bluuebunny/crossref_metadata_2025 huggingface dataset:
Edge Computing and Blockchain for Quick Fake News Detection in IoV
Authors: Xiao Yonggang , Liu Yanbing , Li Tun
Published: August 2020

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.
Physical Unclonable Function and Hashing Are All You Need to Mutually Authenticate IoT Devices
Authors: Mostafa Ahmed , Lee Suk Jin , Peker Yesem Kurt
Published: August 2020

Internet of Things (IoT) has become the driving force in modern day technology with an increasing and rapid urge to create an intelligent, efficient, and connected world. IoT is used in manufacturing, agriculture, transportation, education, healthcare and many other business environments as well as home automation. Authentication for IoT devices is essential because many of these devices establish communication with servers through public networks. A rigorous lightweight device authentication scheme is needed to secure its physical hardware from cloning or side-channel attacks and accommodate the limited storage and computational power of IoT devices in an efficient manner. In this paper, we introduce a lightweight mutual two-factor authentication mechanism where an IoT device and the server authenticate each other. The proposed mechanism exploits Physical Unclonable Functions (PUFs) and a hashing algorithm with the purpose of achieving a secure authentication and session key agreement between the IoT device and the server. We conduct a type of formal analysis to validate the protocol’s security. We also validate that the proposed authentication mechanism is secure against different types of attack scenarios and highly efficient in terms of memory storage, server capacity, and energy consumption with its low complexity cost and low communication overhead. In this sense, the proposed authentication mechanism is very appealing and suitable for resource-constrained and security-critical environments.
Hyperelastic Ex Vivo Cervical Tissue Mechanical Characterization
Authors: Callejas Antonio , Melchor Juan , Faris Inas H. , Rus Guillermo
Published: August 2020

This paper presents the results of the comparison between a proposed Fourth Order Elastic Constants (FOECs) nonlinear model defined in the sense of Landau’s theory, and the two most contrasted hyperelastic models in the literature, Mooney–Rivlin, and Ogden models. A mechanical testing protocol is developed to investigate the large-strain response of ex vivo cervical tissue samples in uniaxial tension in its two principal anatomical locations, the epithelial and connective layers. The final aim of this work is to compare the reconstructed shear modulus of the epithelial and connective layers of cervical tissue. According to the obtained results, the nonlinear parameter A from the proposed FOEC model could be an important biomarker in cervical tissue diagnosis. In addition, the calculated shear modulus depended on the anatomical location of the cervical tissue (μepithelial = 1.29 ± 0.15 MPa, and μconnective = 3.60 ± 0.63 MPa).
Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images
Authors: Piniarski Karol , Pawłowski Paweł , Dąbrowski Adam
Published: August 2020

This paper presents an experimental evaluation of real-time pedestrian detection algorithms and their tuning using the proposed universal performance index. With this index, the precise choice of various parameters is possible. Moreover, we determined the best resolution of the analysis window, which is much lower than the initial window. By such means, we can speed-up the processing (i.e., reduce the classification time by 74%). There are cases in which we increased both the processing speed and the classification accuracy. We made experiments with various baseline detectors and datasets in order to confirm versatility of the proposed ideas. The analyzed classifiers are those typically applied to detection of pedestrians, namely: aggregated channel feature (ACF), deep convolutional neural network (CNN), and support vector machine (SVM). We used a suite of five precisely chosen night (and day) IR vision datasets.
A Novel 2-DOF Lorentz Force Actuator for the Modular Magnetic Suspension Platform
Authors: Yang Fei , Zhao Yong , Mu Xingke , Zhang Wenqiao , Jiang Lingtong , Yue Honghao , Liu Rongqiang
Published: August 2020

