bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploiting Direct Link in Two-Way Half-Duplex Sensor Network over Block Rayleigh Fading Channel: Upper Bound Ergodic Capacity and Exact SER Analysis
Authors: Tin Phu Tran , Nguyen Tan N. , Tran Minh , Trang Tran Thanh , Sevcik Lukas
Published: February 2020

Relay communication, in which the relay forwards the signal received by a source to a destination, has a massive consideration in research, due to its ability to expand the coverage, increase the capacity, and reduce the power consumption. In this paper, we proposed and investigated energy harvesting (EH) based two-way half-duplex (TWHD) relaying sensors network using selection combining (SC) over block Rayleigh fading channel. In this model, we proposed the direct link between two sources for improving the system performance. For the system performance analysis, we investigated and derived the closed-form of the exact and upper bound Ergodic capacity (EC) and the exact form of the symbol error ratio (SER). By using the Monte Carlo simulation, the correctness of the research results is verified in the influence of the main system parameters. From the discussions, we can see that the analytical and simulation agree well with each other.
Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data—Part I
Authors: Semanjski Silvio , Semanjski Ivana , De Wilde Wim , Muls Alain
Published: February 2020

The vulnerability of the Global Navigation Satellite System (GNSS) open service signals to spoofing and meaconing poses a risk to the users of safety-of-life applications. This risk consists of using manipulated GNSS data for generating a position-velocity-timing solution without the user’s system being aware, resulting in presented hazardous misleading information and signal integrity deterioration without an alarm being triggered. Among the number of proposed spoofing detection and mitigation techniques applied at different stages of the signal processing, we present a method for the cross-correlation monitoring of multiple and statistically significant GNSS observables and measurements that serve as an input for the supervised machine learning detection of potentially spoofed or meaconed GNSS signals. The results of two experiments are presented, in which laboratory-generated spoofing signals are used for training and verification within itself, while two different real-world spoofing and meaconing datasets were used for the validation of the supervised machine learning algorithms for the detection of the GNSS spoofing and meaconing.
Calibration Procedure to Test the Effects of Multiple Influence Quantities on Low-Power Voltage Transformers
Authors: Mingotti Alessandro , Peretto Lorenzo , Tinarelli Roberto
Published: February 2020

The article presents a study on low-power voltage transformers (LPVTs). Considering their increasing spread among Smart Grids, it is fundamental to assess their accuracy behavior in as realistic conditions as possible. Therefore, this article presents a detailed calibration procedure to test LPVTs’ accuracy when various external influence quantities are simultaneously acting on them. In the calibration procedure, the considered quantities are frequency, air temperature, and external electric field. Afterwards, the designed procedure is applied on three different off-the-shelf LPVTs using a measurement setup developed in a laboratory environment. The presented results (i) confirm the easy applicability of the designed calibration procedure; (ii) highlight the various effects of the influence quantities on the accuracy of different types of LPVTs; (iii) confirm the need to include more realistic tests, like the type-tests presented, into the standards to appreciate a wider set of possible in-field behaviors.
Camera Calibration with Weighted Direct Linear Transformation and Anisotropic Uncertainties of Image Control Points
Authors: Barone Francesco , Marrazzo Marco , Oton Claudio J.
Published: February 2020

Camera calibration is a crucial step for computer vision in many applications. For example, adequate calibration is required in infrared thermography inside gas turbines for blade temperature measurements, for associating each pixel with the corresponding point on the blade 3D model. The blade has to be used as the calibration frame, but it is always only partially visible, and thus, there are few control points. We propose and test a method that exploits the anisotropic uncertainty of the control points and improves the calibration in conditions where the number of control points is limited. Assuming a bivariate Gaussian 2D distribution of the position error of each control point, we set uncertainty areas of control points’ position, which are ellipses (with specific axis lengths and rotations) within which the control points are supposed to be. We use these ellipses to set a weight matrix to be used in a weighted Direct Linear Transformation (wDLT). We present the mathematical formalism for this modified calibration algorithm, and we apply it to calibrate a camera from a picture of a well known object in different situations, comparing its performance to the standard DLT method, showing that the wDLT algorithm provides a more robust and precise solution. We finally discuss the quantitative improvements of the algorithm by varying the modules of random deviations in control points’ positions and with partial occlusion of the object.
Resource Usage and Performance Trade-offs for Machine Learning Models in Smart Environments
Authors: Preuveneers Davy , Tsingenopoulos Ilias , Joosen Wouter
Published: February 2020

