bluuebunny/crossref_metadata_2025 huggingface dataset:
A Snapshot Infrared Imaging Fourier Transform Spectrometer for Dynamic Target Detection
Authors: Chen Yupeng , Lv Jinguang , Yue Wei , Zhao Yuanhang , Qin Yuxin , Tao Jin , Chen Cheng , Wang Weibiao , Liang Jingqiu
Published: March 2022

Infrared imaging spectrometry is utilized to detect and identify targets by collecting spectral images. In some cases, the infrared spectral images of dynamic targets need to be detected accurately, such as during remote sensing target tracking and engine tail flame detection applications. However, it is difficult to obtain reliable measurement results when using a traditional infrared imaging spectrometer with a scanning structure because of motion artifacts. This work proposes a snapshot infrared imaging Fourier transform spectrometer (SIIFTS) based on stepped micromirrors and a lens array. Two micromirrors sample the spectral information, and the lens array can realize multi-aperture snapshot imaging. The spectrometer is capable of collecting three-dimensional (3D) datasets during a single measurement period, and its absence of motion artifacts and its ability to work without moving parts is very important for dynamic target detection. The achromatic optical design of the SIIFTS is completed, and two front imaging systems for remote sensing and tail flame detection applications are designed for selection. A SIIFTS prototype was built, and flame detection tests were conducted in a laboratory environment. The experimental results show that the SIIFTS developed here can accurately and stably obtain real-time image and spectral information from dynamic targets.
Polarimetric Calibration Technique for a Fully Polarimetric Entomological Radar Based on Antenna Rotation
Authors: Yu Teng , Li Muyang , Li Weidong , Mao Huafeng , Wang Rui , Hu Cheng , Long Teng
Published: March 2022

For entomological radar, the polarization information of the target is usually used to estimate the biological parameters, such as orientation, body length, and mass, of the insect. Thus, the accuracy of polarization measurement directly affects the performance of the biological parameters’ estimation. The polarization measurement error is mainly caused by the imbalance of amplitude and phase between two polarization channels and the crosstalk of the dual-polarization antenna. In order to obtain the correct polarization information of the target, the polarimetric calibration of the entomological radar is required. This paper proposes a new polarimetric calibration technique based on antenna rotation, which does not require the calibrator to have a specific polarization scattering matrix (PSM). Compared with the currently existing calibration techniques, no prior knowledge of the calibrator PSM is required (in fact, any fixed-point target can be used as a calibrator); thus, the errors introduced by the mechanical process can be avoided. Simulations and data measured by radar verify the effectiveness of the method. This method has the potential to be extended to other fully polarimetric radar systems in the future, such as fully polarimetric weather radar, fully polarimetric synthetic aperture radar (SAR), and so on.
Impact of Aerosol Mixing State and Hygroscopicity on the Lidar Ratio
Authors: Zhang Zhijie , Liu Li , Wang Baomin , Tan Haobo , Lan Changxing , Wang Ye , Chan Pakwai
Published: March 2022

The lidar ratio (LR) is a key parameter for the retrieval of atmospheric optical parameters from lidar equations. In this study, we simulated the optical parameters to investigate the impact factors of the LR using a three-component optical aerosol assumption based on the Mie model. The simulated LR was generally related to the overall particle size of the aerosols, the proportion of elemental carbon (EC), as well as aerosol mixing states and hygroscopicity. The LR was positively correlated with the particle size and volume fraction of elemental carbon (fEC). The LR increased more than three-fold with the increase in fEC from 0% to 40%. The LR of the core-shell (CS) mixing state and homogeneously internal (INT) mixing state was greater than that of the external (EXT) mixing state. The LR of all mixing states increased monotonically with hygroscopicity when the fEC was below 10%, while the LR of the core-shell mixing state (homogeneously internal mixing state) initially decreased (increased) and then increased (decreased) with increasing hygroscopicity when the fEC was more than 20%. These results will help in selecting a reasonable LR for practical applications.
Effect of Surface Morphology Changes on Optical Properties of Silicon Nanowire Arrays
Authors: Wang Shanshan , Huang Shujia , Zhao Jijie
Published: March 2022

