bluuebunny/crossref_metadata_2025 huggingface dataset:
An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
Authors: Niu Zun , Li Guangchen , Guo Fugui , Shuai Qiangqiang , Zhu Bocheng
Published: May 2022

The Real-Time Kinematic (RTK) positioning algorithm is a promising positioning technique that can provide real-time centimeter-level positioning precision in GNSS-friendly areas. However, the performance of RTK can degrade in GNSS-hostile areas like urban canyons. The surrounding buildings and trees can reflect and block the Global Navigation Satellite System (GNSS) signals, obstructing GNSS receivers’ ability to maintain signal tracking and exacerbating the multipath effect. A common method to assist RTK is to couple RTK with the Inertial Navigation System (INS). INS can provide accurate short-term relative positioning results. The Extended Kalman Filter (EKF) is usually used to couple RTK with INS, whereas the GNSS outlying observations significantly influence the performance. The Robust Kalman Filter (RKF) is developed to offer resilience against outliers. In this study, we design an algorithm to improve the traditional RKF. We begin by implementing the tightly coupled RTK/INS algorithm and the conventional RKF in C++. We also introduce our specific implementation in detail. Then, we test and analyze the performance of our codes on public datasets. Finally, we propose a novel algorithm to improve RKF and test the improvement. We introduce the Carrier-to-Noise Ratio (CNR) to help detect outliers that should be discarded. The results of the tests show that our new algorithm’s accuracy is improved when compared to the traditional RKF. We also open source the majority of our code, as we find there are few open-source projects for coupled RTK/INS in C++. Researchers can access the codes at our GitHub.
The Zonation of Mountain Frozen Ground under Aspect Adjustment Revealed by Ground-Penetrating Radar Survey—A Case Study of a Small Catchment in the Upper Reaches of the Yellow River, Northeastern Qinghai–Tibet Plateau
Authors: Liu Guangyue , Zhao Lin , Xie Changwei , Zou Defu , Wu Tonghua , Du Erji , Wang Lingxiao , Sheng Yu , Zhao Yonghua , Xiao Yao , Wang Chong , Wang Yiwei
Published: May 2022

Permafrost distribution is of great significance for the study of climate, ecology, hydrology, and infrastructure construction in high-cold mountain regions with complex topography. Therefore, updated high-resolution permafrost distribution mapping is necessary and highly demanded in related fields. This case study conducted in a small catchment in the northeast of the Qinghai Tibet Plateau proposes a new method of using ground-penetrating radar (GPR) to detect the stratigraphic structure, interpret the characteristics of frozen ground, and extract the boundaries of permafrost patches in mountain areas. Thus, an empirical–statistical model of mountain frozen ground zonation, along with aspect (ASP) adjustment, is established based on the results of the GPR data interpretation. The spatial mapping of the frozen ground based on this model is compared with a field survey dataset and two existing permafrost distribution maps, and their consistencies are all higher than 80. In addition, the new map provides more details on the distribution of frozen ground. In this case, the influence of ASP on the distribution of permafrost in mountain areas is revealed: the adjustment of ASP on the lower limit of continuous and discontinuous permafrost is 180–200 m, the difference in the annual mean ground temperature between sunny and shady slopes is up to 1.4–1.6 °C, and the altitude-related temperature variation and uneven distribution of solar radiation in different ASPs comprehensively affect the zonation of mountain frozen ground. This work supplements the traditional theory of mountain permafrost zonation, the results of which are of value to relevant scientific studies and instructive to engineering construction in this region.
Intelligent Land-Vehicle Model Transfer Trajectory Planning Method Based on Deep Reinforcement Learning
Authors: Yu Lingli , Shao Xuanya , Wei Yadong , Zhou Kaijun
Published: May 2022

To address the problem of model error and tracking dependence in the process of intelligent vehicle motion planning, an intelligent vehicle model transfer trajectory planning method based on deep reinforcement learning is proposed, which is able to obtain an effective control action sequence directly. Firstly, an abstract model of the real environment is extracted. On this basis, a deep deterministic policy gradient (DDPG) and a vehicle dynamic model are adopted to jointly train a reinforcement learning model, and to decide the optimal intelligent driving maneuver. Secondly, the actual scene is transferred to an equivalent virtual abstract scene using a transfer model. Furthermore, the control action and trajectory sequences are calculated according to the trained deep reinforcement learning model. Thirdly, the optimal trajectory sequence is selected according to an evaluation function in the real environment. Finally, the results demonstrate that the proposed method can deal with the problem of intelligent vehicle trajectory planning for continuous input and continuous output. The model transfer method improves the model’s generalization performance. Compared with traditional trajectory planning, the proposed method outputs continuous rotation-angle control sequences. Moreover, the lateral control errors are also reduced.
Intraoperative Optical Monitoring of Spinal Cord Hemodynamics Using Multiwavelength Imaging System
Authors: Mainard Nicolas , Tsiakaka Olivier , Li Songlin , Denoulet Julien , Messaoudene Karim , Vialle Raphael , Feruglio Sylvain
Published: May 2022

