bluuebunny/crossref_metadata_2025 huggingface dataset:
Non-Contact Paper Thickness and Quality Monitoring Based on Mid-Infrared Optical Coherence Tomography and THz Time Domain Spectroscopy
Authors: Hansen Rasmus Eilkær , Bæk Thorsten , Lange Simon Lehnskov , Israelsen Niels Møller , Mäntylä Markku , Bang Ole , Petersen Christian Rosenberg
Published: February 2022

In industrial paper production, online monitoring of a range of quality parameters is essential for ensuring that the performance and appearance of the final product is suitable for a given application. In this article, two optical sensing techniques are investigated for non-destructive, non-contact characterization of paper thickness, surface roughness, and production defects. The first technique is optical coherence tomography based on a mid-infrared supercontinuum laser, which can cover thicknesses from ~20–90 μm and provide information about the surface finish. Detection of subsurface voids, cuts, and oil contamination was also demonstrated. The second technique is terahertz time domain spectroscopy, which is used to measure paper thicknesses of up to 443 μm. A proof-of-concept thickness measurement in freely suspended paper was also demonstrated. These demonstrations highlight the added functionality and potential of tomographic optical sensing methods towards industrial non-contact quality monitoring.
Impact Position Estimation for Baseball Batting with a Force-Irrelevant Vibration Feature
Authors: Chen Wei-Han , Feng Yang-Chih , Yeh Ming-Chia , Ma Hsi-Pin , Liu Chiang , Wu Cheng-Wen
Published: February 2022

In this work we propose a novel method for impact position estimation during baseball batting, which is independent of impact intensity, i.e., force-irrelevant. In our experiments, we mount a piezoelectric vibration sensor on the knob of a wooden bat to record: (1) 3600 vibration signals (waveforms) from ball–bat impacts in the static experiment—30 impacts from each of 40 positions (distributed 1–40 cm from the end of the barrel) and 3 intensities (drop heights at 75, 100, and 125 cm, resp.), and (2) 45 vibration signals from actual battings by three baseball players in the dynamic experiment. The results show that the peak amplitude of the signal in the time domain, and the peaks of the first, second, and third eigenfrequencies (EFs) of the bat all increase with the impact intensity. However, the ratios of peaks at these three EFs (1st/2nd, 2nd/3rd, and 1st/3rd) hardly change with the impact intensity, and the observation is consistent for both the static and dynamic experiments across all impact positions. In conclusion, we have observed that the ratios of peaks at the first three EFs are a force-irrelevant feature, which can be used to estimate the impact position in baseball batting.
Modified Artificial Potential Field for the Path Planning of Aircraft Swarms in Three-Dimensional Environments
Authors: Souza Rafael Monteiro Jorge Alves , Lima Gabriela Vieira , Morais Aniel Silva , Oliveira-Lopes Luís Cláudio , Ramos Daniel Costa , Tofoli Fernando Lessa
Published: February 2022

Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.
Effects of Accelerated Aging on the Performance of Low-Cost Ultrasonic Sensors Used for Public Lighting and Mobility Management in Smart Cities
Authors: Quattrocchi Antonino , Alizzio Damiano , Martella Francesco , Lukaj Valeria , Villari Massimo , Montanini Roberto
Published: February 2022

In the field of Smart Cities, especially for Smart Street Lighting and Smart Mobility, the use of low-cost devices is considered an advantageous solution due to their easy availability, cost reduction and, consequently, technological and methodological development. However, this type of transducers shows many critical issues, e.g., in metrological and reliability terms, which can significantly compromise their functionality and safety. Such issue has a large relevance when temperature and humidity are cause of a rapid aging of sensors. The aim of this work is to evaluate the effects of accelerated aging in extreme climatic conditions on the performance of a control system, based on a low-cost ultrasonic distance sensor, for public-lighting management in Smart Cities. The presented architecture allows for the detection of vehicles, pedestrians and small animals and contains a dedicated algorithm, developed in an Edge/Cloud environment, that is able to display the acquired measurements to users connected on the web. The obtained results highlight that the effect of accelerated aging is to significantly reduce the linearity of the calibration curve of the sensor and, moreover, to exponentially increase the number of outliers and invalid measurements. These limitations can be overcome by developing an appropriate self-calibration strategy.
A Method for Pipeline Leak Detection Based on Acoustic Imaging and Deep Learning
Authors: Ahmad Sajjad , Ahmad Zahoor , Kim Cheol-Hong , Kim Jong-Myon
Published: February 2022