The application of artificial intelligence enhances the ability of sensor and networking technologies to realize smart systems that sense, monitor and automatically control our everyday environments. Intelligent systems and applications often automate decisions based on the outcome of certain machine learning models. They collaborate at an ever increasing scale, ranging from smart homes and smart factories to smart cities. The best performing machine learning model, its architecture and parameters for a given task are ideally automatically determined through a hyperparameter tuning process. At the same time, edge computing is an emerging distributed computing paradigm that aims to bring computation and data storage closer to the location where they are needed to save network bandwidth or reduce the latency of requests. The challenge we address in this work is that hyperparameter tuning does not take into consideration resource trade-offs when selecting the best model for deployment in smart environments. The most accurate model might be prohibitively expensive to computationally evaluate on a resource constrained node at the edge of the network. We propose a multi-objective optimization solution to find acceptable trade-offs between model accuracy and resource consumption to enable the deployment of machine learning models in resource constrained smart environments. We demonstrate the feasibility of our approach by means of an anomaly detection use case. Additionally, we evaluate the extent that transfer learning techniques can be applied to reduce the amount of training required by reusing previous models, parameters and trade-off points from similar settings.
Acoustic Indoor Localization Augmentation by Self-Calibration and Machine Learning
Authors: Bordoy Joan , Schott Dominik Jan , Xie Jizhou , Bannoura Amir , Klein Philip , Striet Ludwig , Hoeflinger Fabian , Haering Ivo , Reindl Leonhard , Schindelhauer Christian
Published: February 2020

An acoustic transmitter can be located by having multiple static microphones. These microphones are synchronized and measure the time differences of arrival (TDoA). Usually, the positions of the microphones are assumed to be known in advance. However, in practice, this means they have to be manually measured, which is a cumbersome job and is prone to errors. In this paper, we present two novel approaches which do not require manual measurement of the receiver positions. The first method uses an inertial measurement unit (IMU), in addition to the acoustic transmitter, to estimate the positions of the receivers. By using an IMU as an additional source of information, the non-convex optimizers are less likely to fall into local minima. Consequently, the success rate is increased and measurements with large errors have less influence on the final estimation. The second method we present in this paper consists of using machine learning to learn the TDoA signatures of certain regions of the localization area. By doing this, the target can be located without knowing where the microphones are and whether the received signals are in line-of-sight or not. We use an artificial neural network and random forest classification for this purpose.
Investigation of the Impact of Environmental Parameters on Breath Frequency Measurement by a Textile Sensor
Authors: Skrzetuska Ewa , Wojciechowski Jarosław
Published: February 2020

The aim of this work was to develop sensors that enable the monitoring of respiratory frequencies and will be competitive at a global level in replacing conventional electronic sensors based on rigid and uncomfortable materials. The preliminary work carried out showed the real possibility of creating flat fibrous products containing carbon nanotubes with sensory properties. Bearing in mind the production of a textile deformation sensor, textile materials with high elasticity and deformation reversibility were used in the preliminary studies. The authors assumed that it would be possible to conduct registration associated with the measurement of pneumography continuously in various atmospheric conditions and with varying intensification of human physical activity. The conducted experiment allows us to state that the resistance at the level of 10 kΩ is sufficient to collect results of breathing frequency at rest and after physical effort.
Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification
Authors: Zhang Jianming , Lu Chaoquan , Wang Jin , Yue Xiao-Guang , Lim Se-Jung , Al-Makhadmeh Zafer , Tolba Amr
Published: February 2020

Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.
Geomorphometric Methods for Burial Mound Recognition and Extraction from High-Resolution LiDAR DEMs
Authors: Niculiță Mihai
Published: February 2020

Archaeological topography identification from high-resolution DEMs (Digital Elevation Models) is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology through which burial mounds (tumuli) from LiDAR (Light Detection And Ranging) DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further fed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and select the proper training dataset, settings, and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped, and field checked burial mounds from two neighbor study areas of 100 km2 each. The results show that by training the Random Forest on a dataset composed of between 75% and 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using Latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.
Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits
Authors: QIU SEN , Wang Huihui , Li Jie , Zhao Hongyu , Wang Zhelong , Wang Jiaxin , Wang Qiong , Plettemeier Dirk , Bärhold Michael , Bauer Tony , Ru Bo
Published: February 2020

Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.
AMiCUS 2.0—System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis
Authors: Rudigkeit Nina , Gebhard Marion
Published: February 2020