The spinal cord is a major structure of the central nervous system allowing, among other things, the transmission of afferent sensory and efferent motor information. During spinal surgery, such as scoliosis correction, this structure can be damaged, resulting in major neurological damage to the patient. To date, there is no direct way to monitor the oxygenation of the spinal cord intraoperatively to reflect its vitality. This is essential information that would allow surgeons to adapt their procedure in case of ischemic suffering of the spinal cord. We report the development of a specific device to monitor the functional status of biological tissues with high resolution. The device, operating with multiple wavelengths, uses Near-InfraRed Spectroscopy (NIRS) in combination with other additional sensors, including ElectroNeuroGraphy (ENG). In this paper, we focused primarily on aspects of the PhotoPlethysmoGram (PPG), emanating from four different light sources to show in real time and record biological signals from the spinal cord in transmission and reflection modes. This multispectral system was successfully tested in in vivo experiments on the spinal cord of a pig for specific medical applications.
Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques
Authors: Baghdadi Nadiah , Maklad Ahmed S. , Malki Amer , Deif Mohanad A.
Published: May 2022

Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use.
RT-ViT: Real-Time Monocular Depth Estimation Using Lightweight Vision Transformers
Authors: Ibrahem Hatem , Salem Ahmed , Kang Hyun-Soo
Published: May 2022

The latest research in computer vision highlighted the effectiveness of the vision transformers (ViT) in performing several computer vision tasks; they can efficiently understand and process the image globally unlike the convolution which processes the image locally. ViTs outperform the convolutional neural networks in terms of accuracy in many computer vision tasks but the speed of ViTs is still an issue, due to the excessive use of the transformer layers that include many fully connected layers. Therefore, we propose a real-time ViT-based monocular depth estimation (depth estimation from single RGB image) method with encoder-decoder architectures for indoor and outdoor scenes. This main architecture of the proposed method consists of a vision transformer encoder and a convolutional neural network decoder. We started by training the base vision transformer (ViT-b16) with 12 transformer layers then we reduced the transformer layers to six layers, namely ViT-s16 (the Small ViT) and four layers, namely ViT-t16 (the Tiny ViT) to obtain real-time processing. We also try four different configurations of the CNN decoder network. The proposed architectures can learn the task of depth estimation efficiently and can produce more accurate depth predictions than the fully convolutional-based methods taking advantage of the multi-head self-attention module. We train the proposed encoder-decoder architecture end-to-end on the challenging NYU-depthV2 and CITYSCAPES benchmarks then we evaluate the trained models on the validation and test sets of the same benchmarks showing that it outperforms many state-of-the-art methods on depth estimation while performing the task in real-time (∼20 fps). We also present a fast 3D reconstruction (∼17 fps) experiment based on the depth estimated from our method which is considered a real-world application of our method.
Research Progress of Noise in High-Speed Cutting Machining
Authors: Wei Weihua , Shang Yunyue , Peng You , Cong Rui
Published: May 2022

High-speed cutting technology has become a development trend in the material processing industry. However, high-intensity noise generated during high-speed cutting exerts a potential effect on the processing efficiency, processing accuracy, and product quality of the workpiece; it may even cause hidden safety hazards. To conduct an in-depth study of noise in high-speed cutting machining, this work reviews noise sources, noise collection and numerical recognition, noise control, and condition monitoring based on acoustic signals. First, this article introduces noise sources, noise signal acquisition equipment, and analysis software. It is pointed out that how to accurately classify and recognize the target signal in the complex high-speed machining environment is one of the focuses of scholars’ research. Then, it points out that a computer achieves high accuracy and practicability in signal analysis, processing, and result display. Second, in the aspect of noise signal processing, the characteristics of noise signals are analyzed. It is pointed out that accurately analyzing the characteristics of different noise source signals and adopting appropriate methods for identification and processing are the necessary conditions for effectively controlling and reducing the noise in the process of high-speed cutting. The advantages and applicable fields of artificial intelligence algorithms in processing mixed noise source signals with different frequency characteristics are compared, providing ideas for studying the mechanism of noise generation and the identification of noise sources. Third, in terms of noise control, a detailed overview is provided from the aspects of the treatment of the noise source that contributes the most to the overall noise, the improvement of the tool structure, the optimization of cutting parameters, and the analysis of contact factors between the tool and the workpiece. It provides an effective way for noise control in the process of high-speed cutting. In addition, the application of acoustic signals to condition monitoring is also thoroughly analyzed. The practical application value of condition monitoring based on acoustic signals in high-speed machining is highlighted. Finally, this paper summarizes the positive significance of noise research in high-speed machining and identifies key problems and possible research methods that require further study in the future.
Non-Destructive Evaluation of the Cutting Surface of Hardwood Finger Joints
Authors: Stolze Hannes , Gurnik Michael , Koddenberg Tim , Kröger Jonas , Köhler Robert , Viöl Wolfgang , Militz Holger
Published: May 2022