The modular magnetic suspension platform depends on multi degree of freedom of Lorentz force actuators for large bearing capacity, high precision positioning and structure miniaturization. To achieve the integration of vertical driving force and horizontal driving force, a novel 2- (two degrees-of-freedom) DOF Lorentz force actuator is developed by designing the pose of the windings and permanent magnets (PMs). The structure and the working principle are introduced. The electromagnetic force mathematical model is established by the equivalent magnetic circuit method to analyze the coupling of magnetic flux. The distribution characteristics of magnetic flux density are analyzed by the finite-element method (FEM). It is found that the coupling of the magnetic flux and the large magnetic field gradient severely reduce the uniformity of the air-gap magnetic field. The electromagnetic force characteristic is investigated by FEM and measurement experiments. The difference between FEM and experiment results is within 10%. The reasons of driving force fluctuation are explained based on the distribution of air-gap magnetic field. The actuator performance are compared under the sliding mode control algorithm and PID control algorithm and the positioning accuracy is 20 μm and 15 μm respectively. Compared with the similar configuration, the motion range and force coefficient of the Lorentz force actuator in this paper are larger. It has a certain guiding significance on the structure design of the multi degree of freed Lorentz force actuator.
Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning
Authors: Jena Ratiranjan , Pradhan Biswajeet , Al-Amri Abdullah , Lee Chang Wook , Park Hyuck-jin
Published: August 2020

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management.
A Real-World Approach on the Problem of Chart Recognition Using Classification, Detection and Perspective Correction
Authors: Araújo Tiago , Chagas Paulo , Alves João , Santos Carlos , Sousa Santos Beatriz , Serique Meiguins Bianchi
Published: August 2020

Data charts are widely used in our daily lives, being present in regular media, such as newspapers, magazines, web pages, books, and many others. In general, a well-constructed data chart leads to an intuitive understanding of its underlying data. In the same way, when data charts have wrong design choices, a redesign of these representations might be needed. However, in most cases, these charts are shown as a static image, which means that the original data are not usually available. Therefore, automatic methods could be applied to extract the underlying data from the chart images to allow these changes. The task of recognizing charts and extracting data from them is complex, largely due to the variety of chart types and their visual characteristics. Other features in real-world images that can make this task difficult are photo distortions, noise, alignment, etc. Two computer vision techniques that can assist this task and have been little explored in this context are perspective detection and correction. These methods transform a distorted and noisy chart in a clear chart, with its type ready for data extraction or other uses. This paper proposes a classification, detection, and perspective correction process that is suitable for real-world usage, when considering the data used for training a state-of-the-art model for the extraction of a chart in real-world photography. The results showed that, with slight changes, chart recognition methods are now ready for real-world charts, when taking time and accuracy into consideration.
Machine Learning-Based IoT-Botnet Attack Detection with Sequential Architecture
Authors: Soe Yan Naung , Feng Yaokai , Santosa Paulus Insap , Hartanto Rudy , Sakurai Kouichi
Published: August 2020

With the rapid development and popularization of Internet of Things (IoT) devices, an increasing number of cyber-attacks are targeting such devices. It was said that most of the attacks in IoT environments are botnet-based attacks. Many security weaknesses still exist on the IoT devices because most of them have not enough memory and computational resource for robust security mechanisms. Moreover, many existing rule-based detection systems can be circumvented by attackers. In this study, we proposed a machine learning (ML)-based botnet attack detection framework with sequential detection architecture. An efficient feature selection approach is adopted to implement a lightweight detection system with a high performance. The overall detection performance achieves around 99% for the botnet attack detection using three different ML algorithms, including artificial neural network (ANN), J48 decision tree, and Naïve Bayes. The experiment result indicates that the proposed architecture can effectively detect botnet-based attacks, and also can be extended with corresponding sub-engines for new kinds of attacks.
Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models
Authors: Hameed Zabit , Zahia Sofia , Garcia-Zapirain Begonya , Javier Aguirre José , María Vanegas Ana
Published: August 2020