AMiCUS is a human–robot interface that enables tetraplegics to control an assistive robotic arm in real-time using only head motion, allowing them to perform simple manipulation tasks independently. The interface may be used as a standalone system or to provide direct control as part of a semi-autonomous system. Within this work, we present our new gesture-free prototype AMiCUS 2.0, which has been designed with special attention to accessibility and ergonomics. As such, AMiCUS 2.0 addresses the needs of tetraplegics with additional impairments that may come along with multiple sclerosis. In an experimental setup, both AMiCUS 1.0 and 2.0 are compared with each other, showing higher accessibility and usability for AMiCUS 2.0. Moreover, in an activity of daily living, a proof-of-concept is provided that an individual with progressed multiple sclerosis is able to operate the robotic arm in a temporal and functional scope, as would be necessary to perform direct control tasks for use in a commercial semi-autonomous system. The results indicate that AMiCUS 2.0 makes an important step towards closing the gaps of assistive technology, being accessible to those who rely on such technology the most.
Monitoring the Detailed Dynamics of Regional Thermal Environment in a Developing Urban Agglomeration
Authors: Liu Yue , Li Hui , Gao Peng , Zhong Cheng
Published: February 2020

Many studies have revealed the characteristics and spatial-temporal dynamics of the thermal environment in specific cities or urban agglomerations (UA), as well as the associated determining factors. However, few studies focus on the changing relationships (the difference, distance, interaction, etc.) among inner cities’ heat islands in a UA, which represent not only the detailed dynamics of regional thermal environment (RTE), but also the changing competition and cooperation among cities in a developing UA. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products to map and analyze the detailed dynamics of the Beijing-Tianjin-Hebei (BTH) UA thermal environment. From 2001 to 2015, the mean surface urban heat island intensity (SUHII) of the BTH increased significantly, and the surface urban heat islands (SUHIs) in the southern BTH have rapidly increased, expanded and connected, eventually forming a large heat islands agglomeration. According to correlation analysis, urban sprawl probably led to the expansion and enhance of SUHIs in the south plain, while the forest has significantly alleviated urban heat island effect in northern mountains. The results expose the detailed evolution process of BTH thermal environment, and the changing relationships among the inner cities. In a developing UA, mitigation solutions (e.g., ecological corridors or controlling energy consumption) are in demand to stop the formation of a great heat region.
High-Density Surface EMG-Based Gesture Recognition Using a 3D Convolutional Neural Network
Authors: Chen Jiangcheng , Bi Sheng , Zhang George , Cao Guangzhong
Published: February 2020

High-density surface electromyography (HD-sEMG) and deep learning technology are becoming increasingly used in gesture recognition. Based on electrode grid data, information can be extracted in the form of images that are generated with instant values of multi-channel sEMG signals. In previous studies, image-based, two-dimensional convolutional neural networks (2D CNNs) have been applied in order to recognize patterns in the electrical activity of muscles from an instantaneous image. However, 2D CNNs with 2D kernels are unable to handle a sequence of images that carry information concerning how the instantaneous image evolves with time. This paper presents a 3D CNN with 3D kernels to capture both spatial and temporal structures from sequential sEMG images and investigates its performance on HD-sEMG-based gesture recognition in comparison to the 2D CNN. Extensive experiments were carried out on two benchmark datasets (i.e., CapgMyo DB-a and CSL-HDEMG). The results show that, where the same network architecture is used, 3D CNN can achieve a better performance than 2D CNN, especially for CSL-HDEMG, which contains the dynamic part of finger movement. For CapgMyo DB-a, the accuracy of 3D CNN was 1% higher than 2D CNN when the recognition window length was equal to 40 ms, and was 1.5% higher when equal to 150 ms. For CSL-HDEMG, the accuracies of 3D CNN were 15.3% and 18.6% higher than 2D CNN when the window length was equal to 40 ms and 150 ms, respectively. Furthermore, 3D CNN achieves a competitive performance in comparison to the baseline methods.
The Value of Interactive Polling and Intrinsic Motivation When Using English as a Medium of Instruction
Authors: Kim Yeuseung
Published: February 2020