This paper proposes a reliable technique for pipeline leak detection using acoustic emission signals. The acoustic emission signal of a pipeline contains leak-related information. However, the noise in the signal often obscures the leak-related information, making traditional acoustic emission features, such as count and peaks, less effective. To obtain leak-related features, first, acoustic images were obtained from the time series acoustic emission signals using continuous wavelet transform. The acoustic images (AE images) were the wavelet scalograms that represent the time–frequency scales of the acoustic emission signal in the form of an image. The acoustic images carried enough information about the leak, as the leak-related information had a high-energy representation in the scalogram compared to the noise. To extract leak-related discriminant features from the acoustic images, they were provided as input into the convolutional autoencoder and convolutional neural network. The convolutional autoencoder extracts global features, while the convolutional neural network extracts local features. The local features represent changes in the energy at a finer level, whereas the global features are the overall characteristics of the acoustic signal in the acoustic image. The global and local features were merged into a single feature vector. To identify the pipeline leak state, the feature vector was fed into a shallow artificial neural network. The proposed method was validated by utilizing a data set obtained from the industrial pipeline testbed. The proposed algorithm yielded a high classification accuracy in detecting leaks under different leak sizes and fluid pressures.
An Impedance Readout IC with Ratio-Based Measurement Techniques for Electrical Impedance Spectroscopy
Authors: Cheon Song-I , Kweon Soon-Jae , Kim Youngin , Koo Jimin , Ha Sohmyung , Je Minkyu
Published: February 2022

This paper presents an error-tolerant and power-efficient impedance measurement scheme for bioimpedance acquisition. The proposed architecture measures the magnitude and the real part of the target complex impedance, unlike other impedance measurement architectures measuring either the real/imaginary components or the magnitude and phase. The phase information of the target impedance is obtained by using the ratio between the magnitude and the real components. This can allow for avoiding direct phase measurements, which require fast, power-hungry circuit blocks. A reference resistor is connected in series with the target impedance to compensate for the errors caused by the delay in the sinusoidal signal generator and the amplifier at the front. Moreover, an additional magnitude measurement path is connected to the reference resistor to cancel out the nonlinearity of the proposed system and enhance the settling speed of the low-pass filter by a ratio-based detection. Thanks to this ratio-based detection, the accuracy is enhanced by 30%, and the settling time is improved by 87.7% compared to the conventional single-path detection. The proposed integrated circuit consumes only 513 μW for a wide frequency range of 10 Hz to 1 MHz, with the maximum magnitude and phase errors of 0.3% and 2.1°, respectively.
A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
Authors: Han Bing , Ran Feng , Li Jiao , Yan Limin , Shen Huaming , Li Ang
Published: February 2022

With the various applications of the Internet of Things, research into wireless sensor networks (WSNs) has become increasingly important. However, because of their limited energy, the communication abilities of the wireless nodes distributed in the WSN are limited. The main task of WSNs is to collect more data from targets in an energy-efficient way, because the battery replacement of large amounts of nodes is a labor-consuming work. Although the life of WSNs can be prolonged through energy-harvesting (EH) technology, it is necessary to design an energy-efficient routing protocol for the energy harvesting-based wireless sensor networks (EH-WSNs) as the nodes would be unavailable in the energy harvesting phase. A certain number of unavailable nodes would cause a coverage hole, thereby affecting the WSN’s monitoring function of the target environment. In this paper, an adaptive hierarchical-clustering-based routing protocol for EH-WSNs (HCEH-UC) is proposed to achieve uninterrupted coverage of the target region through the distributed adjustment of the data transmission. Firstly, a hierarchical-clustering-based routing protocol is proposed to balance the energy consumption of nodes. Then, a distributed alternation of working modes is proposed to adaptively control the number of nodes in the energy-harvesting mode, which could lead to uninterrupted target coverage. The simulation experimental results verify that the proposed HCEH-UC protocol can prolong the maximal lifetime coverage of WSNs compared with the conventional routing protocol and achieve uninterrupted target coverage using energy-harvesting technology.
Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments
Authors: Tagarakis Aristotelis C. , Filippou Evangelia , Kalaitzidis Damianos , Benos Lefteris , Busato Patrizia , Bochtis Dionysis
Published: February 2022

During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.
Vehicle Load Estimation Using the Reaction Force of a Vertical Displacement Sensor Based on Fiber Bragg Grating
Authors: Kim Sung-Wan , Yun Da-Woon , Park Dong-Uk , Chang Sung-Jin , Park Jae-Bong
Published: February 2022