The optical properties of silicon nanowire arrays (SiNWs) are closely related to surface morphology due to quantum effects and quantum confinement effects of the existing semiconductor nanocrystal. In order to explore the influence of the diameters and distribution density of nanowires on the light absorption in the visible to near infrared band, we report the highly efficient method of multiple replication of versatile homogeneous Au films from porous anodic aluminum oxide (AAO) membranes by ion sputtering as etching catalysts; the monocrystalline silicon is etched along the growth templates in a fixed proportion chemical solution to form homogeneous ordered arrays of different morphology and distributions on the surface. In this system, we demonstrate that the synthesized nanostructure arrays can be tuned to exhibit different optical characteristics in the test wavelength range by adjusting the structural parameters of AAO membranes.
Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
Authors: Chen Guo-Hong , Ni Jie , Chen Zhuo , Huang Hao , Sun Yun-Lei , Ip Wai Hung , Yung Kai Leung
Published: March 2022

Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time and to apply the detection methods in common scenarios, the CNN structure with preprocessed image data needs to be simplified. In this work, a detection method based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG) features is proposed. First, the Gamma correction was employed to highlight the grayscale distribution of the damage area, which compresses the space of normal pavement. The preprocessed image was then divided into several unit cells, whose grayscale and HOG were calculated, respectively. The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG (GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure and parameters. The trained indices suggested that the performance of the GHOG-based method was significantly improved, compared with the traditional HOG-based method. Furthermore, the GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy, in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale has a definite physical meaning, the present detection method possesses a potential application for the further detection of damage details in the future.
An Automated, Clip-Type, Small Internet of Things Camera-Based Tomato Flower and Fruit Monitoring and Harvest Prediction System
Authors: Lee Unseok , Islam Md Parvez , Kochi Nobuo , Tokuda Kenichi , Nakano Yuka , Naito Hiroki , Kawasaki Yasushi , Ota Tomohiko , Sugiyama Tomomi , Ahn Dong-Hyuk
Published: March 2022

Automated crop monitoring using image analysis is commonly used in horticulture. Image-processing technologies have been used in several studies to monitor growth, determine harvest time, and estimate yield. However, accurate monitoring of flowers and fruits in addition to tracking their movements is difficult because of their location on an individual plant among a cluster of plants. In this study, an automated clip-type Internet of Things (IoT) camera-based growth monitoring and harvest date prediction system was proposed and designed for tomato cultivation. Multiple clip-type IoT cameras were installed on trusses inside a greenhouse, and the growth of tomato flowers and fruits was monitored using deep learning-based blooming flower and immature fruit detection. In addition, the harvest date was calculated using these data and temperatures inside the greenhouse. Our system was tested over three months. Harvest dates measured using our system were comparable with the data manually recorded. These results suggest that the system could accurately detect anthesis, number of immature fruits, and predict the harvest date within an error range of ±2.03 days in tomato plants. This system can be used to support crop growth management in greenhouses.
General Image Fusion for an Arbitrary Number of Inputs Using Convolutional Neural Networks
Authors: Xiao Yifan , Guo Zhixin , Veelaert Peter , Philips Wilfried
Published: March 2022

In this paper, we propose a unified and flexible framework for general image fusion tasks, including multi-exposure image fusion, multi-focus image fusion, infrared/visible image fusion, and multi-modality medical image fusion. Unlike other deep learning-based image fusion methods applied to a fixed number of input sources (normally two inputs), the proposed framework can simultaneously handle an arbitrary number of inputs. Specifically, we use the symmetrical function (e.g., Max-pooling) to extract the most significant features from all the input images, which are then fused with the respective features from each input source. This symmetry function enables permutation-invariance of the network, which means the network can successfully extract and fuse the saliency features of each image without needing to remember the input order of the inputs. The property of permutation-invariance also brings convenience for the network during inference with unfixed inputs. To handle multiple image fusion tasks with one unified framework, we adopt continual learning based on Elastic Weight Consolidation (EWC) for different fusion tasks. Subjective and objective experiments on several public datasets demonstrate that the proposed method outperforms state-of-the-art methods on multiple image fusion tasks.
Application of a Control Scheme Based on Predictive and Linear Strategy for Improved Transient State and Steady-State Performance in a Single-Phase Quasi-Z-Source Inverter
Authors: Diaz-Bustos Manuel , Baier Carlos R. , Torres Miguel A. , Melin Pedro E. , Acuna Pablo
Published: March 2022