Understanding how technology is used to foster active learning and why it is effective is important in advancing educational practices. The purpose of this study was to test adopting in-class interactive polling to engage college students’ learning in their non-native language. After adopting interactive polling activities for six weeks, non-native speakers of English enrolled in a content-based class taught in English were invited to participate in a survey measuring the outcomes of the intervention. The results showed that students found it more comfortable responding to polls using their smartphones rather than verbally responding to questions in the classroom. Guided by self-determination theory, the results demonstrate that students who report high intrinsic motivation to participate in in-class polling exercises exhibit a more favorable attitude, find the class more engaging, feel they perform better on tests because of the polling exercise, and show higher level of perceived learning than those who reported low intrinsic motivation. The importance of facilitating sustainable student learning by using interactive technology to improve the quality of content-based learning and minimize the potential downside of using English as a medium of instruction is discussed.
Perception of Sustainability of Spanish National Parks: Public Use, Tourism and Rural Development
Authors: Pérez-Calderón Esteban , Prieto-Ballester Jorge Manuel , Miguel-Barrado Vanessa , Milanés-Montero Patricia
Published: February 2020

In the last decade, tourism activity associated with natural areas has stood out as a driver for economic development. Thus, it is a key factor for the economic and social sustainability of the community near a protected area. This paper analyses, considering the tourist exploitation and the public use of the National Park in the last decade, the perception about the sustainability of its geographical area closest. A questionnaire was used and sent to the authorities of the villages closest to each of the 15 National Parks. The structural equation model was used for the design and analysis of the model. The results confirmed significant relationships between the perception of economic development and quality of life, but not with social development. A positive relationship between quality of life and social development is also demonstrated. The three dimensions analysed, economic, social and quality of life, are influencing the perception of sustainability of the geographical area closest to the protected natural area. The legal limitations to the public use of these natural protected areas have been considered in the assessments made by the respondents. In conclusion, National Park managers, local entrepreneurs and institutional authorities (local, regional and national) are encouraged to better coordinate the resources of the protected natural area. The dynamization of tourist activities should be encouraged while respecting the biological value of the park, as has been done so far.
A Novel Solution for Severe Loss Prevention While Drilling Deep Wells
Authors: Elkatatny Salaheldin , Gamal Hany , Ahmed Abdulmalek , Sarmah Pranjal , Sangaru Shiv , Alohaly Maryam
Published: February 2020

The loss of circulation is a big problem in drilling operations. This problem is costly, time-consuming and may lead to a well control situation. Much research has investigated the effectiveness of using different chemicals as lost circulation material (LCM) to stop mud and cement slurry losses. However, there remain many limitations for using such LCM types, especially when it comes to field applications. This paper presents a new high strength lost circulation material (HSLCM) that could effectively be used for managing severe lost circulation cases. The HSLCM could easily be pumped into the thief zone where it forms a gel that solidifies after a setting time to provide sealing between the wellbore and the thief zone. With this technique, the material stops the circulation losses, and hence enhances the well bore stability by reducing the well bore stresses. The HSLCM has a high compressive strength and it has a high acid solubility of around 96%. Because the HSLCM has high tolerance towards contamination, it can be utilized with water-based mud or invert emulsion-drilling fluids, hence providing a wide window of applications with the drilling fluids. In this study, laboratory experiments were conducted to evaluate the rheology, thickening time, compressive strength, and acid solubility of the HSLCM. The results showed good performance for the HSLCM as LCM. In addition, a case field study is presented which shows a successful field treatment for severe losses.
Towards Strategic Business Performance of the Hospitality Sector: Nexus of ICT, E-Marketing and Organizational Readiness
Authors: Anser Muhammad Khalid , Yousaf Zahid , Usman Muhammad , Yousaf Seemab
Published: February 2020

Purpose: This study aims to present a strategic business performance (SBP) model for firms operating in the hospitality industry by providing them guidance on how to use information and communication technologies (ICTs) and e-marketing to attain strategic performance. This paper also explores the moderating role of organizational readiness in the relationship between ICT and e-marketing. Methodology: Data were collected from the top, middle, and operational managers in 4-star and 5-star hotels. To test the mediating role of e-marketing in the relationship between ICT and SBP, Preacher and Hayes’s (2008) approach was used along with the bootstrap method. Regression analysis was carried out to examine the moderating role of organizational readiness. Findings: ICTs provide opportunities to execute e-marketing activities for achieving competitiveness. The empirical findings proved that the use of ICTs provides a basis for establishing a successful e-marketing mechanism that helps hotels to achieve SBP. Furthermore, ICTs’ influence on e-marketing is strengthened by organizational readiness. Originality/value: This paper adds to previous literature on ICTs, SBP, and e-marketing by examining the role of e-marketing and ICTs in positively contributing to the hotels’ SBP, which is a broader measure of firms’ business performance, as compared to the traditional financial or operational measures of a firm’s performance. Since previous studies on the links between ICTs, e-marketing, and firm performance are based on conventional measures of firm performance, this study offers new insights into the nexus of ICTs, e-marketing, and firm performance.
Entrepreneurial Interest and Entrepreneurial Competence Among Spanish Youth: An Analysis with Artificial Neural Networks
Authors: Luis-Rico Isabel , Escolar-Llamazares María-Camino , De la Torre-Cruz Tamara , Jiménez Alfredo , Herrero Álvaro , Palmero-Cámara Carmen , Jiménez-Eguizábal Alfredo
Published: February 2020