Accurately calculating the vehicle load acting on a bridge at any one time is crucial to determining the integrity and safety of the bridge. To ensure this integrity and safety, information on the types, characteristics, and load of vehicles that regularly cross the bridge is very important in terms of its structural adequacy and maintenance. In this study, the vehicle load that a bridge will be subjected to was estimated using the reaction force response at the support. To estimate this response to the reaction force, a vertical displacement sensor, developed based on Fiber Bragg Grating (FBG), was applied to the Eradi Quake System (EQS), a commercially available bridge bearing. This vertical displacement sensor can measure the vertical load and has the advantage of being easy to attach and detach. To verify the performance and accuracy of this sensor, this study conducted numerical analysis and vehicle loading tests. It found that the vehicle load can be estimated from the reaction force response, as measured by the vertical displacement sensor on the bridge.
A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks
Authors: Li Diyuan , Zhao Junjie , Liu Zida
Published: February 2022

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.
A Novel Approach for Dynamic (4d) Multi-View Stereo System Camera Network Design
Authors: Osiński Piotr , Markiewicz Jakub , Nowisz Jarosław , Remiszewski Michał , Rasiński Albert , Sitnik Robert
Published: February 2022

Image network design is a critical factor in image-based 3D shape reconstruction and data processing (especially in the application of combined SfM/MVS methods). This paper aims to present a new approach to designing and planning multi-view imaging networks for dynamic 3D scene reconstruction without preliminary information about object geometry or location. The only constraints are the size of defined measurement volume, the required resolution, and the accuracy of geometric reconstruction. The proposed automatic camera network design method is based on the Monte Carlo algorithm and a set of prediction functions (considering accuracy, density, and completeness of shape reconstruction). This is used to determine the camera positions and orientations and makes it possible to achieve the required completeness of shape, accuracy, and resolution of the final 3D reconstruction. To assess the accuracy and efficiency of the proposed method, tests were carried out on synthetic and real data. For a set of 20 virtual images of rendered spheres, completeness of shape reconstruction was up by 92.3% while maintaining accuracy and resolution at the user-specified level. In the case of the real data, the differences between predictions and evaluations for average density were in the range between 33.8% to 45.0%.
The Prototype Monitoring System for Pollution Sensing and Online Visualization with the Use of a UAV and a WebRTC-Based Platform
Authors: Chodorek Agnieszka , Chodorek Robert Ryszard , Yastrebov Alexander
Published: February 2022

Nowadays, we observe a great interest in air pollution, including exhaust fumes. This interest is manifested in both the development of technologies enabling the limiting of the emission of harmful gases and the development of measures to detect excessive emissions. The latter includes IoT systems, the spread of which has become possible thanks to the use of low-cost sensors. This paper presents the development and field testing of a prototype pollution monitoring system, allowing for both online and off-line analyses of environmental parameters. The system was built on a UAV and WebRTC-based platform, which was the subject of our previous paper. The platform was retrofitted with a set of low-cost environmental sensors, including a gas sensor able to measure the concentration of exhaust fumes. Data coming from sensors, video metadata captured from 4K camera, and spatiotemporal metadata are put in one situational context, which is transmitted to the ground. Data and metadata are received by the ground station, processed (if needed), and visualized on a dashboard retrieving situational context. Field studies carried out in a parking lot show that our system provides the monitoring operator with sufficient situational awareness to easily detect exhaust emissions online, and delivers enough information to enable easy detection during offline analyses as well.
Optical Writing and Electro-Optic Imaging of Reversible Space Charges in Semi-Insulating CdTe Diodes
Authors: Cola Adriano , Dominici Lorenzo , Valletta Antonio
Published: February 2022

Deep levels control the space charge in electrically compensated semi-insulating materials. They limit the performance of radiation detectors but their interaction with free carriers can be favorably exploited in these devices to manipulate the spatial distribution of the electric field by optical beams. By using semi-insulating CdTe diodes as a case study, our results show that optical doping functionalities are achieved. As such, a highly stable, flux-dependent, reversible and spatially localized space charge is induced by a line-shaped optical beam focused on the cathode contact area. Real-time non-invasive imaging of the electric field is obtained through the Pockels effect. A simple and convenient method to retrieve the two-dimensional electric field components is presented. Numerical simulations involving just one deep level responsible for the electrical compensation confirm the experimental findings and help to identify the underlying mechanism and critical parameters enabling the optical writing functionalities.
A Hybrid Intelligent Framework to Combat Sophisticated Threats in Secure Industries
Authors: Javeed Danish , Gao Tianhan , Khan Muhammad Taimoor , Shoukat Duaa
Published: February 2022