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.
Closed-Loop Elastic Demand Control under Dynamic Pricing Program in Smart Microgrid Using Super Twisting Sliding Mode Controller
Authors: Khan Taimoor Ahmad , Ullah Kalim , Hafeez Ghulam , Khan Imran , Khalid Azfar , Shafiq Zeeshan , Usman Muhammad , Qazi Abdul Baseer
Published: August 2020

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid.
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140
Authors: Kuntoğlu Mustafa , Aslan Abdullah , Sağlam Hacı , Pimenov Danil Yurievich , Giasin Khaled , Mikolajczyk Tadeusz
Published: August 2020

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).
Fast Single-Image HDR Tone-Mapping by Avoiding Base Layer Extraction
Authors: Fahim Masud An-Nur Islam , Jung Ho Yub
Published: August 2020

The tone-mapping algorithm compresses the high dynamic range (HDR) information into the standard dynamic range for regular devices. An ideal tone-mapping algorithm reproduces the HDR image without losing any vital information. The usual tone-mapping algorithms mostly deal with detail layer enhancement and gradient-domain manipulation with the help of a smoothing operator. However, these approaches often have to face challenges with over enhancement, halo effects, and over-saturation effects. To address these challenges, we propose a two-step solution to perform a tone-mapping operation using contrast enhancement. Our method improves the performance of the camera response model by utilizing the improved adaptive parameter selection and weight matrix extraction. Experiments show that our method performs reasonably well for overexposed and underexposed HDR images without producing any ringing or halo effects.
A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers
Authors: Rahaman G. M. Atiqur , Parkkinen Jussi , Hauta-Kasari Markku
Published: August 2020

In the field of cultural heritage, applied dyes on textiles are studied to explore their great artistic and historic values. Dye analysis is essential and important to plan correct restoration, preservation and display strategy in museums and art galleries. However, most of the existing diagnostic technologies are destructive to the historical objects. In contrast to that, spectral reflectance imaging is potential as a non-destructive and spatially resolved technique. There have been hardly any studies in classification of dyes in textile fibers using spectral imaging. In this study, we show that spectral imaging with machine learning technique is capable in preliminary screening of dyes into the natural or synthetic class. At first, sparse logistic regression algorithm is applied on reflectance data of dyed fibers to determine some discriminating bands. Then support vector machine algorithm (SVM) is applied for classification considering the reflectance of the selected spectral bands. The results show nine selected bands in short wave infrared region (SWIR, 1000–2500 nm) classify dyes with 97.4% accuracy (kappa 0.94). Interestingly, the results show that fairly accurate dye classification can be achieved using the bands at 1480nm, 1640 nm, and 2330 nm. This indicates possibilities to build an inexpensive handheld screening device for field studies.
In Field Detection of Downy Mildew Symptoms with Proximal Colour Imaging
Authors: Abdelghafour Florent , Keresztes Barna , Germain Christian , Da Costa Jean-Pierre
Published: August 2020

This paper proposes to study the potentialities of on-board colour imaging for the in-field detection of a textbook case disease: the grapevine downy mildew. It introduces an algorithmic strategy for the detection of various forms of foliar symptoms on proximal high-resolution images. The proposed strategy is based on structure–colour representations and probabilistic models of grapevine tissues. It operates in three steps: (i) Formulating descriptors to extract the characteristic and discriminating properties of each class. They combine the Local Structure Tensors (LST) with colorimetric statistics calculated in pixel’s neighbourhood. (ii) Modelling the statistical distributions of these descriptors in each class. To account for the specific nature of LSTs, the descriptors are mapped in the Log-Euclidean space. In this space, the classes of interest can be modelled with mixtures of multivariate Gaussian distributions. (iii) Assigning each pixel to one of the classes according to its suitability to their models. The decision method is based on a “seed growth segmentation” process. This step exploits statistical criteria derived from the probabilistic model. The resulting processing chain reliably detects downy mildew symptoms and estimates the area of the affected tissues. A leave-one-out cross-validation is conducted on a dataset constituted of a hundred independent images of grapevines affected only by downy mildew and/or abiotic stresses. The proposed method achieves an extensive and accurate recovery of foliar symptoms, with on average, a 83% pixel-wise precision and a 76% pixel-wise recall.
Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing
Authors: Mei Han , Han Pengfei , Wang Yinan , Zeng Ning , Liu Di , Cai Qixiang , Deng Zhaoze , Wang Yinghong , Pan Yuepeng , Tang Xiao
Published: August 2020