Studies of the socio-economic function of entrepreneurship have emphasized the critical role that entrepreneurial competence and its implementation play at different stages of the education system. In this paper, as a research objective, we seek to determine the entrepreneurial interest of Spanish youth aged between 15 and 18 years of age enrolled in formal secondary education programs, an initial stage in the development of entrepreneurship. A previously validated ad hoc questionnaire is applied through simple random sampling to 1764 students at secondary school in Spain. A descriptive cross-sectional study is carried out. The analysis is done with Artificial Neural Networks (ANNs), a technique that reduces the high dimensionality of data through Cooperative Maximum Likelihood Hebbian Learning (CMLHL), applying neurocomputational methods to the educational sciences. We find as key results that Spanish youth express a medium level of interest in entrepreneurship. Analysis with ANNs shows that education in entrepreneurial competence is an influential aspect of interest in entrepreneurship. As a conclusion, our results suggest that educational and curricular reforms must be undertaken to promote the development of entrepreneurial competence at various stages of education in order to increase interest in entrepreneurship.
Provincial Quality of Economic Growth: Measurements and Influencing Factors for China
Authors: Ru Shaofeng , Liu Jiaqi , Wang Tonghui , Wei Guo
Published: February 2020

An extended total factor productivity method is developed for measuring the quality of economic growth (QEG). Specifically, criteria for judging the QEG are first elaborated using endogenous economic growth theory, and subsequently, an assessment index system for evaluating QEG is constructed. In this system, the production factors primarily include labor, material capital, education, medical and health, environmental resource, and social security, while the output indexes comprise the gross domestic product (GDP), employment rate, income gap, and environmental pollution. In the empirical study, the directional distance function and Global Malmquist-Luenberger index are implemented to examine the QEG in China from 2000 to 2016 by provinces, regions, and factor decomposition, respectively. The global scale technological change and global pure technological change are the main sources for improving the QEG. The results also reveal a considerable widespread inefficiency and uneven development of the QEG. In general, from the eastern to western to central regions, the QEG becomes noticeably lower; The investment level is not only a driving force for economic growth, but also a source for boosting the QEG. These findings will provide a reference for China in adjusting relevant investments, ameliorating environmental conditions, and accomplishing the unity of quantity, quality, and efficiency in economic growth.
The Trivariate Causality among Education, Health, and Economic Growth in Zimbabwe
Authors: Pasara Michael Takudzwa , Mutambirwa Tapiwa Kelvin , Diko Nolutho
Published: February 2020

This study investigated the causality among education, health, and economic growth in Zimbabwe. Causality effects are a thinly explored area in literature, with most studies focusing on bidirectional relationships. Granger causality tests were employed in a Vector autoregressive (VAR) model. Results showed that education Granger causes health improvements, with health improvements in turn fairly associating to Granger cause economic growth in Zimbabwe. Thus, the effect of education on economic growth is not direct, but works through improved health, pointing to the conclusion that health is a transmission mechanism through which education drives economic growth. No feedback effect was established from health to education and from economic growth to education and health. Thus, results suggest the need for a holistic policy approach which integrates education and health policies in a bid to drive economic growth, since education has no effect on economic growth in its own domain, but through health.
Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination
Authors: Liu Yan , Yang Linchuan , Chau Kwong Wing
Published: February 2020

