established by the recorded field data, and transform them into the new uncorrelated features. The amount of initial information retained in the derived features could be determined quantitatively by the contribution rate. The binary classification models were established by utilizing the support vector machine (SVM) algorithm and the PCA derived waveform image features. Results of four groups of cross validation show that the optimal values for the accuracy of events and blasts, total accuracy, and quality evaluation parameter MCC are 97.1%, 93.8%, 93.60%, and 0.8723, respectively. Moreover, the computation efficiency per accuracy (CEA) was introduced to quantitatively evaluate the effects of contribution rate on classification accuracy and computation efficiency. The optimal contribution rate was determined to be 0.90. The waveform image discriminant method can automatically classify events and blasts in underground mines, ensuring the efficient establishment of high-quality micro-seismic databases and providing adequate data for the subsequent seismicity analysis.
A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals
Authors: Li Xilin , Ling Sai Ho , Su Steven
Published: August 2020

People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.
Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs
Authors: Bassolillo Salvatore Rosario , D’Amato Egidio , Notaro Immacolata , Blasi Luciano , Mattei Massimiliano
Published: August 2020

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.
Real-Time Detection of Railway Track Component via One-Stage Deep Learning Networks
Authors: Wang Tiange , Yang Fangfang , Tsui Kwok-Leung
Published: August 2020

Railway inspection has always been a critical task to guarantee the safety of the railway transportation. The development of deep learning technologies brings new breakthroughs in the accuracy and speed of image-based railway inspection application. In this work, a series of one-stage deep learning approaches, which are fast and accurate at the same time, are proposed to inspect the key components of railway track including rail, bolt, and clip. The inspection results show that the enhanced model, the second version of you only look once (YOLOv2), presents the best component detection performance with 93% mean average precision (mAP) at 35 image per second (IPS), whereas the feature pyramid network (FPN) based model provides a smaller mAP and much longer inference time. Besides, the detection performances of more deep learning approaches are evaluated under varying input sizes, where larger input size usually improves the detection accuracy but results in a longer inference time. Overall, the YOLO series models could achieve faster speed under the same detection accuracy.
Isolation, Characterization, and HPTLC-Quantification of Compounds with Anticancer Potential from Loranthus Acaciae Zucc.
Authors: Noman Omar M. , Nasr Fahd A. , Mothana Ramzi A. , Alqahtani Ali S. , Qamar Wajhul , Al-Mishari Abdullah A. , Al-Rehaily Adnan J. , Siddiqui Nasir A. , Alam Perwez , Almarfadi Omer M.
Published: August 2020

The cytotoxic effects of the crude extract of Loranthus acaciae Zucc. and its n-hexane, chloroform, and n-butanol fractions were assessed against three cancer cell lines using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. Cell apoptosis was determined using an annexin V-phycoerythrin/7-aminoactinomycin kit. We observed that the L. acaciae n-hexane extract (LAHE) could inhibit cancer cell growth, particularly of MCF7 and A549 cells. Chromatographic purification of LAHE and nuclear magnetic resonance analysis led to the identification of two compounds from this plant species, namely, betulinic acid and β-sitosterol, for the first time. Flow cytometry study suggested that betulinic acid induced cell death via apoptosis, as a distinguished marked enhancement in the early and late apoptosis of human lung (A549) and breast (MCF-7) cancer cell lines. The isolated compounds were further estimated concurrently in LAHE using a validated high-performance thin-layer chromatographic (HPTLC) method on a 10 × 10 cm2 HPTLC plate with chloroform, methanol, and glacial acetic acid (97:2:1, v/v/v) as the mobile phase and a λmax of 540 nm. The amounts of betulinic acid and β-sitosterol in LAHE were 69.46 and 135.53 µg/mg of dried weight of extract, respectively. The excellent cytotoxic effect of LAHE could be attributed to the presence of ample amounts of betulinic acid.
Delay Factors Management and Ranking for Reconstruction and Rehabilitation Projects Based on the Relative Importance Index (RII)
Authors: Khatib Basem Al , Poh Yap Soon , El-Shafie Ahmed
Published: August 2020