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached −24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary.
Model Predictive Control of a Road Junction
Authors: Liberati Francesco
Published: August 2020

This paper presents a model predictive control (MPC) approach for optimally managing the traffic light (TL) signals at a road junction. The objective is to improve queue balancing compared to traditional control strategies where TL signals are periodic. The resulting MPC optimization problem is of quadratic mixed-integer nature. The proposed approach is validated via simulations based on a real scenario.
Dialogic Feminist Gatherings: Impact of the Preventive Socialization of Gender-Based Violence on Adolescent Girls in Out-of-Home Care
Authors: Salceda Marifa , Vidu Ana , Aubert Adriana , Roca Esther
Published: August 2020

Gender-based violence is a social scourge with an increasing incidence at younger ages. Many studies have focused on finding effective solutions for overcoming this problem; however, few studies have analyzed the contribution of interactive learning environments to gender-based violence prevention. This article attempts to fill this gap by showing the impact of Dialogic Feminist Gatherings on the preventive socialization against gender-based violence toward adolescent girls (aged 15–18) who are in out-of-home care and living in shelters—part of the institutional protection system—for different reasons that are primarily associated with violence and a lack of family protection. This qualitative study was conducted using the communicative methodology involving fifteen daily life stories that analyze the dialogues and reflections produced among the girls during Dialogic Feminist Gatherings. The results show the acquisition of competencies in aspects such as attraction, election, and equality in sexual-affective relationships. These dimensions, when developed through Dialogic Feminist Gatherings, are consistent with the scientific literature that characterized them as protective factors against gender-based violence. This study concludes by contrasting participants’ daily life reality with scientific evidence, which makes possible new methods of the preventive socialization against gender-based violence for adolescent girls in out-of-home care.
Short-Term Effects of Sewage Sludge Compost Amendment on Semiarid Soil
Authors: Curci Maddalena , Lavecchia Anna , Cucci Giovanna , Lacolla Giovanni , De Corato Ugo , Crecchio Carmine
Published: August 2020

The adoption of sewage sludge as an agricultural management strategy to improve soil properties and crop production is attracting great interest. Despite many positive effects on soil inorganic and organic components reported for different soil types, little information is available on sewage sludge application on Mediterranean soils, as well as on its use at different dose rates. The objectives of the present research was to evaluate, through an integrated approach, the effects of sewage sludge compost from urban wastewater on physicochemical, hydrological, biochemical parameters, and microbiota composition in soil pots under a three-year crop rotation system. Four different doses of sewage sludge compost (C3, C6, C9, C12) from municipal wastewater and a dose of them in combination with mineral fertilizer (C6N) were used. We have used 3-6-9-12 Mg/ha of sewage sludge compost for the treatments C3, C6, C9 and C12, respectively, and 6 Mg/ha of sewage sludge compost in combination with 60 kg/ha of ammonium nitrate for the treatment C6N.The effects were compared to non-fertilized (C0) and mineral fertilized (Min) sets of controls. The electrical conductivity, soil pH, stability of soil aggregates, percent of moisture of the dry soil both at the field capacity and at the wilting point, available P, and exchangeable K were all positively affected by increasing the amounts of composted sludge. The organic carbon and total N increased up to 66% and 39%, respectively. Increased enzymatic activities and microbial biomass were also observed in soil after the application of sewage sludge compost when compared to un-amended control. A higher richness and evenness among the soil plots amended with sewage sludge compost was observed, with no significant differences among the application dose rates, when compared to the un-amended soil control and soil treated with a mineral fertilizer. A three-year amendment was able to separate soil plots amended with high doses of sewage sludge compost from the low dose amended and control samples. Among the microbial groups responsible for such marked separation, bacteria belonging to Actinobacteria, Acidobacteria, Cyanobacteria and Bacteroidetes contribute the most, with a shift from oligotrophic to copiotrophic taxa. Significant changes in bacterial composition and taxonomic structure should be considered in order to properly balance agronomic and economic advantages with environmental concerns. After all, our results have evidenced the effects of sewage sludge amendment on different soil properties, microbial activity, and composition already after a short period of application. The findings are particularly relevant in semiarid soils, where an immediate restoration of soil fertility by short-term compost application is needed.
Drought Stress Alleviation by ACC Deaminase Producing Achromobacter xylosoxidans and Enterobacter cloacae, with and without Timber Waste Biochar in Maize
Authors: Danish Subhan , Zafar-ul-Hye Muhammad , Fahad Shah , Saud Shah , Brtnicky Martin , Hammerschmiedt Tereza , Datta Rahul
Published: August 2020