In this study, the surface parameters wettability, roughness, and adhesive penetration, which are important for wood bonding, were investigated and evaluated utilizing non-destructive methods after different mechanical processing. For this purpose, beech and birch finger joints were prepared with different cutting combinations (three cutters with different sharpness levels and two feed rates) in an industrial process. Effects and interactions on the surface parameters resulting from the different cutting combinations were evaluated using three Full Factorial Designs. The various cutting parameters had a predominantly significant influence on the surface parameters. The effects and identified interactions highlight the complexity of the cutting surface and the importance of wood bonding. In this respect, a new finding is that with sharper cutters, higher contact angles of the adhesives occur. The methods (contact angle measurement, laser scanning microscopy, and brightfield microscopy) used were well suited to make effects visible and quantifiable, which can be of interest for the quality control of the wood processing industry. The results can help to better understand and evaluate the design of wood surfaces via machining and the bonding of hardwoods. Possibly the results can contribute to further standardizing the production of load-bearing hardwood finger joints and making them more efficient.
A Review on Coupled Bulk Acoustic Wave MEMS Resonators
Authors: Wang Linlin , Wang Chen , Wang Yuan , Quan Aojie , Keshavarz Masoumeh , Madeira Bernardo Pereira , Zhang Hemin , Wang Chenxi , Kraft Michael
Published: May 2022

With the introduction of the working principle of coupled resonators, the coupled bulk acoustic wave (BAW) Micro-Electro-Mechanical System (MEMS) resonators have been attracting much attention. In this paper, coupled BAW MEMS resonators are discussed, including the coupling theory, the actuation and sensing theory, the transduction mechanism, and the applications. BAW MEMS resonators normally exhibit two types of vibration modes: lateral (in-plane) modes and flexural (out-of-plane) modes. Compared to flexural modes, lateral modes exhibit a higher stiffness with a higher operating frequency, resulting in a lower internal loss. Also, the lateral mode has a higher Q factor, as the fluid damping imposes less influence on the in-plane motion. The coupled BAW MEMS resonators in these two vibration modes are investigated in this work and their applications for sensing, timing, and frequency reference are also presented.
Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities
Authors: Ingle Palash Yuvraj , Kim Young-Gab
Published: May 2022

With the adaptation of video surveillance in many areas for object detection, monitoring abnormal behavior in several cameras requires constant human tracking for a single camera operative, which is a tedious task. In multiview cameras, accurately detecting different types of guns and knives and classifying them from other video surveillance objects in real-time scenarios is difficult. Most detecting cameras are resource-constrained devices with limited computational capacities. To mitigate this problem, we proposed a resource-constrained lightweight subclass detection method based on a convolutional neural network to classify, locate, and detect different types of guns and knives effectively and efficiently in a real-time environment. In this paper, the detection classifier is a multiclass subclass detection convolutional neural network used to classify object frames into different sub-classes such as abnormal and normal. The achieved mean average precision by the best state-of-the-art framework to detect either a handgun or a knife is 84.21% or 90.20% on a single camera view. After extensive experiments, the best precision obtained by the proposed method for detecting different types of guns and knives was 97.50% on the ImageNet dataset and IMFDB, 90.50% on the open-image dataset, 93% on the Olmos dataset, and 90.7% precision on the multiview cameras. This resource-constrained device has shown a satisfactory result, with a precision score of 85.5% for detection in a multiview camera.
Advanced Fault-Tolerant Anti-Surge Control System of Centrifugal Compressors for Sensor and Actuator Faults
Authors: Alsuwian Turki , Amin Arslan Ahmed , Maqsood Muhammad Taimoor , Qadir Muhammad Bilal , Almasabi Saleh , Jalalah Mohammed
Published: May 2022