In recent decades, the construction industry has developed rapidly. Delay was and remains one of the most important challenges negatively affecting not only this industry but also the economy and sustainable development. This study aims to conduct a comprehensive analysis of the delay factors in reconstruction projects because of the vital importance of these types of projects. This is the first attempt to conduct such an analysis for reconstruction projects in particular, and it is a continuation of the previous case study, which was considered one of the most important reconstruction projects in the Middle East region. The previous study focused on determining the types of delay factors used in this project. It is necessary to carry out advance in-depth analysis to sort the delay factors based on their effect on the project plan. In order to achieve the study goal, 93 questionnaires were distributed by hand to experts working on the project: some of them working the day shift, and the others working the night shift. The questionnaire results were analyzed and delay factors were sorted in descending order according to the relative importance index (RII). The three main factors affecting the overall reconstruction project duration were site restrictions and conditions, electrical and mechanical rerouting works, and design buildability and adjustment.
Sustainable Development and Ecological Deficit in the United Arab Emirates
Authors: Modarress Batoul , Ansari Alexander , Ansari Al
Published: August 2020

The economy of the United Arab Emirates has ranked 27th in the world for decades, which has supported its human development being rated highest among Arab nations. The country, however, has remained oblivious to its ecological deficit. This study explores sources of this deficit by analyzing three sets of data related to its economic growth, human development, and ecological deficit for 29 years from 1990 to 2019. The results of the data analyses indicate that although an increase in the country’s GDP has a high positive correlation with the nation’s human development, the indicator variables related to both measures have a significant reverse influence on the variability of the values for the 333country’s ecological deficit. Validating the statistical results through interviews with ten authorities from various government ministries and the oil industry shows that, when considering the nation’s finite biocapacity, the genesis of its ecological deficit lies in persistent developments that rely on petroleum revenues and the rapid influx of the millions of migrants who are needed to close the skill gaps of United Arab Emirates (UAE) citizens. Although initiatives to reduce the UAE’s ecological footprint have been in place since 2007, the lack of environmental action plans, policies, and enforcing regulations have resulted in the nation’s failure to move toward achieving sustainable development. This has pushed the country toward the brink of ecological bankruptcy.
Effects of Short-Term Aging on the Compactibility and Volumetric Properties of Asphalt Mixtures Using the Response Surface Method
Authors: Omranian Seyed Reza , Hamzah Meor Othman , Pipintakos Georgios , Van den bergh Wim , Vuye Cedric , Hasan Mohd Rosli Mohd
Published: August 2020

Several factors affect asphalt binder and mixture characteristics. This makes pavement performance assessment a mounting task. This paper evaluates the effects of short-term aging on compactibility and volumetric properties of asphalt mixtures using the Response Surface Method (RSM). Three different binders were utilized to produce mixtures (type AC-14). Aging temperature, aging duration, and duration in a climate chamber with increased humidity and ultraviolet lighting were considered as independent variables (IV), while compactibility and volumetric properties were regarded as dependent variables (DV). The findings revealed significant impacts of aging temperature and duration on compactibility, air voids, voids in mineral aggregate, and voids filled with asphalt, while duration in the climate chamber exhibited no significant influence on the DVs. The effects of IVs on DVs varied by binder type. This was achieved through an elaborate statistical analysis. The study, finally, demonstrates the RSM’s potential to predict changes in responses from mathematical equations—converging with the experimental observation—with excellent accuracy. Potentially, pavement contractors can use this method by replacing haulage duration and mixtures’ temperatures during paving in the developed models. It enables them to predict the pavement density and adjust pressure as well as the number of roller passes to achieve the desired requirements.
Target-Oriented Promotion of the Intention for Sustainable Behavior with Social Norms
Authors: Silberer Jan , Müller Patrick , Bäumer Thomas , Huber Stephanie
Published: August 2020