The high consumption of water in industries, domestic areas and increasing earth temperature are major hurdles for the optimization of maize yield. Being the third most widely cultivated cereal crop, improvement in maize yield is a big challenge under the limited availability of irrigation. As the water requirement for maize cultivation is high, it is time to introduce technologies that can mitigate drought stress and are environmentally friendly. The inoculation of rhizobacteria with ‘1-aminocyclopropane-1-carboxylate deaminase’ (ACCD) can play an imperative role in that regard by decreasing stress ethylene in plants. Biochar (BC) can also alleviate drought stress. Therefore, a field study was conducted, to examine the single and combined application of drought-tolerant plant-growth-promoting rhizobacteria (PGPRs) Achromobacter xylosoxidans and Enterobacter cloacae, with 15 Mg ha−1 of timber waste biochar (TWBC) at normal irrigation = 16 irrigations, mild drought = 14 irrigations and severe drought = 12 irrigation for maize cultivation. A significant improvement in shoot dry weight (28%), 1000-grains weight (19%), grain yield (27%), concentrations of N (43%), P (92%) and K (71%) in grains, rate of photosynthesis (33%), transpiration rate (55%), stomatal conductance (104%), chlorophyll A (33%), chlorophyll B (62%) and total chlorophyll (45%) of maize was noted under drought stress where E. cloacae + TWBC was applied. Likewise, the application of A. xylosoxidans + TWBC also significantly enhanced the plant height (24%) and cob length (9%) of maize under drought stress. In conclusion, E. cloacae is more effective than A. xylosoxidans, with 15 Mg ha−1 TWBC to increase maize yield under drought stress, due to the potential of higher ‘1-aminocyclopropane-1-carboxylate’ (ACC)-deaminase synthesis, better nutrient solubilization and indole acetic acid (IAA) production.
Fostering Social Project Impact with Twitter: Current Usage and Perspectives
Authors: Pejić Bach Mirjana , Pulido Cristina M. , Suša Vugec Dalia , Ionescu Vladia , Redondo-Sama Gisela , Ruiz-Eugenio Laura
Published: August 2020

Social impact assessment has become a major concern within the research community. While different methodological advancements have been made to better display, as well as to measure, achieved impacts, social media has proved to be a potential domain to generate many new opportunities to support both the communication as well as the realization of social impact. Within this context, the current research presents an analysis of how Twitter is used among a subset of research projects to maximize social impact. The research focuses on the use of Twitter, as one of the most often used social media, by the members of scientific projects funded under one part of the FP7 funding framework of the European Union called Science in Society. The data were analyzed using NVivo, and WordStat Provalis software. The results presented in this study include exploratory data analysis, topic mining and the analysis of the impact of projects on Twitter. The results indicate moderate use of Twitter among the observed projects, but with a strong focus on the dissemination of project results, thus indicating a trend towards the usage of social media for communicating the social impact of research projects.
The Evolution of the Kazakhstani Silk Road Section from a Transport into a Logistics Corridor and the Economic Sustainability of Regional Development in Central Asia
Authors: Taisarinova Aislu , Loprencipe Giuseppe , Junussova Madina
Published: August 2020