With the new advancements in Internet of Things (IoT) and its applications in different sectors, such as the industrial sector, by connecting billions of devices and instruments, IoT has evolved as a new paradigm known as the Industrial Internet of Things (IIoT). Nonetheless, its benefits and applications have been approved in different areas, but there are possibilities for various cyberattacks because of its extensive connectivity and diverse nature. Such attacks result in financial loss and data breaches, which urge a consequential need to secure IIoT infrastructure. To combat the threats in the IIoT environment, we proposed a deep-learning SDN-enabled intelligent framework. A hybrid classifier is used for threat detection purposes, i.e., Cu-LSTMGRU + Cu-BLSTM. The proposed model achieved a better detection accuracy with low false-positive rate. We have conducted 10-fold cross-validation to show the unbiasdness of the results. The proposed scheme results are compared with Cu-DNNLSTM and Cu-DNNGRU classifiers, which were tested and trained on the same dataset. We have further compared the proposed model with other existing standard classifiers for a thorough performance evaluation. Results achieved by our proposed scheme are impressive with respect to speed efficiency, F1 score, accuracy, precision, and other evaluation metrics.
Food Safety Assessment: Overview of Metrological Issues and Regulatory Aspects in the European Union
Authors: Sorbo Angela , Pucci Emilia , Nobili Chiara , Taglieri Isabella , Passeri Daniele , Zoani Claudia
Published: February 2022

The safety of the food we consume has a direct impact on individual and population health and affects the economic growth of the region where food safety is practised and enhanced. The central goal of the European Commission’s Food Safety policy is to ensure a high level of protection of human health covering the whole supply chain. In recent years, great attention has been paid to food testing and the application of metrological tools to support food safety. The global food market and national and international food safety regulations have created a huge demand for the measurement traceability and comparability of analytical results that are independent of time or space boundaries. This review provides an overview of the European food safety policy and regulation, with a focus on the measurement-related elements of the European Union (EU) food law. It also highlights how the application of analytical techniques, with particular reference to separation approaches, and metrological tools can ensure the control of certain contaminants that nowadays represent the main challenges for food safety (e.g., mycotoxins, nanoparticles, emerging and process contaminants). METROFOOD-RI-Infrastructure for promoting metrology in food and nutrition is therefore described in this context. This European research infrastructure has been developed and is being implemented in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to support metrology in food and nutrition and establish a strategy allowing reliable and comparable analytical measurements in food across the entire process line, from primary producers to consumers, and making data findable, accessible, interoperable, and reusable (FAIR).
Using ARC-D Toolkit for Measuring Community Resilience to Disasters
Authors: Islam Muhammad Awfa , Chisty Musabber Ali , Fuad Abdullah , Rahman Md. Mostafizur , Muhtasim Maliha , Dola Syeda Erena Alam , Biva Fariha Jahin , Khan Nesar Ahmed
Published: February 2022

Increased levels of resilience will reduce the negative consequences of any disaster and develop the capacities of communities to mitigate future disasters. The main objective of this study was to measure the level of resilience of two different communities in two different study areas and compare the resilience levels in terms of a flood. The study used the Analysis of Resilience of Communities to Disasters (ARC-D) toolkit. The study was conducted in two different areas to compare the level of community resilience. Both quantitative and qualitative methods were used in the study. A structured questionnaire was developed by using the toolkit. Results of the study indicated that communities in study area 1 were more resilient than communities in study area 2. Communities from study area 1 were more aware of their risk(s) and problem(s) and ensured proper strategies and actions to solve problems. On the other hand, communities in study area 2 were less aware of their risk(s). The strategies and actions implemented by the communities of study area 1 focused on the short-term problem(s), which reduced their level of resilience. Measuring resilience is very important in terms of developing disaster risk reduction (DRR) plans and incorporating DRR in the development process in lower-income countries and developing countries. As data scarcity is one of the major issues in developing countries, introducing a community resilience assessment mechanism can be a great help to reduce gaps in the planning and implementation process.
How Higher Education Promotes the Integration of Sustainable Development Goals—An Experience in the Postgraduate Curricula
Authors: López Belén
Published: February 2022

The Coronavirus (COVID-19) pandemic has created big challenges and opportunities in Higher Education (HE). In this situation, several universities worldwide have responded with digital methods and hybrid classes in a short period of time. The aim of this paper is to show how the universities have adapted teaching methods to digital platforms in the academic year 2020–2021. This case study is based on the experience of 37 postgraduate communication students in the course Business and Communication from a Communication Programme. The objective of this course was to promote the 17 sustainable development goals (SDGs) in business following the Principles for Responsible Management Education (PRME) of the United Nations. To do this, the students provided different solutions related to the SDGs and developed a communication strategy to inform and engage the stakeholders in the companies analyzed in a hybrid class. The results show that using this methodology and working in digital platforms, students have learned the importance of SDGs through implementing specific solutions linked to the corporate social responsibility (CSR) strategy. Based on this analysis, they also developed a communication strategy showing how companies can improve society with specific actions through the lens of the SDG perspective.
Assessment of the Effects of Heavy Metals in Soils after Removal by Nanoscale Zero-Valent Iron with Three Methods
Authors: Zhang Tianen , Xia Bing , Lu Yuanyuan , Zhang Xiaoyu , Chen Hongfeng , Ying Rongrong , Jin Shu
Published: February 2022