Z and quasi-Z-source inverters (Z/qZSI) have a nonlinear impedance network on their dc side, which allows the system to behave as a buck–boost converter in their outputs. The challenges derived from the qZSI topology include (a) the control of the voltage and current on its nonlinear impedance network, (b) the dynamic coupling between the ac and dc variables, and (c) the fact that a unique set of switches are used to manage the power at dc and ac side of the system. In this work, a control scheme that combines a PWM linear control strategy and a strategy based on finite control state model predictive control (FCS-MPC) is proposed. The linear approach works during steady state, while the FCS-MPC works during transient states, either in the start-up of the converter or during sudden reference changes. This work aims to show that the performance of this control proposal retains the best characteristics of both schemes, which allows it to achieve high-quality waveforms and error-free steady state, as well as a quick dynamic response during transients. The feasibility of the proposal is validated through experimental results.
A Configurable and Fully Synthesizable RTL-Based Convolutional Neural Network for Biosensor Applications
Authors: Kumar Pervesh , Yingge Huo , Ali Imran , Pu Young-Gun , Hwang Keum-Cheol , Yang Youngoo , Jung Yeon-Jae , Huh Hyung-Ki , Kim Seok-Kee , Yoo Joon-Mo , Lee Kang-Yoon
Published: March 2022

This paper presents a register-transistor level (RTL) based convolutional neural network (CNN) for biosensor applications. Biosensor-based diseases detection by DNA identification using biosensors is currently needed. We proposed a synthesizable RTL-based CNN architecture for this purpose. The adopted technique of parallel computation of multiplication and accumulation (MAC) approach optimizes the hardware overhead by significantly reducing the arithmetic calculation and achieves instant results. While multiplier bank sharing throughout the convolutional operation with fully connected operation significantly reduces the implementation area. The CNN model is trained in MATLAB® on MNIST® handwritten dataset. For validation, the image pixel array from MNIST® handwritten dataset is applied on proposed RTL-based CNN architecture for biosensor applications in ModelSim®. The consistency is checked with multiple test samples and 92% accuracy is achieved. The proposed idea is implemented in 28 nm CMOS technology. It occupies 9.986 mm2 of the total area. The power requirement is 2.93 W from 1.8 V supply. The total time taken is 8.6538 ms.
Analysis of Electric Field Distribution for SOI-FET Sensors with Dielectrophoretic Control
Authors: Naumova Olga V. , Zaytseva Elza G.
Published: March 2022

Silicon-on-insulator (SOI) nanowire or nanoribbon field-effect transistor (FET) biosensors are versatile platforms of electronic detectors for the real-time, label-free, and highly sensitive detection of a wide range of bioparticles. At a low analyte concentration in samples, the target particle diffusion transport to sensor elements is one of the main limitations in their detection. The dielectrophoretic (DEP) manipulation of bioparticles is one of the most successful techniques to overcome this limitation. In this study, TCAD modeling was used to analyze the distribution of the gradient of the electric fields E for the SOI-FET sensors with embedded DEP electrodes to optimize the conditions of the dielectrophoretic delivery of the analyte. Cases with asymmetrical and symmetrical rectangular electrodes with different heights, widths, and distances to the sensor, and with different sensor operation modes were considered. The results showed that the grad E2 factor, which determines the DEP force and affects the bioparticle movement, strongly depended on the position of the DEP electrodes and the sensor operation point. The sensor operation point allows one to change the bioparticle movement direction and, as a result, change the efficiency of the delivery of the target particles to the sensor.
Development of a Prototype Overground Pelvic Obliquity Support Robot for Rehabilitation of Hemiplegia Gait
Authors: Hwang Seunghoon , Lee Seungchan , Shin Dongbin , Baek Inhyuk , Ham Seoyeon , Kim Wansoo
Published: March 2022

In this work, we present the overground prototype gait-rehabilitation robot for using motion assistance and training for paralyzed patients. In contrast to the existing gait-rehabilitation robots, which focus on the sagittal plane motion of the hip and knee, we aim to develop a mobile-based pelvic support gait-rehabilitation system that includes a pelvic obliquity support mechanism and a lower-limb exoskeleton. To achieve this, a scissor mechanism is proposed to generate the paralyzed patient’s pelvic obliquity motion and weight support. Moreover, the lower limb exoskeleton robot is integrated with the developed system to provide the patient’s gait by correcting mechanical aids. We used computer-aided analysis to verify the performance of the prototype hardware itself. Through these methods, it was shown that our motor can sufficiently lift 100 kg of user weight through the scissor mechanism, and that the mobile driving wheel motor can operate at a speed of 1.6 m/s of human walking, showing that it can be used for gait rehabilitation of patients in need of a lower speed. In addition, we verified that the system drives the model by generating pelvic motion, and we verified the position controller of the integrated system, which supports the multi-degree motion by creating hip/knee/pelvic motion with a human dummy mannequin and systems. We believe that the proposed system can help address the complex rehabilitation motion assistance and training of paralyzed patients.
Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest
Authors: Jing Ying , Zheng Hong , Lin Chang , Zheng Wentao , Dong Kaihan , Li Xiaolong
Published: March 2022