Faults frequently occur in the sensors and actuators of process machines to cause shutdown and process interruption, thereby creating costly production loss. centrifugal compressors (CCs) are the most used equipment in process industries such as oil and gas, petrochemicals, and fertilizers. A compressor control system called an anti-surge control (ASC) system based on many critical sensors and actuators is used for the safe operation of CCs. In this paper, an advanced active fault-tolerant control system (AFTCS) has been proposed for sensor and actuator faults of the anti-surge control system of a centrifugal compressor. The AFTCS has been built with a dedicated fault detection and isolation (FDI) unit to detect and isolate the faulty part as well as replace the faulty value with the virtual redundant value from the observer model running in parallel with the other healthy sensors. The analytical redundancy is developed from the mathematical modeling of the sensors to provide estimated values to the controller in case the actual sensor fails. Dual hardware redundancy has been proposed for the anti-surge valve (ASV). The simulation results of the proposed Fault-tolerant control (FTC) for the ASC system in the experimentally validated CC HYSYS model reveal that the system continued to operate in the event of faults in the sensors and actuators maintaining system stability. The proposed FTC for the ASC system is novel in the literature and significant for the process industries to design a highly reliable compressor control system that would continue operation despite faults in the sensors and actuators, hence preventing costly production loss.
Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors
Authors: Justa Josef , Šmídl Václav , Hamáček Aleš
Published: May 2022

The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost IMU sensor. Each subject was observed using three sensors at the shoe, shin, and thigh. We show that existing general-purpose architectures outperform classical feature-based approaches and propose a novel architecture tailored for this task. The proposed architecture is based on a semi-supervised variational auto-encoder structure with innovated decoder in the form of a dense layer with a sinusoidal activation function. The proposed architecture achieved the lowest average error on the test data. Analysis of sensor placement reveals that the best location for the sensor is the shoe. Significant accuracy gain was observed when all three sensors were available. All data acquired in this experiment and the code of the estimation methods are available for download.
Optimal Sensor Placement for Modal-Based Health Monitoring of a Composite Structure
Authors: Ručevskis Sandris , Rogala Tomasz , Katunin Andrzej
Published: May 2022

Optimal sensor placement is one of the important issues in monitoring the condition of structures, which has a major influence on monitoring system performance and cost. Due to this, it is still an open problem to find a compromise between these two parameters. In this study, the problem of optimal sensor placement was investigated for a composite plate with simulated internal damage. To solve this problem, different sensor placement methods with different constraint variants were applied. The advantage of the proposed approach is that information for sensor placement was used only from the structure’s healthy state. The results of the calculations according to sensor placement methods were subsets of possible sensor network candidates, which were evaluated using the aggregation of different metrics. The evaluation of selected sensor networks was performed and validated using machine learning techniques and visualized appropriately. Using the proposed approach, it was possible to precisely detect damage based on a limited number of strain sensors and mode shapes taken into consideration, which leads to efficient structural health monitoring with resource savings both in costs and computational time and complexity.
Performance of the Linear Model Scattering of 2D Full Object with Limited Data
Authors: Sekehravani Ehsan Akbari , Leone Giovanni , Pierri Rocco
Published: May 2022

Inverse scattering problems stand at the center of many important imaging applications, such as geophysical explorations, radar imaging, and synthetic-aperture radar (SAR). Several methods have been proposed to solve them when the full data are available, usually providing satisfactory reconstructions. However, it is impossible to acquire the full data in many practical circumstances, such as target detection and ground penetrating radar (GPR); consequently, only limited data are available. Thus, this paper focuses on the mathematical analysis and some numerical simulations to estimate the achievable resolution in reconstructing an object from the knowledge of the scattered far-field when only limited data are available, with multi-view excitations at a single frequency. We focus on 2D full rectangular geometry as the investigation domain (ID). We also examine the number of degrees of freedom (NDF) and evaluate the point spread function (PSF). In particular, the NDF of the considered geometry can be estimated analytically. An approximated closed-form evaluation of the PSF is recalled, discussed, and compared with the exact one. Moreover, receiving, transmission, and angle sensing modes are considered to apply the analysis to more realistic scenarios to highlight the difference between the corresponding NDF and the resulting resolution performances. Finally, interesting numerical applications of the resolution analysis for the localization of a collection of point-like scatterers are presented to illustrate how it matches the expectations.
Feasibility of Overground Gait Training Using a Joint-Torque-Assisting Wearable Exoskeletal Robot in Children with Static Brain Injury
Authors: Hong Juntaek , Lee Jongweon , Choi Taeyoung , Choi Wooin , Kim Taeyong , Kwak Kyuwan , Kim Seongjun , Kim Kyeongyeol , Kim Daehyun
Published: May 2022