Nanoscale zero-valent iron (nZVI) has been broadly applied in the remediation of heavy metals pollution. In this research, the toxicity characteristic leaching procedure (TCLP), the in vitro gastrointestinal (IVG) method, and the diffusive gradients in thin-films (DGT) technique were used to evaluate the effects of heavy metals in soil with remediation by nZVI. The results indicate that, compared with the dose of 0.5 g·L−1, the nZVI in the dose of 1.0 g·L−1 can remove the heavy metals in the soil. The leaching toxicities of the heavy metals (Cr, Cu, Zn, Pb) showed apparent decreases after the remediation by nZVI. In the gastric phase, the highest bioaccessibility values of the Cr, Cu, Zn, Pb were decreased by 27.2, 31.7, 11.7, and 20.1%, respectively. Moreover, in the gastric phase, the highest bioaccessibility values of the Cr, Cu, Zn, Pb were decreased by 5.5, 1.29, 8.0, and 7.3%, respectively. The availabilities of the heavy metals were also reduced. The above results show that the nZVI effectively reduced the heavy metal pollution in the soil.
Fostering Responsible Innovation through Stakeholder Engagement: Case Study of North Carolina Sweetpotato Stakeholders
Authors: Grieger Khara , Zarate Sebastian , Barnhill-Dilling Sarah Kathleen , Hunt Shelly , Jones Daniela , Kuzma Jennifer
Published: February 2022

Stakeholder and community engagement are critical for the successful development of new technologies that aim to be integrated into sustainable agriculture systems. This study reports on an approach used to engage stakeholders within the sweetpotato community in North Carolina to understand their preferences, needs, and concerns as they relate to a new sensing and diagnostic platform. This work also demonstrates an example of real-time technology assessment that also fosters responsible innovation through inclusivity and responsiveness. Through the conduction of 29 interviews with sweetpotato stakeholders in North Carolina, we found that participants found the most value in detecting external sweetpotato characteristics, as well as the ability to use or connect to a smartphone that can be used in field. They also found value in including environmental parameters and having a Spanish language module. Most participants indicated that they were comfortable with sharing data as long as it benefited the greater North Carolina sweetpotato industry, and were concerned with sharing these data with “outside” competitors. We also observed differences and variations between stakeholder groups. Overall, this work demonstrates a relatively simple, low-cost approach to eliciting stakeholder needs within a local agricultural context to improve sustainability, an approach that could be leveraged and transferred to other local agrifood systems.
An Interdisciplinary Systematic Review on Sustainability in Tunneling—Bibliometrics, Challenges, and Solutions
Authors: Huymajer Marco , Woegerbauer Matthias , Winkler Leopold , Mazak-Huemer Alexandra , Biedermann Hubert
Published: February 2022

Sustainability is defined by current research as an interdisciplinary field comprising environmental, social, and economic aspects. This paper presents a systematic literature review following the PRISMA guidelines investigating how authors currently view sustainability issues in the specific context of tunneling. Thereby, we introduce a new methodology for reviewing sustainability aspects in an interdisciplinary way, where key bibliographic metrics are derived from the metadata of the reviewed literature. Regarding the content of the articles, we cluster sustainability aspects into specific topics and discuss challenges and solutions. In addition, we examine the role of digital technologies applied in sustainable tunneling. Our results show that there is a lack of interdisciplinary studies and that the current research does not represent all three dimensions of sustainability equally. The current research focuses on assessing the status quo instead of presenting specific solutions. Finally, we see great potential to further leverage digital tools to enable sustainable tunneling.
Assessing Urban Vulnerability to Flooding: A Framework to Measure Resilience Using Remote Sensing Approaches
Authors: Cerbaro Mercio , Morse Stephen , Murphy Richard , Middlemiss Sarah , Michelakis Dimitrios
Published: February 2022