Understanding the relationship between tourism demand and retail property prices is of great significance to tourist destinations, especially shopping destinations. The increase in tourism demand may alter the implicit prices of certain retail property characteristics (e.g., age and accessibility to transit). This study examines how tourism demand (measured by tourist volume) affects retail property prices in the tourist precinct of a shopping destination, namely Hong Kong. The implementation of the policy Individual Visit Scheme (IVS) in 2003 in Hong Kong has substantially increased tourist shoppers from Mainland China, and it is used as a quasi-natural experiment of the increased tourist volume. Spatial and non-spatial hedonic pricing models are developed based on the ground-floor retail property transaction data of Causeway Bay, Hong Kong before and after the IVS (1993–2011). The findings of this study are as follows. (1) Accessibility to transit has a larger positive price effect after the implementation of the IVS. (2) The implicit price of accessibility to accommodation facilities is not significantly altered by the implementation of the IVS. (3) Age has a larger negative price effect after the implementation of the IVS. The first two outcomes are related to the economic concerns of tourist shoppers, while the last can be explained by their hometown experience. Finally, practical implications are discussed.
Social Expectations and Market Changes in the Context of Developing the Industry 4.0 Concept
Authors: Saniuk Sebastian , Grabowska Sandra , Gajdzik Bożena
Published: February 2020

There are more and more talks in the community of scientists and business practitioners about new challenges for industry in connection with the fourth industrial revolution. Industry 4.0 is the result of the development of cyber-physical generation systems as part of the fourth industrial revolution. Industry 4.0 sets new areas of change in the sphere of production and management but also exerts an impact on various aspects of society’s life. It is a transformational challenge for enterprises of the present age. Industry 4.0 is present in economic studies at the macroeconomic level and business at the microeconomic level. Scientists discuss the essence of change, and specialized research centers and consulting companies carry out research on various aspects of this industrial revolution. The article presents the range of expectations and changes in society towards the development of the concept of Industry 4.0. The work was based on a literature study and direct research in the field of social change in the Industry 4.0 era. The aim of the article is to identify social expectations of development changes related to the implementation of the Industry 4.0 concept. The article devotes a lot of attention to customization because it is one of the keys of Industry 4.0, leading to a change of the paradigm from mass production to personalized production. This simple change will affect customers, producers, and employees. Based on the synthesis of literature and secondary research, authors identify opportunities and threats to the broadly understood society functioning in the Industry 4.0 environment. Social conditions were analyzed from the point of view of the consumer, producer, and employee. In the cited direct studies, the basic area of analysis was product personalization and pre-recognition of the opinions of potential consumers about customization in Industry 4.0. The limitation of the research area to the consumer segment resulted from the importance of product personalization in Industry 4.0 and its impact on producer behavior and effects for employees.
Adaptive Sustainable Reuse for Cultural Heritage: A Multiple Criteria Decision Aiding Approach Supporting Urban Development Processes
Authors: Della Spina Lucia
Published: February 2020

The European Union identifies the cultural heritage of cities as the main driver of development strategies. From this perspective, adaptive reuse can play a decisive role not only in terms of increasing the life cycle of the heritage but also as an urban strategy capable of generating new economic, cultural, and social values, thus supporting innovative dynamics of local development. The aim is to propose an integrated evaluation model based on the combined use of multi-criteria techniques, which helps to classify adaptive reuse strategies of unused cultural heritage assets and supports decision-makers in the implementation of development strategies in vulnerable contexts. The case study focuses on the potential reuse of some historical fortifications located along the coasts of the Strait of Messina in Southern Italy. The results obtained show that the proposed model can be a useful decision support tool, in contexts characterized by high complexity, able to guarantee the transparency of the decision-making process, and in which it is necessary to highlight the elements that influence the dynamics of the choice for the construction of shared development strategies.
Prediction of the Rate of Penetration while Drilling Horizontal Carbonate Reservoirs Using the Self-Adaptive Artificial Neural Networks Technique
Authors: Al-AbdulJabbar Ahmad , Elkatatny Salaheldin , Abdulhamid Mahmoud Ahmed , Moussa Tamer , Al-Shehri Dhafer , Abughaban Mahmoud , Al-Yami Abdullah
Published: February 2020