Pediatric gait disorders are often chronic and accompanied by various complications, which challenge rehabilitation efforts. Here, we retrospectively analyzed the feasibility of overground robot-assisted gait training (RAGT) using a joint-torque-assisting wearable exoskeletal robot. In this study, 17 children with spastic cerebral palsy, cerebellar ataxia, and chronic traumatic brain injury received RAGT sessions. The Gross Motor Function Measure (GMFM), 6-min walk test (6 MWT), and 10-m walk test (10 MWT) were performed before and after intervention. The oxygen rate difference between resting and training was performed to evaluate the intensity of training in randomly selected sessions, while the Quebec User Evaluation of Satisfaction with assistive Technology 2.0 assessment was performed to evaluate its acceptability. A total of four of five items in the GMFM, gait speed on the 10 MWT, and total distance on the 6 MWT showed statistically significant improvement (p < 0.05). The oxygen rate was significantly higher during the training versus resting state. Altogether, six out of eight domains showed satisfaction scores more than four out of five points. In conclusion, overground training using a joint-torque-assisting wearable exoskeletal robot showed improvement in gross motor and gait functions after the intervention, induced intensive gait training, and achieved high satisfaction scores in children with static brain injury.
Physical Demands during the Game and Compensatory Training Session (MD + 1) in Elite Football Players Using Global Positioning System Device
Authors: Calderón-Pellegrino Gabriel , Gallardo Leonor , Garcia-Unanue Jorge , Felipe Jose Luis , Hernandez-Martin Antonio , Paredes-Hernández Víctor , Sánchez-Sánchez Javier
Published: May 2022

The aims of this study were to analyze the differences of physical demands of non-starter players regarding the playing time during the competition and to evaluate the physical demands of the compensatory training (MD + 1C) for substitute players in elite football. The match statistics and MD + 1C of substitute players from a professional Spanish LaLiga football club were analyzed using a 10-Hz global positioning system (GPS) Apex GPS system device, which has been validated as a reliable and valid method to analyze performance in team sports, during all games of the 2016/2017, 2017/2018 and 2018/2019 seasons. The starting players showed both lower total distances covered and high-intensity actions compared to the substitutes. Regarding the minutes played by the substitutes, greater physical performance was found for the players with fewer minutes (5–15 min). Furthermore, no differences were found between first and second divisions regarding physical performance of substitutes (p > 0.05). This study highlights the importance of individualizing the workload of training sessions for substitutes and starters. Furthermore, the complementary session should be individualized according to the minutes played by the substitutes. These players are potentially under-loaded compared to starters, especially in terms of high-intensity actions, therefore additional session-specific training for each substitute would be useful to reach the optimal training load according to the minutes played during the game.
Model-Based Correction of Temperature-Dependent Measurement Errors in Frequency Domain Electromagnetic Induction (FDEMI) Systems
Authors: Tazifor Martial , Zimmermann Egon , Huisman Johan Alexander , Dick Markus , Mester Achim , Van Waasen Stefan
Published: May 2022

Data measured using electromagnetic induction (EMI) systems are known to be susceptible to measurement influences associated with time-varying external ambient factors. Temperature variation is one of the most prominent factors causing drift in EMI data, leading to non-reproducible measurement results. Typical approaches to mitigate drift effects in EMI instruments rely on a temperature drift calibration, where the instrument is heated up to specific temperatures in a controlled environment and the observed drift is determined to derive a static thermal apparent electrical conductivity (ECa) drift correction. In this study, a novel correction method is presented that models the dynamic characteristics of drift using a low-pass filter (LPF) and uses it for correction. The method is developed and tested using a customized EMI device with an intercoil spacing of 1.2 m, optimized for low drift and equipped with ten temperature sensors that simultaneously measure the internal ambient temperature across the device. The device is used to perform outdoor calibration measurements over a period of 16 days for a wide range of temperatures. The measured temperature-dependent ECa drift of the system without corrections is approximately 2.27 mSm−1K−1, with a standard deviation (std) of only 30 μSm−1K−1 for a temperature variation of around 30 K. The use of the novel correction method reduces the overall root mean square error (RMSE) for all datasets from 15.7 mSm−1 to a value of only 0.48 mSm−1. In comparison, a method using a purely static characterization of drift could only reduce the error to an RMSE of 1.97 mSm−1. The results show that modeling the dynamic thermal characteristics of the drift helps to improve the accuracy by a factor of four compared to a purely static characterization. It is concluded that the modeling of the dynamic thermal characteristics of EMI systems is relevant for improved drift correction.
An Advanced System for the Visualisation and Prediction of Equipment Ageing
Authors: Ancione Giuseppa , Saitta Rebecca , Bragatto Paolo , Fiumara Giacomo , Milazzo Maria Francesca
Published: May 2022