Assessing and measuring urban vulnerability resilience is a challenging task if the right type of information is not readily available. In this context, remote sensing and Earth Observation (EO) approaches can help to monitor damages and local conditions before and after extreme weather events, such as flooding. Recently, the increasing availability of Google Street View (GSV) coverage offers additional potential ways to assess the vulnerability and resilience to such events. GSV is available at no cost, is easy to use, and is available for an increasing number of locations. This exploratory research focuses on the use of GSV and EO data to assess exposure, sensitivity, and adaptation to flooding in urban areas in the cities of Belem and Rio Branco in the Amazon region of Brazil. We present a Visual Indicator Framework for Resilience (VIFOR) to measure 45 indicators for these characteristics in 1 km2 sample areas in poor and richer districts in the two cities. The aim was to assess critically the extent to which GSV-derived information could be reliable in measuring the proposed indicators and how this new methodology could be used to measure vulnerability and resilience where official census data and statistics are not readily available. Our results show that variation in vulnerability and resilience between the rich and poor areas in both cities could be demonstrated through calibration of the chosen indicators using GSV-derived data, suggesting that this is a useful, complementary and cost-effective addition to census data and/or recent high resolution EO data. Furthermore, the GSV-linked approach used here may assist users who lack the technical skills to process raw EO data into usable information. The ready availability of insights on the vulnerability and resilience of diverse urban areas by straightforward remote sensing methods such as those developed here with GSV can provide valuable evidence for decisions on critical infrastructure investments in areas with low capacity to cope with flooding.
Crowd Evacuation in Hajj Stoning Area: Planning through Modeling and Simulation
Authors: Kurdi Heba , Alzuhair Amal , Alotaibi Dana , Alsweed Hesah , Almoqayyad Noor , Albaqami Razan , Althnian Alhanoof , Alnabhan Najla , Islam A. B. M. Alim Al
Published: February 2022

Pilgrimage is one of the largest mass gatherings, where millions of Muslims gather annually from all over the world to perform Hajj. The stoning ritual during Hajj has been historically vulnerable to serious disasters that often cause severe impacts ranging from injuries to death tolls. In efforts to minimize the number and extent of the disasters, the stoning area has been expanded recently. However, no research has been carried out to study the evacuation effectiveness of the current exit placements in the area, which lies at the heart of effective minimization of the number and extent of the disasters. Therefore, this paper presents an in-depth study on emergency evacuation planning for the extended stoning area. It presents a simulation model of the expanded stoning area with the current exit placement. In addition, we suggested and examined four different exit placements considering evacuation scenarios in case of no hazard as well as two realistic hazard scenarios covering fire and bomb hazards. The simulation studied three stoning phases, beginning of stoning, during the peak hour of stoning, and ending of stoning at three scales of population sizes. The performance was measured in the light of evacuation time, percentage of evacuees, and percentage of crowd at each exit. The experimental results revealed that the current exits are not optimally positioned, and evacuation can be significantly improved through introducing a few more exits, or even through changing positions of the current ones.
A Capacity Cost-Sharing Contract for a Two-Stage Supply Chain with a Risk-Averse Supplier under a Bargaining Power
Authors: Koo Pyung-Hoi
Published: February 2022

This study considers a two-stage supply chain (SC) consisting of a single supplier and a manufacturer. When the manufacturer introduces a new product to the market, both the manufacturer and supplier should install production capacity in advance. Since capacity building often takes a long time, the demand is uncertain at the time of capacity decision making. The supplier often makes a conservative decision on capacity building to avoid possible capital risks due to excess capacity, which leads to the so-called double marginalization problem. Various risk-sharing supply contracts have recently been studied in academia to overcome the double marginalization problem. However, most existing studies ignore the bargaining power of each SC member and capacity investment. This study aims to fill the research gap by including capacity investment and bargaining power in the supply contract process. A capacity cost-sharing (CCS) contract is introduced in which the manufacturer shares the supplier’s capacity investment risk. We investigate how to set the contract parameters in the CCS contract to coordinate the supply chain. It is found that the wholesale price and manufacturer’s CCS ratio are negatively proportional to each other, and the manufacturer’s expected profit increases as the CCS ratio (wholesale price) increases (decreases) in the coordinated CCS contract. We show that there exists a CCS contract leading to a coordinated supply chain for a specific range of bargaining power. We also present a new CCS contract for a supply chain with a risk-averse supplier. A numerical illustration is provided to clarify how the contract parameters are determined and to examine the effect of the contract parameters on SC performance. Managerial implications and possible future work are discussed.
Potential of NTFP Based Bioeconomy in Livelihood Security and Income Inequality Mitigation in Kashmir Himalayas
Authors: Peerzada Ishtiyak Ahmad , Islam Mohammad A. , Chamberlain James , Dhyani Shalini , Reddy Mohan , Saha Somidh
Published: February 2022