Central Asian countries attract investment in transport infrastructure to rebuild the Silk Road paths and enjoy economic benefits from the participation in international trade. The Kazakhstani government approached the Russian and Chinese governments intending to join the Western Europe–Western China (WE–WC) initiative to boost the country’s regional development. The paper aims to assess how the WE–WC transport corridor affected the economic potential of linking cities and regions starting from the quality of transport infrastructure and leading to their export potential. The study’s findings showed that the Kazakhstan section of the WE–WC corridor was at an early stage of transformation from a transport into an economic corridor. While the Russia-Uzbekistan section continues to serve mainly a transit function and operate at the level of transport infrastructure, the China-Kyrgyzstan section has started evolving from the level of multimode transport corridor to the level of logistics corridor. The economic sustainability of the WE–WC linking mining and agricultural regions of Kazakhstan still comes into question and depends on the government’s further region-specific policy actions.
Exploring Arbitrage Strategies in Corporate Social Responsibility Companies
Authors: Montoya-Cruz Estefanía , Ramos-Requena José Pedro , Trinidad-Segovia Juan Evangelista , Sánchez-Granero Miguel Ángel
Published: August 2020

Today, Socially Responsible financial investment has taken on particular importance. Investors normally select their most profitable investments, but over the years they have appreciated that companies develop Socially Responsible policies. Financial indices have also created Socially Responsible versions. In this paper, we run a statistical arbitrage technique known as Pairs Trading using stocks of the FTSE4GOOD Socially Responsible Index. Different strategies will be tested to demonstrate that there are no significant differences between the performance of the portfolio composed by Corporate Social Responsibility (CSR) stocks and those composed by ordinary stocks.
Consumer Behaviour of Purchasing Biofortified Food Products
Authors: Timpanaro Giuseppe , Bellia Claudio , Foti Vera Teresa , Scuderi Alessandro
Published: August 2020

In light of increasing attention on biofortified products from the institutional, scientific and industrial worlds, we investigate consumer knowledge, consumer choice, the relationship between consumer choice and lifestyles, willingness to pay, and factors influencing consumer groups regarding biofortified foods. Complicating the matter is the lack of a clear definition of biofortified foods in the last proposal of the Codex Alimentarius Commission dated 26 November 2018. Research has shown the importance of market information and variables related to lifestyle, socio-demographic characteristics, knowledge of nutritional principles, and diet, to understanding consumers’ purchase and consumption choices regarding biofortified products. Our research shows that at present the potential consumer of biofortified food products is generally confused and uninformed, conditions that, even when there is a high willingness to pay, limit purchases of biofortified products. Even in the absence of a concise definition and clear labelling at a globally recognized level, in Italy biofortified products are increasingly widespread (products biofortified with selenium, iodine, etc.), confirming consumer demand for this category of product.
Mapping Role-Playing Games in Ibero-America: An Educational Review
Authors: Grande-de-Prado Mario , Baelo Roberto , García-Martín Sheila , Abella-García Víctor
Published: August 2020