The control of major hazards involving dangerous substances in the chemical and process industry requires verifying equipment ageing according to the current legislation. This means to monitor its real conditions regarding degradation mechanisms and forecast their evolution over time. A system, named Virtual Sensor, supports this activity that is usually conducted during on-field inspections (safety walks). It is designed to collect ageing-related information, process data through some models, and produce prognostic estimates regarding the corrosion rate, the probability of the critical pit, the corrosion evolution on the equipment surface, and the residual lifetime, visualising the results in Augmented Reality (AR). An atmospheric storage tank of diesel oil was chosen as the case study; its 3D model was realised, and a miniature model was reproduced in the laboratory. Through the Virtual Sensor, data of past inspections were acquired. The application successfully managed and elaborated these data, showing the outputs in AR during a safety walk.
Assessing the Underlying Drivers of Change over Two Decades of Land Use and Land Cover Dynamics along the Standard Gauge Railway Corridor, Kenya
Authors: Sang Catherine C. , Olago Daniel O. , Nyumba Tobias O. , Marchant Robert , Thorn Jessica P. R.
Published: May 2022

Land cover has been modified by anthropogenic activities for thousands of years, although the speed of change has increased in recent decades, particularly driven by socio-economic development. The development of transport infrastructure can accelerate land use land cover change, resulting in impacts on natural resources such as water, biodiversity, and food production. To understand the interaction between land cover and social–ecological drivers, changing land cover patterns and drivers of change must be identified and quantified. This study documents land cover dynamics along the Standard Gauge Railway (SGR) corridor in Kenya and evaluates the underlying drivers of this change from 2000 to 2019. The study utilised GIS and remote sensing techniques to assess the land use and land cover changes along the SGR corridor, while correlational and regression analyses were used to evaluate various drivers of the changes. Results showed that built-up areas, bare lands, water bodies, croplands and forests increased by 144.39%, 74.73%, 74.42%, 9.32% and 4.85%, respectively, while wetlands, grasslands and shrub lands reduced by 98.54%, 67.00% and 33.86%, respectively. The underlying drivers responsible for these land use and land cover dynamics are population growth, urbanisation, economic growth and agro-ecological factors. Such land cover changes affect environmental sustainability, and we stress the need to adequately identify and address the cumulative social and environmental impacts of mega-infrastructure projects and their interacting investments. The findings of this study provide an evidence base for the evaluation of the social–ecological impacts of the SGR and the implementation of best practices that will lead to enhanced sustainability in the development corridors in Kenya and beyond.
Original Innovation through Inter-Organizational Collaboration: Empirical Evidence from University-Focused Alliance Portfolio in China
Authors: Zhou Jia , Guo Aifang , Chen Yutao , Chen Jin
Published: May 2022

A university-focused alliance portfolio is a manifestation of industry–university–research cooperation and has become an important path to realize original innovation in science and technology. Unlike traditional technological innovation, original innovation particularly emphasizes new ideas and research areas never covered before. This paper integrates resource-based theory, alliance portfolio theory, and innovation theory, and aims to scientifically establish an evaluation index system of original innovation performance from the three dimensions of initiate research, technology breakthrough, and research breakthrough. The work explores how a university can select partners to realize collaborative innovation in the context of inter-organizational scientific research cooperation with multiple innovation subjects for nationwide research institutes and universities in mainland China. The empirical results show that resource complementarity has a significant positive effect on innovation performance. Three typical universities in the “2011 project” are selected as post-interview cases for enriching empirical evidence. This study contributes to original innovation literature by introducing the concept of resource complementarity in a university-focused alliance portfolio, and further provides implications for original and science-driven innovation studies and suggests directions for university and research institutes.
Green Finance as an Institutional Mechanism to Direct the Belt and Road Initiative towards Sustainability: The Case of China
Authors: Zhang Meihui , Zhang Chi , Li Fenghua , Liu Ziyu
Published: May 2022

China’s Belt and Road Initiative (hereinafter BRI) has been challenged due to its failure to promote long-term environmental sustainability. Recent years, however, have witnessed a tendency in which green finance is being deliberately crafted by Chinese regulatory authorities in order to support a green BRI. Despite BRI’s popularity as a scientific research topic, the legal literature with regard to China’s domestic green finance laws/regulations and their impact on BRI’s sustainability is insufficient. The purpose of this study is to fill this research gap by examining how China has utilized green finance as an institutional mechanism in order to steer the BRI toward sustainability, as well as to provide policy suggestions on how to further improve BRI’s sustainability by addressing existing legal and regulatory deficiencies. The methodology utilized was a textual analysis of legal/regulatory documents. Based on an investigation of the environmental sustainability challenges faced by the BRI, this paper elaborates Chinese banks’ green finance practices in facilitating a sustainable BRI. It makes a further inquiry into the regulatory instruments by which Chinese banks are able improve their green performances, and elucidates existing regulatory deficiencies. Finally, it presents policy recommendations intended to enhance the ability of Chinese banks to obtain more private capital to finance BRI green projects, including: (1) introducing ‘green’ provisions to the draft Commercial Bank Law; (2) developing a mutually recognized green standard; (3) mandating environmental disclosures; and (4) encouraging institutional investors to buy green assets.
Taguchi Loss Function in Intuitionistic Fuzzy Sets along with Personal Perceptions for the Sustainable Supplier Selection Problem
Authors: Turk Seda
Published: May 2022