In recent years, aviation security has become an important area of concern as foreign object debris (FOD) on the airport pavement has a huge potential risk to aircraft during takeoff and landing. Therefore, accurate detection of FOD is important to ensure aircraft flight safety. This paper proposes a novel method to detect FOD based on random forest. The complexity of information in airfield pavement images and the variability of FOD make FOD features difficult to design manually. To overcome this challenge, this study designs the pixel visual feature (PVF), in which weight and receptive field are determined through learning to obtain the optimal PVF. Then, the framework of random forest employing the optimal PVF to segment FOD is proposed. The effectiveness of the proposed method is demonstrated on the FOD dataset. The results show that compared with the original random forest and the deep learning method of Deeplabv3+, the proposed method is superior in precision and recall for FOD detection. This work aims to improve the accuracy of FOD detection and provide a reference for researchers interested in FOD detection in aviation.
Random Neural Network Based Epileptic Seizure Episode Detection Exploiting Electroencephalogram Signals
Authors: Shah Syed Yaseen , Larijani Hadi , Gibson Ryan M. , Liarokapis Dimitrios
Published: March 2022

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation.
The Conception of Test Fields for Fast Geometric Calibration of the FLIR VUE PRO Thermal Camera for Low-Cost UAV Applications
Authors: Fryskowska-Skibniewska Anna , Delis Paulina , Kedzierski Michal , Matusiak Dominik
Published: March 2022

The dynamic evolution of photogrammetry led to the development of numerous methods of geometric calibration of cameras, which are mostly based on building flat targets (fields) with features that can be distinguished in the images. Geometric calibration of thermal cameras for UAVs is an active research field that attracts numerous researchers. As a result of their low price and general availability, non-metric cameras are being increasingly used for measurement purposes. Apart from resolution, non-metric sensors do not have any other known parameters. The commonly applied process is self-calibration, which enables the determining of the approximate elements of the camera’s interior orientation. The purpose of this work was to analyze the possibilities of geometric calibration of thermal UAV cameras using proposed test field patterns and materials. The experiment was conducted on a FLIR VUE PRO thermal camera dedicated to UAV platforms. The authors propose the selection of various image processing methods (histogram equalization, thresholding, brightness correction) in order to improve the quality of the thermograms. The consecutive processing methods resulted in over 80% effectiveness on average by 94%, 81%, and 80 %, respectively. This effectiveness, for no processing and processing with the use of the filtering method, was: 42% and 38%, respectively. Only high-pass filtering did not improve the obtained results. The final results of the proposed method and structure of test fields were verified on chosen geometric calibration algorithms. The results of fast and low-cost calibration are satisfactory, especially in terms of the automation of this process. For geometric correction, the standard deviations for the results of specific methods of thermogram sharpness enhancement are two to three times better than results without any correction.
Vertical Handover Prediction Based on Hidden Markov Model in Heterogeneous VLC-WiFi System
Authors: Babalola Oluwaseyi Paul , Balyan Vipin
Published: March 2022

Visible light communication (VLC) channel quality depends on line-of-sight (LoS) transmission, which cannot guarantee continuous transmission due to interruptions caused by blockage and user mobility. Thus, integrating VLC with radio frequency (RF) such asWireless Fidelity (WiFi), provides good quality of experience (QoE) to users. A vertical handover (VHO) scheme that optimizes both the cost of switching and dwelling time of the hybrid VLC–WiFi system is required since blockage on VLC LoS usually occurs for a short period. Hence, an automated VHO algorithm for the VLC–WiFi system based on the hidden Markov model (HMM) is developed in this article. The proposed VHO prediction scheme utilizes the channel characterization of the networks, specifically, the measured received signal strength (RSS) values at different locations. Effective RSS are extracted from the huge datasets using principal component analysis (PCA), which is adopted with HMM, and thus reducing the computational complexity of the model. In comparison with state-of-the-art VHO handover prediction methods, the proposed HMM-based VHO scheme accurately obtains the most likely next assigned access point (AP) by selecting an appropriate time window. The results show a high VHO prediction accuracy and reduced mixed absolute percentage error performance. In addition, the results indicate that the proposed algorithm improves the dwell time on a network and reduces the number of handover events as compared to the threshold-based, fuzzy-controller, and neural network VHO prediction schemes. Thus, it reduces the ping-pong effects associated with the VHO in the heterogeneous VLC–WiFi network.
On-Demand Energy Transfer and Energy-Aware Polling-Based MAC for Wireless Powered Sensor Networks
Authors: Li Mingfu , Fang Ching-Chieh , Ferng Huei-Wen
Published: March 2022