To date, measures to promote sustainable behavior by influencing attitudes have often not had the expected success. Past research indicates that using social norms to promote sustainable behavior in people with low involvement in environmental protection offers a promising basis to address this problem. In this study, this insight was transferred to the context of two environmental measures at an early stage of development by investigating the effects of social norms on the intention for sustainable behavior in people with a low intention to be involved in environmental protection. Study 1 was conducted in the context of a university project on energy-saving behavior with 606 university members, and Study 2 was carried out in the context of a project on the acceptance of an innovative, sustainable technology with 101 consumers. In both studies, social norms had stronger effects on people with a low intention to be involved in environmental protection. Therefore, using social norms to promote the intention for sustainable behavior among people with a low intention to be involved offers a promising basis for further research and could serve as an approach for designing target-group-specific strategies at early stages in the development of sustainability measures.
Do Countries with Similar Levels of Corruption Compete to Attract Foreign Investment? Evidence Using World Panel Data
Authors: Alamá-Sabater Luisa , Fernández-Núñez Teresa , Márquez Miguel Ángel , Salinas-Jimenez Javier
Published: August 2020

This paper examines whether foreign direct investment in one country helps to increase foreign investment in other countries with a similar degree of corruption. Our estimates are based on an unbalanced annual panel of 164 countries over the 2005–2015 period. Using spatial econometric techniques, our main findings reveal that foreign investment in one recipient country is complementary to that in countries with similar levels of corruption. Furthermore, our results point to the existence of different circuits of foreign direct capital among countries that are determined by corruption similarity. These results suggest important policy implications for countries aiming to attract foreign investment.
Experimental Evaluation of Information Interventions to Encourage Non-Motorized Travel: A Case Study in Hefei, China
Authors: Geng Jichao , Long Ruyin , Yang Li , Zhu Junqi , Engeda Birhane Getnet
Published: August 2020

This study aims at presenting an experimental evaluation of the different effects of environmental and health information on encouraging car owners to travel on foot and by bicycle. Health information consists of a high and a low target setting. One hundred and forty-six participants in Hefei city reported their travel behaviors in terms of mode, time, and trip before and after the experiment. Their cognitive and emotional processes with regard to the protection motivation theory (PMT) that determine their potential travel behavior changes in response to information intervention are also identified. Three experimental groups and one control group based on a between-group design are adopted and the methodology of paired sample chi-squared tests and stepwise linear regressions are used. The results show that environmental information alone fails to encourage car owners’ non-motorized travel. When health information is added, information intervention can effectively encourage a time increase in walking and cycling as well as a time and trip decrease in car use in the short term. But the long-term effect is not significant after a year and a half. Moreover, there are no significant differences between the high and the low target settings in health information for encouraging non-motorized travel. In terms of PMT constructs, severity has a significant relationship with the change of time or trip on foot and by bicycle. Vulnerability emerges as a non-effective predictor. Reward, self-efficacy, response efficacy, and response cost are more remarkable in predicting the change of time or trip by car. This study recommends that (1) health information with a target setting is superior to environmental information, (2) reduction strategy is potentially superior to transfer strategy to control car usage, (3) policymakers should design intervention strategies relevant to the coping appraisal rather than to the threat appraisal.
Energy Saving: Views and Attitudes among Primary School Students and Their Parents
Authors: Zerinou Ioanna , Karasmanaki Evangelia , Ioannou Konstantinos , Andrea Veronika , Tsantopoulos Georgios
Published: August 2020

The ongoing economic development and prosperity have led not only to the exhaustion of the planet’s natural resources but also to numerous environmental problems. To address these problems, it is necessary that more individuals adopt energy saving practices while promoting alternative energy sources. In other words, future generations ought to adopt behaviors that will involve the sustainable management of energy. To that end, sustainable education can create citizens who not only have knowledge and awareness about the environment and the severe environmental issues but are also willing to take action to address these issues. Hence, the aim of the present study is to investigate the environmental attitudes and behaviors among primary school students and their parents in the municipality of Orestiada, in Greece. To achieve this objective, a structured questionnaire with closed-ended questions was used. Results showed that both students and parents had adequate levels of environmental awareness while their beliefs were translated into actual behaviors as they had adopted, to a significant extent, energy saving habits and practices.
Field Study on the Efficiency of a Methane Degradation Layer Composed of Fine Fraction Soil from Landfill Mining
Authors: Pehme Kaur-Mikk , Orupõld Kaja , Kuusemets Valdo , Tamm Ottar , Jani Yahya , Tamm Toomas , Kriipsalu Mait
Published: August 2020