Sustainability, a new interdisciplinary paradigm, can be defined as a standard in terms of economic, environmental and social awareness of a company. In many applications, theoretical sustainability models considering the importance of three aspects equally differed from models used in practice. A sustainable supplier selection problem generally contains many conflicting information and the imprecise decision makers’ knowledge, and decision makers can judge suppliers based on their first impression. Hence, in this study, a sustainable supplier selection problem of a plastic packaging company in Turkey is taken into account under an expert-based model and a theorical-based model for three scenarios which consider personal perceptions of decision makers. First, an intuitionistic fuzzy set-based method is applied to the problem using two different distance measurement approaches, namely, fuzzy normalized Euclidean distance and the Taguchi loss function, for which an alternative method is proposed. Then, suppliers are ranked and the validity of the results is also checked using the Pearson product–moment correlation coefficient. The results indicate that (i) the personal perception of decision makers has an inevitable impact on results, (ii) the proposed approach can capture the associated uncertainties embedded in decision makers and fuzzy environment, and (iii) there is a disparity between the theory and the reality of sustainability.
Optimal Control of an Energy-Storage System in a Microgrid for Reducing Wind-Power Fluctuations
Authors: Aazami Rahmat , Heydari Omid , Tavoosi Jafar , Shirkhani Mohammadamin , Mohammadzadeh Ardashir , Mosavi Amir
Published: May 2022

In conventional low-voltage grids, energy-storage devices are mainly driven by final consumers to correct peak consumption or to protect against sources of short-term breaks. With the advent of microgrids and the development of energy-storage systems, the use of this equipment has steadily increased. Distributed generations (DGs), including wind-power plants as a renewable energy source, produces vacillator power due to the nature of variable wind. Microgrids have output power fluctuations, which can cause devastating effects such as frequency fluctuations. Storage can be used to fix this problem. In this paper, a grid-connected wind turbine and a photovoltaic system are investigated considering the atmospheric conditions and wind-speed variations, and a control method is proposed. The main purpose of this paper is to optimize the capacity of energy-storage devices to eliminate power fluctuations in the microgrid. Finally, the conclusion shows that, in microgrids with supercapacitors, the optimal capacity of microgrid supercapacitors is determined. This method of control, utilizing the combined energy-storage system of the battery supercapacitor, in addition to reducing the active power volatility of the wind turbine and photovoltaic generation systems, also considers the level of battery protection and reduction in reactive-power fluctuations. In the proposed control system, the DC link in the energy-storage systems is separate from most of the work conducted, which can increase the reliability of the whole system. The simulations of the studied system are performed in a MATLAB software environment.
The Impact of Green Finance on Industrial Land Use Efficiency: Evidence from 279 Cities in China
Authors: Tian Fa , Hou Shiying
Published: May 2022

Improving the efficiency of industrial land use is of great significance to the sustainable development of cities. Based on a financial perspective, this paper studies the relationship between green finance and urban industrial land use efficiency (UILUE). First, the epsilon-based measure model was used to calculate the UILUE of 279 cities in China from 2011 to 2020, and then an empirical model is constructed to test the impact and path mechanism of green finance on UILUE. The research results show that green finance can improve the UILUE. The mediation effect test shows that the optimization of industrial structure and technological innovation are the key paths for green finance to affect UILUE. In addition, land finance inhibits the positive effect of green finance on the UILUE. This study provides new evidence for the role green finance plays in improving the efficiency of industrial land use and promoting the sustainable development of cities.
Women on Boards in Portuguese Listed Companies: Does Gender Diversity Influence Financial Performance?
Authors: Carmo Cecília , Alves Sandra , Quaresma Bruna
Published: May 2022