Role-playing games (RPGs) have a controversial public image in several countries, including Spain. These fears lack a scientific basis, as role-playing games may be useful in education. Educational trends such as gamification are helping to change this perspective, incorporating elements of RPGs in applications like learning management systems (LMS), e.g., Classcraft. Given the increased research interest in this topic, this paper presents a systematic literature review (SLR) report on the state-of-the-art related to RPGs in an Ibero-American education research context. In the study, a comprehensive search is carried out for the most relevant research papers indexed in Latindex, founded through the virtual repository Dialnet for papers between 2010 and 2019 in the field of education. The search chain was ‘role-playing games’, erasing those topics not related. Results show that there are several relevant references, even though they do not seem to have had a great impact. It can be concluded that there is an interest in RPGs in education, especially in Spain, but their potential is still to be developed.
Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis
Authors: Nesticò Antonio , La Marca Marianna
Published: August 2020

It is well known that production activities are often the cause of ecosystem disservices. Such disservices can have serious effects on urban real estate values. But how much is the contraction that the market values of housing suffer due to the polluting emissions produced by a medium-sized foundry? And how large is the urban area within which buildings are depreciated? With this research we intend to give an answer. To this aim, with specific regard to urban apartments free from contractual constraints, the use of multiple regression analysis makes it possible to obtain a function that explains the real estate value through multiple variables, one of which is representative of the ecosystem disservice. The study reveals that the urban area that suffers from the negative effects of polluting industrial activities on property prices can be extensive. On the other hand, the contractions of real estate values can even reach 43%. These results, for the first time expressed in quantitative terms, must direct towards urban planning interventions, and more generally of economic policy, aimed at minimizing the environmental impacts of production activities. This is not only for the essential obligations to protect environment and human health, but also in relation to the direct economic implications of the decrease in the value of real estate.
Impact of Placement of Fitbit HR under Laboratory and Free-Living Conditions
Authors: Jung Hyun Chul , Kang Minsoo , Lee Nan Hee , Jeon Soeun , Lee Sukho
Published: August 2020

Wrist-worn activity monitors have become accessible for measuring physical activities, but an activity monitor’s accuracy worn at different placements is not well understood. This study aimed to examine the differences in measurements of heart rates, step counts, and calories estimated from the fitness tracker worn at different locations and the accuracy of Fitbit HR against criterion measures. Thirty-two healthy adults participated in this study. Participants wore Fitbit HR at four different locations (right proximal, distal, and left proximal, distal). Treadmill exercise consisted of five 5-min phases including slow walking and jogging. Free-living activities involved ten 5-min activities with four different intensities. We found that the placement of Fitbit HR has no significant influence on the measurement outcomes overall. However, significant differences were observed in the step counts between left and right while climbing stairs (p = 0.003) and sports activities (p < 0.001). The accuracy of Fitbit HR measurements against the criterion measure was moderate. However, we found out that Fitbit HR overestimated the calories regardless of their positions and activity types. In conclusion, the different placements of Fitbit HR do not have a significant influence on the measurements and Fitbit HR was moderately associated with criterion reference devices for measures of heart rate and steps.
Assessing the Potential of Improving Livelihoods and Creating Sustainable Socio-Economic Circumstances for Rural Communities in Upper Egypt
Authors: Ahmed Osama , Sallam Walid
Published: August 2020

Agriculture in Upper Egypt is characterized by unsustainable farming practices and a lack of the use of market intelligence. Improving agriculture in Upper Egypt is necessary, as farmers in this region need to meet the quality standards required by international markets if they are to increase export volumes. For this reason, agricultural interventions are required to transition from the current traditional farming systems and marketing practices to sustainable conservation farming practices and the use of market intelligence and logistics. The main aim of this research study is to assess the potential for improving the livelihoods of rural communities by conducting a cost–benefit analysis (CBA) and conducting a risk analysis using the Monte Carlo simulation method for the proposed agricultural interventions. Our results imply that the analyzed interventions are viable from both a financial and socio-economic point of view. The impacts of the interventions reveal real incremental employment opportunities at the farming level. In addition, the interventions have a very low probability of negative returns and become almost zero when we add the economic benefit to society.
Employer Ratings through Crowdsourcing on Social Media: An Examination of U.S. Fortune 500 Companies
Authors: Suen Hung-Yue , Hung Kuo-En , Tseng Fan-Hsun
Published: August 2020