The contribution of non timber forest products (NTFPs) has been acknowledged globally for their role in conservation, income generation, livelihood improvement and rural development. The potential of a NTFP-based bioeconomy has given a new dimension to the forest sector, and NTFPs are now considered favourably by the resource rich developing economies. The actual contribution of NTFPs has never been adequately estimated due to lack of sufficient baseline information on extraction, consumption patterns and traded quantities in Kashmir, India. Complicated management frameworks and fragmented value chains have eclipsed their diverse social life cycle in Kashmir. Therefore the present study investigates the bioeconomic transformation, livelihood contribution, income inequality mitigation and determinant socioeconomic factors of NTFP extraction in the Kashmir Himalayas. A multistage random sampling technique was employed to collect data through participatory household-based surveys from different villages. Data were collected through structured in-depth interviews, non-participant observation and focussed group discussions. Descriptive and analytical statistics were used for data analysis. The Lorenz curve and Gini index were used to evaluate the influence of household NTFP incomes on income inequality mitigation, and econometric models were developed to identify key factors that influence the level of household income from NTFPs to determine their potential for supporting livelihood security and bioeconomy in the region.
The Potential Determinants for Smartphone Recycling Behaviour Sustainability in UAE
Authors: Ben Yahya Taher , Jamal Noriza Mohd , Sundarakani Balan , Omain Siti Zaleha
Published: February 2022

Repeated actions and behaviours are characteristic of people’s daily lives. However, there is a dilemma when this repeated action is associated with mobile phone recycling since convincing mobile users to recycle sustainably is challenging. This study analysed the four factors (i.e., actual knowledge, salience, environmental constraints, and habits) adopted from the Integrated Behavioural Model (IBM) theory and examined their impact on behavioural sustainability. A partial least squares structural equation modelling (PLS-SEM) approach was applied to evaluate 601 responses from a self-administered online survey collected from mobile user participants based in the United Arab Emirates (UAE). The survey findings indicated that habit has the strongest and statistically significant positive influence on behaviour; followed by knowledge and skills. Additionally, the salience of behaviour has a considerably negative influence on behaviour sustainability unaffected by environmental constraints. This study serves as a springboard for future research examining the IBM model to understand recycling behaviour in general and smartphone recycling sustainability in particular. Additionally, this research can assist smartphone manufacturers in understanding the factors that will maintain the recycling behaviour continuity, increasing the number of returned devices.
Application of openBIM for the Management of Existing Railway Infrastructure: Case Study of the Cancello–Benevento Railway Line
Authors: Ciccone Angelo , Stasio Sabrina , Asprone Domenico , Salzano Antonio , Nicolella Maurizio
Published: February 2022

In the field of infrastructure, the development and application of the openBIM (open Building Information Modeling) approach and related standards (principally Industry Foundation Classes) remain limited with regard to processes in O&M (Operation and Maintenance) phases, as well as the broader context of AM (Asset Management). This article deals with the activities carried out as part of a pilot project based on the need to manage the operation and assess the condition and value of existing infrastructure along the Cancello–Benevento railway line. The principal goal was to systematize information by digitalizing the infrastructure, in order to enable the assessment of possible performance gaps (compared to national railway standards) in the event of integration within the national infrastructure. In compliance with the project requirements, a digitalization strategy was designed for the definition of surveying activities and the implementation of openBIM systems for the development of an object library and a federated digital model, structured within the collaborative platform that was used, and allowing management, maintenance, and subsequent financial evaluation in the broader context of asset management. The project involved the collaboration of railway operators, a university, and a software company that implemented innovative concepts concerning IFC (specifically, IFC4x2 was used) through the development of dedicated software solutions. The digital solution we proposed enabled the use of digital models as access keys to survey and maintenance information (ERP platforms used by the railway operators) that was available in real time. This project was nominated at the buildingSMART awards 2021 and was one of three finalists in the “Asset Management Using openBIM” category.
Evaluating the Hydraulic Conductivity of Dense Nonaqueous Phase Liquid in a Single Fracture of Rock-like Material
Authors: Weng Meng-Chia , Lin Chiou-Liang , Jeng Fu-Shu , Ou Hao-Chih
Published: February 2022