To improve the performance of the wireless powered sensor network (WPSN), this paper proposes a frequency division duplex (FDD)-based on-demand energy transfer protocol and an energy-aware polling-based medium access control (MAC) protocol, called composite energy and data first (CEDF), by using the numbers of data packets and energy packets to determine polling priorities. The performance of the proposed MAC protocol, i.e., CEDF, along with the on-demand energy transfer protocol was evaluated through simulations, with comparison to the closely related protocols such as the round robin (RR) and data first (DF) polling protocols. Compared with RR and DF, our proposed CEDF performs much better in terms of throughput, data packet loss rate, and delay. Additionally, the doubly near–far problem in WPSNs under our proposed on-demand energy transfer protocol and CEDF was investigated to come up with good solutions to alleviate such a problem.
Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing
Authors: Jin Sunghyun , Johansson Philip , Kim HeeSeok , Hong Seokhie
Published: March 2022

Side-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simultaneously is a general and cost-efficient approach to counteracting side-channel analysis. With the development of side-channel countermeasures, there are plenty of advanced attacks introduced to defeat such countermeasures. At CARDIS 2013, Belgarric et al. first proposed time-frequency analysis, a promising attack regarding the complexity of computation and memory compared to other attacks, such as conventional second-order side-channel analysis after synchronization. Nevertheless, their time-frequency analysis seems to have lower performance than expected against some datasets protected by combined countermeasures. It is therefore required to study the factors that affect the performance of time-frequency analysis. In this paper, we investigate Belgarric et al.’s time-frequency analysis and conduct a mathematical analysis in regard to the preprocessing of frequency information for second-order side-channel analysis. Based on this analysis, we claim that zero-mean preprocessing enhances the performance of time-frequency analysis. We verify that our analysis is valid through experimental results from two datasets, which are different types of first-order masked Advanced Encryption Standard (AES) software implementations. The experimental results show that time-frequency analysis with zero-mean preprocessing seems to have an enhanced or complementary performance compared to the analysis without preprocessing.
How Do People Decide on Getting Vaccinated? Evaluating the COVID-19 Vaccination Program through the Lens of Social Capital Theory
Authors: Bernados Simeon , Ocampo Lanndon
Published: March 2022

The COVID-19 pandemic has disrupted economies at a scale unprecedented in recent history, and vaccination is deemed the only option to ultimately halt its spread. However, vaccine hesitancy remains a global issue that must be addressed. If left unaddressed, it will impede the recovery of both the economy and public health following the pandemic. To better understand the issue, on the premise that individuals have layers of social ties to achieve common goals, social capital theory is proposed to examine the social connections associated with vaccine acceptance. A case study of the Republic of the Philippines’ vaccination program, in the form of an online survey involving 430 participants, was conducted using logistic binomial regression to analyze the data. Findings reveal that people’s vaccination decisions are influenced by their perceptions of their social ties regarding the vaccines, such as the perceptions one’s social ties’ trust in the vaccines, safety of use, benefits vaccines can offer, the role of media in information dissemination, and the influence of social networks. Insights regarding these findings are also discussed.
Doctors’ Perceptions of Multiracial Adolescents
Authors: Herman Melissa
Published: March 2022

Perceptions of race influence the way health care is delivered to minority populations, particularly multiracial people. Currently only 11% of American physicians are Black or Latinx, making the chances slim that Black and Latinx patients will be treated by a co-ethnic. White supremacy is well documented in the medical literature—along with efforts to combat it—yet medical education and health care are still largely based on studies by and about white people. This privileging of whiteness is increasingly evident as the COVID-19 pandemic has shone a renewed spotlight on disparities in health and care in the U.S. The current study considers how doctors perceive race and use it to make healthcare decisions. Among the (N = 509) doctors in this sample, few said they considered the patient’s race. Of those who did, many determined race by some method other than asking the patient directly. The most common methods were observing skin tone and observing cultural cues. Doctors perceived nearly half the multiracial targets as monoracial. Targets who followed the norm of hypodescent were significantly more likely to be identified congruently by doctors. Targets who identified at the extremes of the color spectrum (black and white) were easier to identify, while the beige tones in the middle of the spectrum were more challenging. Patient ancestry was the most relevant criterion in explaining the congruence of racial identification between patients and doctors, while characteristics of the doctors were nearly irrelevant.
Intersectional Organizing and Educational Justice: How Lived Experience Influences Community Organizers’ Understanding and Practice of Intersectional Organizing
Authors: Ortiz-Wythe Bianca , Warren Mark R. , King Andrew R.
Published: March 2022