Rate of penetration (ROP) is one of the most important drilling parameters for optimizing the cost of drilling hydrocarbon wells. In this study, a new empirical correlation based on an optimized artificial neural network (ANN) model was developed to predict ROP alongside horizontal drilling of carbonate reservoirs as a function of drilling parameters, such as rotation speed, torque, and weight-on-bit, combined with conventional well logs, including gamma-ray, deep resistivity, and formation bulk density. The ANN model was trained using 3000 data points collected from Well-A and optimized using the self-adaptive differential evolution (SaDE) algorithm. The optimized ANN model predicted ROP for the training dataset with an average absolute percentage error (AAPE) of 5.12% and a correlation coefficient (R) of 0.960. A new empirical correlation for ROP was developed based on the weights and biases of the optimized ANN model. The developed correlation was tested on another dataset collected from Well-A, where it predicted ROP with AAPE and R values of 5.80% and 0.951, respectively. The developed correlation was then validated using unseen data collected from Well-B, where it predicted ROP with an AAPE of 5.29% and a high R of 0.956. The ANN-based correlation outperformed all previous correlations of ROP estimation that were developed based on linear regression, including a recent model developed by Osgouei that predicted the ROP for the validation data with a high AAPE of 14.60% and a low R of 0.629.
The Scent of Art. Perception, Evaluation, and Behaviour in a Museum in Response to Olfactory Marketing
Authors: Vega-Gómez Francisco I. , Miranda-Gonzalez Francisco J. , Pérez Mayo Jesús , González-López Óscar Rodrigo , Pascual-Nebreda Laura
Published: February 2020

Olfactory marketing has been increasing its presence in commercial environments in recent years thanks to its influence on perceptions, evaluations, and behaviour. However, the number of scientific studies on the issue is limited, and this is even more true for nonprofit environments. The aim of this experiment is to provide empirical answers about the influence of smell on the evaluation and behavior of the consumer of nonprofit services, specifically the museum consumer. For this purpose, an experiment was carried out at the González Santana Museum over four weeks in March and April 2017, during which three rooms in the museum were filled with scents considered to be congruent. The results obtained from the MANCOVA analysis demonstrate scent having a significant influence on perceptions and evaluations, as well as on the intentions to revisit the institution, and significant repercussions for management and researching can be derived from this.
Green Innovation Mode under Carbon Tax and Innovation Subsidy: An Evolutionary Game Analysis for Portfolio Policies
Authors: Zhang Shengzhong , Yu Yingmin , Zhu Qihong , Qiu Chun Martin , Tian Aixuan
Published: February 2020

Previous literature has shown that manufacturers’ choices between radical and incremental green innovation modes can greatly impact the tradeoff between industry growth and carbon emission reduction. Yet, how the government can motivate manufacturers to implement radical green innovations to reduce carbon emission is unclear. In this paper, the researchers construct an evolutionary game model to analyze the joint impacts of carbon tax and innovation subsidy on manufacturers’ choices of green innovation mode. We derive the conditions for manufacturers’ stable strategies. Based on those results, we find that four factors—carbon tax, innovation subsidy, consumer green preference, and manufacturers’ capabilities of absorbing and adopting new technologies—may facilitate the choice of radical innovation. Furthermore, we conduct numerical simulations to verify the theoretical results, and further illustrate how the synergy of carbon tax rate and subsidy level affects the evolution of the green innovation mode choices. Specifically, we demonstrate the superiority of portfolio policy in the early stage of green innovation over single policy. In contrast, in the later stage, it is carbon tax but not innovation subsidy that remains effective. We discuss the insights for the government to formulate appropriate environmental policies to effectively promote the adoption of green innovation and reduce carbon emission.
The Quality of Carrot after Field Biostimulant Application and after Storage
Authors: Pobereżny Jarosław , Szczepanek Małgorzata , Wszelaczyńska Elżbieta , Prus Piotr
Published: February 2020

The carrot (Daucus carota L.) is a staple vegetable in human nutrition in Europe. In recent years, the use of biostimulants in vegetable crops has become a way to affect the quantity and quality of yields. The aim of this study was to assess the effect of the type and methods of biostimulant (natural seaweed extract Kelpak and synthetic Asahi) application on the nitrates and nitrites content in carrot roots after harvest and storage. The study was based on a strict field experiment with carrot cv. ′Karotan′, conducted in Poland (53°13′N; 17°51′E) in three successive growing seasons and after six months of storage (RH 95%, and air temperature +1 °C). The biostimulants were applied during the growing season in a foliar form. The content of NO3ˉ and NO2ˉ in carrot after harvest depended on the dose and the date of biostimulant application. The single application of biostimulant Kelpak as well as two times of Asahi had no effect on the nitrate and nitrite content, while the application of Kelpak in a total dose of 6 or 7 dm3 ha−1 increased them. The maximum intake of nitrates and nitrites following the harvest and storage was, respectively, 7.1, 2.3% and 6.7, 2.1% of the ADI.
Total Factor Energy Efficiency, Carbon Emission Efficiency, and Technology Gap: Evidence from Sub-Industries of Anhui Province in China
Authors: Chen Ya , Xu Wei , Zhou Qian , Zhou Zhixiang
Published: February 2020