The main components of landfill gas are methane and carbon dioxide. Emissions of methane, a strong greenhouse gas, can be minimized by in situ oxidation in the bioactive cover layer. Typically, organic-rich porous materials such as compost are used for this process. In this study, the material for a biocover was obtained from the same landfill by landfill mining. The objective was to study the spatial distribution of gases and the efficiency of methane degradation in the biocover. The methane and carbon dioxide emissions were measured at 29 measuring points six times on the surface and once at a depth of 0.5 m. The highest values of both gases from the surface were recorded in July 2015: 1.0% for CO2 and 2.1% for CH4. Deeper in the cover layer, higher values of methane concentration were recorded. The results showed that (a) methane from the waste deposit was entering the biocover, (b) the migration of methane to the atmosphere was low, (c) fluctuations in the composition of gases are seasonal, and (d) the trend in the concentration of CH4 over time was an overall decrease. The described cover design reduces the CH4 emissions in landfills using elements of circular economy—instead of wasting natural soils and synthetic liners for the construction of the final cover layer, functional waste-derived materials can be used.
Urban Video Surveillance as a Tool to Improve Security in Public Spaces
Authors: Socha Robert , Kogut Bogusław
Published: August 2020

Video surveillance is an integral part of the contemporary world. Its use increases the sense of security but also generates certain risks. Laws do not always clearly and comprehensively define the rules for installing and using video surveillance and different rules are adopted in different countries to address these issues. This article presents an analysis of statistical data concerning urban video surveillance as a tool to improve the security of public spaces in the city of Katowice using the example of the operation of the Katowice Smart Surveillance and Analysis System. By presenting the operation of video surveillance in two different time periods, it was possible to assess the effectiveness of urban video surveillance for the security of public spaces in terms of particularly onerous crimes. The technical and organizational solutions applied, as in the case of the Katowice Smart Surveillance and Analysis System, made it possible to assess the impact of the operation of the system on offenses and the number of legal proceedings.
Enhancing the Smart Tourism Experience for People with Visual Impairments by Gamified Application Approach through Needs Analysis in Hong Kong
Authors: Huang Lusha , Lau Newman
Published: August 2020

Sustainability studies in tourism are increasingly emphasizing social matters, for instance, “Accessible Tourism for All.” Research on people with visual impairments with smart tourism has so far been sparse. In order to resonate with the global call for sustainable tourism development, this paper aims to contribute to understanding the aspirations of people with visual impairments in terms of tourism and to explore how smart tourism destinations could potentially enhance the tourism experience they offer. We utilized multisensory participant observations and interviews to empathize with the target users and gain insights into their needs. The results showed that they were generally positive about travel, particularly autonomy, achievement needs, and socializing with other individuals. The findings also shed light on their desire to play games on their phones. Consequently, based on the findings and the theoretical groundings of PERMA model and MDA framework from positive psychology and game design, the study proposed a gamified approach to future tourism app design for people with visual impairments, which could enhance engagement, motivation, and enjoyment in the tourism experience.
Suggesting a Framework for Performance Evaluation of Tourist Attractions: A Balance Score Approach
Authors: Wang Yuejiao , Shen Shiwei , Sotiriadis Marios , Zhang Li
Published: August 2020

The aim of this study is to suggest a framework for performance evaluation of smart tourism initiatives/projects implemented in tourist attractions. The suggested tool is based on the theoretical foundations of Service-Dominant Logic and the Balance Scorecard framework, an established strategic management framework that maps an organization’s strategic objectives into performance indicators. The suggested template for performance evaluation was tested and validated within the context of China, and 30 interviews were conducted with managers of tourist attractions rated the highest quality level in Zhejiang Province, China. The empirical study’s results indicated that (i) the crafted Balance Scorecard template is implementable and of high utility; (ii) a volume of 8 Critical Success Factors and 22 Key Performance Indicators constitute an integrated set to monitor and assess the performance in a comprehensive manner; (iii) the key factors and indicators should be the same regardless of the type of ownership and management (public or private); and (iv) technological infrastructure and services are evolving; however, the principles and tools remain the same regardless the advent of smart paradigm.
Corporate Environmentalism: An Emerging Economy Perspective
Authors: Saleem Farida , Gopinath C. , Khattak Amira , Qureshi Saiqa Saddiqa , Allui Alawiya , Adeel Ahmad
Published: August 2020