Recently, education organizers working with youth and parents have taken intersectional approaches. Little research, however, considers how personal experience informs these understandings and the approaches organizers take. The purpose of this study is to understand how social locations like gender identity and race inform organizers’ understanding and practice of intersectional organizing. We interviewed eight community, parent, and youth organizers with a variety of racial and gender identities. The organizers are members of the People’s Think Tank (PTT), an idea and strategy space that includes organizers and activist scholars working together to strengthen and expand the educational justice movement. We found three different practices of intersectional organizing. One subgroup of organizers understands intersectional organizing as a tool for interrogating power and privilege. A second subgroup of organizers understands intersectional organizing as centering the lives of the most marginalized. Finally, the third subgroup of organizers stated that terms like “interconnected” and “intergenerational” are preferred terms when talking about intersectional organizing. Our analysis finds that an individuals’ lived experience impacts how they understand intersectional organizing and that engagement in intersectional organizing helps individuals better understand their social identities. We discuss the relationship between these distinct approaches and how they relate to intersectional organizing.
Effect of Fluid Contact Angle of Oil-Wet Fracture Proppant on the Competing Water/Oil Flow in Sandstone-Proppant Systems
Authors: Wang Ming , Guo Boyun
Published: March 2022

Ceramic fracture proppants are extensively used for enhancing the recovery of fossil energy and geothermal energy. Previous work has reported the attracting-oil-repelling-water (AORW) property of oil-wet proppants at the faces of fractures. Because of the lack of a method for measuring the contact angle of proppant packs, the terms water-wet proppant and oil-wet proppant were defined based on observations of liquid droplets on the surfaces of proppant packs without quantitative measurement. An innovative method was developed in this study to determine the contact angles of fracture proppant packs. The effect of the oil contact angle of the oil-wet fracture proppant pack on the competing water/oil flow from sandstone cores to the packs was investigated. It was found that, for a given fracture proppant pack, the sum of the water contact angle and oil contact angle measured in the liquid–air–solid systems is less than 180°, i.e., the two angles are not supplementary. This is believed to be due to the weak wetting capacity of air to the solid surfaces in the liquid–air–solid systems. Both water and oil contact angles should be considered in the classification of wettability of proppant packs. Fracture proppant packs with water contact angles greater than 90° and oil contact angles significantly less than 90° can be considered as oil-wet proppants. Reducing oil contact angles of oil-wet proppants can increase capillary force, promote oil imbibition into the proppant packs, and thus improve the AORW performance of proppants. Fracture proppant packs with water contact angles less than 90° and oil contact angles less than 90° may be considered as mixed-wet proppants. Their AORW performance should be tested in laboratories before they are considered for well fracturing operations.
The Effect of ESG Performance on Corporate Innovation in China: The Mediating Role of Financial Constraints and Agency Cost
Authors: Tang Hua
Published: March 2022

The effectiveness of environmental, social, and corporate governance (ESG) performance has been widely discussed and is often linked to corporate financial performance or firm value by academics and practitioners. However, a significant research gap remains unexplored; specifically, prior scholars have ignored path research about the effect of ESG performance on corporate innovation, and they have also ignored the impacts of the heterogeneity of stakeholders. Therefore, taking China’s A-share listed companies as an example, the research applied linear regressions with panel data, using the ESG rating of SynTao Green Finance Agency as a proxy variable of ESG performance. The results show that ESG performance significantly promotes the quantity and quality of corporate innovation and is mediated by alleviating the financial constraints and agency cost. Internal and external governance plays different roles; the higher institutional investors’ attention as an external governance form does not help enterprises improve the quantity and quality of corporate innovation; however, CEO duality as an internal governance form strengthens the effect of ESG performance on corporate innovation. This study provides scientific evidence for the effect and effect path of ESG performance on promoting proactive innovation based on sustainable development in China; furthermore, the study reveals the heterogeneity factors of ESG performance on the innovation effect under stakeholder theory.
A Biophilic Design Approach for Improved Energy Performance in Retrofitting Residential Projects
Authors: Nitu Maliha Afroz , Gocer Ozgur , Wijesooriya Niranjika , Vijapur Diksha , Candido Christhina
Published: March 2022