The phenomena of “large energy consumption, high carbon emission, and serious environmental pollution” are against the goals of “low energy consumption, low emissions” in China’s industrial sector. The key to solving the problem lies in improving total factor energy efficiency (TFEE) and carbon emission efficiency (TFCE). Considering the heterogeneity of different sub-industries, this paper proposes a three-stage global meta-frontier slacks-based measure (GMSBM) method for measuring TFEE and TFCE, as well as the technology gap by combining meta-frontier technology with slacks-based measure (SBM) using data envelopment analysis (DEA). DEA can effectively avoid the situation where the technology gap ratio (TGR) is larger than unity. This paper uses the three-stage method to empirically analyze TFEE and TFCE of Anhui’s 38 industrial sub-industries in China from 2012 to 2016. The main findings are as follows: (1) Anhui’s industrial sector has low TFEE and TFCE, which has great potential for improvement. (2) TFEE and TFCE of light industry are lower than those of heavy industry under group-frontier, while they are higher than those of heavy industry under meta-frontier. There is a big gap in TFEE and TFCE among sub-industries of light industry. Narrowing the gap among different sub-industries of light industry is conducive to the overall improvement in TFEE and TFCE. (3) The TGR of light industry is significantly higher than that of heavy industry, indicating that there are sub-industries with the most advanced energy use and carbon emission technologies in light industry. And there is a bigger carbon-emitting technology gap in heavy industry, so it needs to encourage technology spillover from light industry to heavy industry. (4) The total performance loss of industrial sub-industries in Anhui mainly comes from management inefficiency, so it is necessary to improve management and operational ability. Based on the findings, some policy implications are proposed.
Integrating GIS Accessibility and Location-Allocation Models with Multicriteria Decision Analysis for Evaluating Quality of Life in Buraidah City, KSA
Authors: Abd El Karim Ashraf , Awawdeh Mohsen M.
Published: February 2020

The present study was aimed at evaluating the quality of life in the districts of Buraidah city in the Kingdom of Saudi Arabia as part of the Kingdom’s Vision 2030, based on provided services in terms of distance and time parameters. This was achieved by integrating Geographic Information System (GIS)-based network analysis and multicriteria decision analysis (MCDA) techniques. The service area analysis technique of the GIS Network Analysis tools was applied to the available services in the districts of Buraidah city. Twelve services were investigated with regard to their coverage within 5, 10, and 15 minutes of travel time: universities, high schools, middle schools, elementary schools, hospitals, health centers, ambulance facilities, government services, religious services, security services, sports services, and recreational services. GIS-based multicriteria decision analysis (GIS-MCDA) was implemented in the study to obtain a spatial suitability map of the standard of living quality in the various districts in Buraidah city. The analytic hierarchy process (AHP) technique was used to determine the criteria weights. The location-allocation model was used in the study as well for suggestions of new service locations to improve the spatial distribution of services and enhance the quality of life in the districts. The results of the final suitability map to determine districts with the best standard of living according to the provided services show that the living standards in the city are very good, with very high and high levels in around 51 districts, representing 72.9% (449,235 people), and about 19 districts with moderate, low, and very low levels, representing 27.1% (56,765 people). Narrowing the quality of life gap and improving spatial planning requires correcting the current deficiency in services, which is estimated in this study to be 16 services: one high school, one elementary school, one middle school, two universities, one sports service, two security services, two courts, two hospitals, one health center, one park, and two ambulance facilities.
Smart Home Innovative Heat Test Analysis for Heat Storage and Conductivity Coefficients
Authors: Erten Duygu , Şen Zekâi
Published: February 2020

This paper aims at presenting an innovative methodological approach to identify the heat storage and heat conductivity coefficients of a smart house. For this purpose, an experimental room is selected and heated to a high degree and then left for cooling, during which the reduction in the room temperature is measured with time. The time–temperature record provides the basis for determining the heat storage and heat conductivity coefficients through an objective standard curve matching procedure as the main context. These coefficients help to calculate the heat performance of a single room and, accordingly, the whole building, depending on its surrounding environment, construction material, volume and meteorological conditions. The proposed methodology depends on the one-dimensional heat flow event assuming that the construction material is homogeneous and isotropic and that the heat scatters uniformly in the room. The novelty in the paper is the standard curve for time–temperature decrease record evaluation, which can be used in a smart building planning procedure for decisions on heat storage and transfer.