Corporate sustainability integrates financial performance with environmental, social, and governance (ESG) performance. Due to their personal characteristics, women can play an important role in promoting sustainable actions and strategic decision-making, creating positive effects on a company’s financial performance. The main objective of this paper is to analyze the effect of Board gender diversity on financial performance, in the context of Portuguese listed companies. Financial performance was measured through an accounting-based measure, the return on assets (ROA), and a market-based measure, the Tobin’s Q. Board gender diversity was proxied through several measures identified in the literature and the information needed to construct them was manually collected from the corporate governance reports. A regression analysis was conducted on panel data consisting of 29 companies from the period from 2010 to 2019. The results show that female presence is positively related with ROA when there are at least two women on the Board, or when the proportion of women is, at the least, 20%. A positive relationship was also found between the Tobin’s Q and the presence of at least 20% women on the Board. The results confirm the existence of the positive effects of Board gender diversity on financial performance, as predicted by resource dependence theory and agency theory, but only when a critical mass of women is reached. In general, we conclude that, although gender diversity promotion measures underlying Portuguese legislation are not yet fully consolidated, the imposed threshold of 20% women on Boards is a signal sent to the market and one that is valued by investors.
Enabling Knowledge through Structured Disaster Damage & Loss Data Management System
Authors: Faiella Anna , Menoni Scira , Boni Maria Pia , Panoutsopoulou Maria , Thoma Thekla , Salari Sandro , Rueda Nicolas
Published: May 2022

Effective and efficient management of post-disaster damage and loss data is a key component of disaster risk reduction and climate change adaptation policies to fulfil the requirements of the Sendai Framework, Sustainable Development Goals, and more recently, the European Climate Law. However, the reality of organized and structured damage and loss data collection is still in its infancy. In the era of rapid technological improvements, with overwhelming volumes and channels of data, we still record a lack of basic figures of disaster losses at the scale, granularity and level of detail needed for most applications. First, a theoretical overview of data science applied to disaster risk management and the description of collection procedures and use of damage data for buildings in the case of earthquakes for Italy and Greece are provided. Second, the Information System (IS) which is intended to enhance damage and loss data collection and management proposed by the LODE (Loss Data Enhancement for Disaster Risk Reduction and Climate Change Adaptation) project is illustrated. The IS is described in detail, starting from the stakeholder consultation to elicit the requirements, to the system’s architecture, design and implementation. The IS provides a comprehensive tool to input and query multisectoral post-disaster damage and loss data at relevant spatial and temporal scales. The part of the IS devoted to building damage is described in depth showing how obstacles and difficulties highlighted in the collection and use of data in the Greek and the Italian case have been approached and solved. Finally, further developments of the IS and its background philosophy are discussed, including the need for institutionalized damage data collection, engineering of the developed software and re-engineering of current damage and loss data practices.
An MFD Construction Method Considering Multi-Source Data Reliability for Urban Road Networks
Authors: Hong Rongrong , Liu Huan , An Chengchuan , Wang Bing , Lu Zhenbo , Xia Jingxin
Published: May 2022

Road network traffic management and control are the key mechanisms to alleviate urban traffic congestion. With this study, we aimed to characterize the traffic flow state of urban road networks using the Macroscopic Fundamental Diagram (MFD) to support area traffic control. The core property of an MFD is that the network flow is maximized when network traffic stays at an optimal accumulation state. The property can be used to optimize the temporal and spatial distribution of traffic flow with applications such as gating control. MFD construction is the basis of these MFD-based applications. Although many studies have been conducted to construct MFDs, few studies are dedicated to improving the accuracy considering the reliability of different sources of data. To this end, we propose an MFD construction method using multi-source data based on Dempster–Shafer evidence (DS evidence) theory considering the reliability of different data sources. First, the MFD was constructed using VTD and CSD, separately. Then, the fused MFD was derived by quantifying the reliability of different sources of data for each MFD parameter based on DS evidence theory. The results under real data and simulated data show that the accuracy of the constructed MFDs was greatly improved considering the reliability of different data sources (the maximum MFD estimation error was reduced by 22.3%). The proposed method has the potential to support the evaluation of traffic operations and the optimization of signal control schemes for urban traffic networks.
Segmentation of Consumer Preferences for Vegetables Produced in Areas Depressed by Drought
Authors: Adasme-Berríos Cristian , Valdes Rodrigo , Roco Lisandro , Gómez David , Carvajal Emilia , Herrera Camila , Espinoza Joaquín , Rivera Karla
Published: May 2022

The megadrought in Chile’s north and central zones has impacted the horticultural production of family farming. One way to mitigate these effects is by adding value to vegetables. However, no studies show the main attributes consumers value of vegetables produced in the megadrought zone. The study’s objective was to evaluate consumer preferences and identify segments based on the extrinsic attributes of vegetables grown in areas depressed by drought. We surveyed 946 vegetable buyers from the Antofagasta, Valparaiso, and Maule regions. Through the conjoint analysis technique, we identified that the main attributes preferred by consumers were presentation (45.3%) and type of vegetables (21.8%), followed by labeling (15.9%), producers (10.3%), and origin (6.7%). The cluster analysis revealed the existence of three segments. The largest segment is motivated by the way vegetables are presented (49.7%), followed by a second segment that values multi-attributes (31.9%) and a smaller segment that is interested in labeling (18.4%). These findings can help position these products in the market and raise awareness of family farming and the economic and production problems that they currently face.