The existing building stock is recognised as a major contributor to total energy consumption and related carbon emissions around the globe. There is increased attention on the retrofit of existing building stock, especially residential buildings, as a way of curbing energy consumption and carbon emissions. Within this context, human nature connectedness (HNC) has the potential of further amplifying the benefits of sustainable buildings both from an energy conservation practice and tangible improvements to users’ satisfaction, health, and wellbeing. This study attempts to show a case study of the potential of using HNC through the adoption of biophilic design principles to improve a residential building performance. A terrace house located in Sydney, NSW, was used as a case study and proposed retrofit scenarios were simulated with DesignBuilder® and Rhinoceros/Grasshopper with a view of improved daylighting, thermal comfort, and energy consumption. The building performance is improved in terms of daylighting, thermal comfort, and reduced energy consumption, additionally enhancing HNC.
Impact of Urban Re-Densification on Indoor Lighting Demand and Energy Poverty on the Equator, in the City of Quito
Authors: Montes-Villalva Estefanía , Pereira-Ruchansky Lucía , Piderit-Moreno Beatriz , Pérez-Fargallo Alexis
Published: March 2022

Human wellbeing and their quality of life is linked to daylight. However, this is being hindered by the rapid growth of cities, promoted by regulatory frameworks and the interests of property developers that seek high-rise densification and re-densification of certain urban areas, jeopardizing access to daylight. This article proposes a methodology to evaluate the impact of urban re-densification on indoor lighting demand in high-rise buildings in Ecuador and its relationship with energy poverty. It analyzes the urban and building features of Quito, considering the location conditions of buildings and using simulation tools to explore solar irradiance reductions on the façade. It also analyzes increases in lighting demand, while determining the extreme conditions, considering an increase in energy consumption, the average salary, and the Ten Percent Rule. The results show that daylight obstructions and umbral cones generated when facing a high-rise re-densification scenario in the city reduce daylight by between 40% and 80%, generating increases of between 2% and 498% in lighting demand when compared to an unobstructed scenario. These re-densification scenarios may cause significant social problems associated with energy poverty. In conclusion, according to the Ten Percent Rule, buildings should be limited to four stories for streets under 10 m wide, between four and six stories for those between 10 and 14 m, and between six and nine stories for streets that are between 14 and 18 m wide. This research seeks to help public policy developers in making future decisions about risks that are currently not considered in urban planning and that may contradict sustainable development goals.
Balancing Social and Economic Sustainability in Renovation with an Affordable Option for Tenants? A Pilot Study from Sweden
Authors: Mjörnell Kristina , von Platten Jenny , Björklund Kicki
Published: March 2022

A public housing company has applied a new renovation strategy, comprising no standards raising and thus rent-raising measures, in 20% of its apartments. Prior to renovation, the tenants were given the opportunity to choose renovation options involving different standards and costs after renovation. The purpose of the study is to follow up and give feedback on the renovation strategy. The aim was to evaluate implementation of the strategy in practice using a case study, in terms of the tenants’ opportunity to influence and the housing company’s profitability. To follow up, two methods were used: a survey of the tenants’ perception of choosing renovation options, and a financial assessment of the profitability based on the renovation cost and rent increase for different choice scenarios. The results from the survey show that the tenants appreciate being able to choose between different renovation options as it gives them the opportunity to decide on their housing costs and standard. With more than half of the tenants choosing the maintenance option involving a very low rent increase, the dividend yield will not be high enough to make the renovation profitable, but if only 20% had chosen the maintenance option, the dividend yield would be more feasible in the long run.
Research on the Effect of the Rural Wealth-Leader on Poverty Reduction of Farmers—Based on the Questionnaire Survey in China
Authors: Li Xuexiang , Zhang Zhenghe , Li Binbin
Published: March 2022