To investigate the seepage characteristics of dense nonaqueous phase liquids (DNAPLs) in rock fractures, two types of NAPLs (paint and creosote) were used in triaxial permeability tests conducted on single-fracture samples. The hydraulic conductivity of rock fractures with different apertures, confining pressures, and fluid properties was measured, and the influence of various physical factors on transmissivity was explored. The results demonstrated the following: (1) Fracture aperture and fluid viscosity are the main factors influencing transmissivity; (2) The widely used cubic law fails to effectively predict the transmissivity of high-viscosity liquids in a fracture, and the influence of liquid viscosity is considerably higher than that predicted by the cubic law; and (3) This study proposed a transmissivity prediction model of DNAPLs in a rock fracture based on multivariate regression analysis. The proposed model provides more accurate prediction results than those predicted by the cubic law, and is applicable to fracture apertures ranging from 5 × 10−4 to 2.5 × 10−3 m as well as to every kind of fluid used in this study.
One-Step Fabrication of Amino-Functionalized Fe3O4@SiO2 Core-Shell Magnetic Nanoparticles as a Potential Novel Platform for Removal of Cadmium (II) from Aqueous Solution
Authors: Prabu Deivasigamani , Senthil Kumar Ponnusamy , Indraganti Sravya , Sathish Sundararaman , Aravind Kumar Jagadeesan , Vijai Anand Kabali
Published: February 2022

Fe3O4@SiO2-NH2 core-shell magnetic nanoparticles were developed by a rapid one-step precipitation route followed by reverse microemulsion and amine functionalization. In this study, an Fe3O4@SiO2-NH2 nanoparticle was used to evaluate its adsorption efficiency for the treatment of a synthetic solution of Cd(II) ion. The structural and physicochemical properties of Fe3O4@SiO2-NH2 nanoparticles were characterized by XRD, SEM-EDAX, TEM, FTIR and TGA. From the TEM analysis, the morphology of Fe3O4@SiO2-NH2 was found as 100–300 nm. In TGA, the first weight loss was noticed between 373 and 573 K, the second was between 673 and 773 K and the final weight loss took place above 773 K. Batch experimental tests, such as pH, dosage of Fe3O4@SiO2-NH2, Cd(II) ion concentration, temperature as well as interaction time, were conducted and evaluated. Experimental study data were used for the non-linear forms exhibited by isotherms and kinetics of the sorption procedure. The equilibrium adsorption observations were adequately combined with pseudo-first-order kinetics as well as Freundlich isotherm. Monolayer maximum adsorption capacity was found to be 40.02 mg/g, recorded at pH 6 with an interaction time of 30 min, temperature of 303 K and sorbent dose of 2.0 g/L. The thermodynamic study indicated that the adsorption process was an exothermic, spontaneous reaction (−∆oo = −15.46–7.81 (kJ/mol)). The as-synthesized sorbent had excellent recyclability, and its adsorption efficiency was maintained after five cycles of reuse. The findings of the study exhibited the magnetic Fe3O4@SiO2-NH2-nanoparticle as an alternative effective adsorbent in eradicating Cd(II) ions from aqueous solution.
Scientometric Analysis of Research on Corporate Social Responsibility
Authors: Morales-Parragué Mario , Araya-Castillo Luis , Molina-Luque Fidel , Moraga-Flores Hugo
Published: February 2022

This work shows how Corporative Social Responsibility (CSR) has been filtering into different management areas, providing an insight into its evolution, and presenting literature reviews and efforts to incorporate conceptualisations and recommendations on its application. It can be understood through a scientometric and bibliometric analysis, using the WoS documents on the “Social Responsibility” concept in the “Business and Economics” category, analysing a total of 8728 papers up to the year 2020. In this work, CSR is associated with views from different fields of study in economics and business, highlighting diverse management fields; it seeks to explain the correlation between CSR and concepts from such fields of study, suggesting that there is a need to order and question the current understanding of CSR and show its relevance so it can be considered an area of specialisation within the management of businesses.
Self-Assessment Framework for Corporate Environmental Sustainability in the Era of Digitalization
Authors: Eisner Eduard , Hsien Cadence , Mennenga Mark , Khoo Zi-Yu , Dönmez Jasmin , Herrmann Christoph , Low Jonathan Sze Choong
Published: February 2022

The shift towards a climate-neutral economy will affect businesses in the upcoming decades. Companies will need to increase their transformation towards environmentally sustainable businesses in the following years, in which digitalization might be a practical enabler to accelerate this transformation. However, as a starting point, companies require knowledge of their current sustainability performance to manage this transition and need a method that provides the necessary information. The use of self-assessment tools is a widely acknowledged method for such processes. Nevertheless, there is a lack of self-assessment tools that integrate sustainability and digitalization perspectives to overcome different organizational barriers. This paper focuses on how managers can be supported in planning their transformations by interlinking sustainability and digitization. Our objective is to enable the managers of companies to assess their current state in terms of corporate environmental sustainability and to explore their policies, information systems, and actions to support their transformation towards sustainable and digital businesses. A self-assessment tool based on a rapid questionnaire is presented after reviewing and synthesizing different approaches, including maturity modeling, sustainability reporting, and digital assessment tools. The self-assessment tool is improved upon evaluation by industry experts and the framework is tested on a case company.