The aims of this study are to examine the effect of crowdsourced employer ratings and employee recommendations of an employer as an employer of choice, to examine which employer ratings that represent different employee value propositions can predict the overall employer rating through crowdsourcing, to examine whether the Fortune 500 ranking can also influence overall employer ratings, and to mine which keywords are popularly used when employees post a comment about the pros and cons of their employers on a crowdsourced employer branding platform. The study collected crowdsourced employer review data from Glassdoor based on 2019 Fortune 500 companies, and the results found that crowdsourced employer ratings are positively associated with “recommend to a friend,” while culture and values predominantly influence overall employer ratings. The rank of Fortune 500 has less predictive power for overall employer ratings than for other specific employer ratings, except for business outlook. The most popular keywords of Pros on Glassdoor are work–life balance and pay and benefits, whereas the most popular keywords of Cons on Glassdoor are work–life balance and upper management.
What are the Factors affecting Tourist Behavior based on the Perception of Risk? Romanian and Serbian Tourists’ Perspective in the Aftermath of the recent Floods and Wildfires in Greece
Authors: Kovačić Sanja , Mărgărint Mihai Ciprian , Ionce Ruxandra , Miljković Đurđa
Published: August 2020

During the summer season 2018 and 2019, natural hazards (namely, floods and wildfires) have occurred at some of the most popular tourist destinations in Greece, endangering tourists’ safety and vacation quality. These have influenced tourists’ perception of Greece as a “potentially risky destination”, causing a decrease in the number of tourists willing to visit this country. However, the current study assumes that some tourists will show more courageous travel behavior in the aftermath of natural hazards, while others will remain cautious when deciding to travel to risky destinations. Therefore, the questionnaire on a sample group of 431 respondents from Serbia and Romania was conducted to explore the factors influencing such differences. The study aims to explore whether tourists’ individual characteristics influence tourism worries and tourist behavior based on perceived risks. The study also intends to analyze the moderating role of tourists’ nationality, considering the relationship between personality and tourist behavior based on the perceived risk. The findings provide evidence that not only tourists’ personality but also sociodemographic characteristics influence tourism worries and tourist behavior based on the perceived risk. Additionally, the study is the first to explore and confirm the role of nationality in tourist behavior based on the perceived risk, as well as the moderation role of nationality in regression between tourist’s personality and behavior based on the perception of risk. Theoretical and practical implications are discussed in the paper.
From EU Directives to Local Stormwater Discharge Permits: A Study of Regulatory Uncertainty and Practice Gaps in Denmark
Authors: Jensen Ditte M. R. , Thomsen Anja T. H. , Larsen Torben , Egemose Sara , Mikkelsen Peter S.
Published: August 2020

Climate changes and urbanization push cities to redesign their drainage systems, which may increase separate stormwater discharges to local recipients. In the EU, regulation of these is governed by the Water Framework Directive (WFD) and the Floods Directive, but national implementation varies and is often supplemented with local non-legislative guidelines. By reviewing trends and discrepancies in the Danish regulation practice for separated stormwater discharges, this article investigates how the directives are put into effect. A legislative gap for separate stormwater discharges introduces uncertainty in the discharge permit conditions, which especially affect conditions targeting water quality. We point to several topics to be addressed, e.g., the level at which the regulation of separate stormwater discharges takes place, opportunities for coordination with flood risk and climate change adaptation initiatives, as well as uncertainties regarding the application of Best Available Techniques. Working with these issues would elevate the regulation practice and aid regulators in reaching a more holistic and consistent approach, thus improving chances of reaching the desired recipient status before or after the WFD deadline in 2027. This could be undertaken at river basin, river basin district or national level, but there is also potential for harvesting mutual benefits by addressing these challenges internationally.