Corporate environmentalism recognizes the importance of environmental issues to a corporation and how it integrates these issues into its strategy. Studies in the US and Spain have identified three variables, namely, public concern, regulatory pressures and competitive advantage to be significant in influencing corporate environmentalism. Emerging countries, although significant contributors to global environmental concerns have generally been ignored in the corporate environmentalism literature. We collected data through a questionnaire survey from managers operating in a variety of industries in Pakistan, an emerging market. Structural equation modeling technique was used for data analysis. Results show that, unlike in the developed countries, customer concern is not an antecedent of corporate environmentalism. Top management commitment was found to be important, both directly and as a mediating variable which has important implications for research and policy in this context.
Gendered Beliefs in STEM Undergraduates: A Comparative Analysis of Fuzzy Rating versus Likert Scales
Authors: Castaño Ana M. , Lubiano M. Asunción , García-Izquierdo Antonio L.
Published: August 2020

Women are underrepresented in growing positions such as those related to STEM field careers (i.e., science, technology, engineering, and mathematics). One of the causes for remaining out of that field could lie on gender stereotypes. Undergraduate stereotypes and beliefs are important as could easily uphold future gender segregation at the workplace. In the research arena the measurement of those biased beliefs is important as most commonly used Likert scales (LS) could raise problems in terms of accuracy. As fuzzy rating scales (FRS) are a promising measurement alternative, the aim of this study is to compare the properties of FRS against LS. We conducted a cross-sectional study with 262 STEM and non-STEM participants who answered to a questionnaire that, besides gendered beliefs and injustice perception towards the situation of women at the workplace, included personal characteristics as coursed degree and working experience. Results pointed out, on one hand, that FRS allowed for a better capture of the variability of individual responses, but on the other hand, that LS were better valued than FRS in what is concerned with satisfaction and ease of response. Advantages of FRS for psychosocial measurement are discussed to facilitate the study around causes of segregation that excludes women from the STEM labour market.
Sink or Swim? Impacts of Management Strategies on Small Business Survival and Recovery
Authors: Marshall Maria I. , Schrank Holly L.
Published: August 2020

The rate of small business demise is exacerbated by exogenous events such as natural disasters that threaten even the healthiest business. This study focused on the effects of management strategies used by small business owners affected by a natural disaster and the resulting recovery status eight years after Hurricane Katrina. The results indicate that location, human resource, and financial management decisions affect operating status and recovery. Both pre-and post-disaster strategies and across system exchanges were utilized and predicted survival and recovery, e.g., financial managerial strategies utilized post disaster predicted whether a business would fully recover, but effective overall management strategies differed over time and operating category.
The Study of Evaluation Index of Growth Evaluation of Science and Technological Innovation Micro-Enterprises
Authors: Lin Yao-Chin , Chen Chun-Liang , Chao Cheng-Fu , Chen Wei-Hung , Pandia Henry
Published: August 2020

Micro-enterprises are critical to the national economy in many countries, although they have many limitations. Therefore, governments have proposed many policies and programs to generate and enhance micro-enterprises’ growth to assure national economies’ growth and sustainability. This study evolves an index to evaluate growth in science and technological innovation among micro-enterprises that participated in collaborative innovation through a counselling program. The results not only define growth indicators through a literature review but also examine evidence of growth among 10 micro-enterprises through interviewing subjects located in various regions in Taiwan and related to different industries. This study uses multiple case study methods and data statistics to verify growth indicators; this reveals that micro-enterprises focusing on collaborative technological innovation demonstrated an increasing trend toward this study’s chosen growth indicators in a majority of cases. According to the survey results, nine indicators lead to positive growth. Finally, after establishing growth assessment indicators, the data collected from an empirical investigation proves the indicators’ credibility. The study then discusses the chosen cases’ experiences with growth and presents managerial implications.