The rural wealth-leader is one of the 10 projects of targeted poverty alleviation in the period of poverty alleviation. As the “leading wild goose” in rural areas, the personal characteristics of the rural wealth-leader directly affect the effect of stabilizing poverty and increasing income of poor farmers. Based on the survey of the rural wealth-leader in 10 cities and 21 counties, this paper systematically analyzes the path of poverty reduction by the rural wealth-leader, combs out the key individual characteristics that affect farmers’ poverty reduction, and puts forward a research hypothesis. Through empirical analysis, the research hypothesis is tested and the influencing mechanism is analyzed. The model results show that: The rural wealth-leader has a significant effect on increasing farmers’ income and eliminating poverty. The rural wealth-leader with high education level and engaged in e-commerce has a significant effect on increasing farmers’ income. Party members and the skilled rural wealth-leaders have a significant impact on rural households’ poverty alleviation. The income increasing effect of culture and education is negatively affected by corporate loans, and the income increasing effect of e-commerce is negatively affected by corporate profits. Therefore, we should improve the profit distribution mode of e-commerce related enterprises to avoid farmers being marginalized in the process of e-commerce development. The skills, management and financial knowledge training of the rural wealth-leaders should be strengthened to improve their knowledge and skills. Meanwhile, supporting fiscal, financial and tax policies should be improved.
An Interval-Valued Intuitionistic Fuzzy Model Based on Extended VIKOR and MARCOS for Sustainable Supplier Selection in Organ Transplantation Networks for Healthcare Devices
Authors: Salimian Sina , Mousavi Seyed , Antucheviciene Jurgita
Published: March 2022

The selection of proper healthcare device suppliers in sustainable organ transplantation networks has become an essential topic of increasing life expectancy. Assessment of sustainable healthcare device suppliers can be regarded as a complex multi-criteria decision-making (MCDM) problem that consists of multiple alternative solutions with sustainable criteria. For this reason, this paper proposes a new integrated MCDM model based on combining an extended vlsekriterijuska optimizacija i komoromisno resenje (E-VIKOR) and measurement alternatives and ranking according to the compromise solution (MARCOS) approaches under interval-valued intuitionistic fuzzy sets (IVIFSs). The aggregating technique of the E-VIKOR method is a strong point of this method compared to the original approach. The IVIFS is taken to cope with the uncertain situation of real-world applications. In this regard, an IVIF-similarity measure is introduced to compute weights of the decision-makers (DMs). The IVIF-Shannon entropy method is utilized to calculate the criteria weights, and a new hybrid proposed model is developed by presenting the IVIF-E-VIKOR method and IVIF-MARCOS, to calculate the ranking of sustainable supplier alternatives in organ transplantation networks to supply the surgery devices. Afterward, an illustrative example is introduced to evaluate the performance of the proposed model, and a comparative analysis is presented to confirm and validate the proposed approach. Moreover, sensitivity analysis for essential parameters of the proposed model is performed to assess their effects on outcomes.
The Perceived Restorative Quality of Viewing Various Types of Urban and Rural Scenes: Based on Psychological and Physiological Responses
Authors: Li Chang , Yuan Yu , Sun Changan , Sun Minkai
Published: March 2022

Attention restoration theory argues that the type of visual scene is important; however, related research is mostly based on a dichotomous comparison between natural and urban environments. Few studies have evaluated complex scenes comprising both natural and artificial elements. Therefore, we compared the differences between four types of environments: urban artificial scenes, urban natural scenes, rural artificial scenes, and rural natural scenes—using a survey based on the Perceived Restorativeness Scale (PRS), perception complexity scoring, and eye tracking. Participants (N = 119) viewed photographs in a random order. The results showed significant differences between the visual landscape scores and eye-tracking data for each type of visual image: PRS, perception complexity, average fixation duration, and mean pupil size. Rural natural scenes had a higher restoration effect than the other scenes. Waterscapes and well-maintained vegetation had positive correlations between the typical landscape element indices and restorative benefits in different scene types. Contrastingly, weeds and hardscapes showed negative correlations, which can be attributed to the maintenance of these typical elements. The harmony of elements with circumstances in a scene was a key factor. The results provide a reference for urban and rural landscape planning and design to improve perceived restorative quality.
Multi-Hazard Housing Safety Perceptions of Those Involved with Housing Construction in Puerto Rico
Authors: Goldwyn Briar , Javernick-Will Amy , Liel Abbie B.
Published: March 2022

Globally, hazards are increasingly threatening housing each year, and housing constructed outside the formal sector may be particularly vulnerable. Yet, limited studies have investigated the perceptions of those responsible for designing and building this housing. These safety perceptions motivate the informal housing construction practices that ultimately determine housing safety. Thus, this study investigates the multi-hazard housing safety perceptions of individuals involved with housing construction in Puerto Rico. We surveyed 345 builders and hardware store employees across Puerto Rico to understand their perceptions of expected housing damage in hurricanes and earthquakes, important mitigation measures, and barriers to safer housing construction. Our results reveal that prior hazard experience did not influence perceptions of expected housing damage, but previous housing construction experience did. Respondents viewed wood and concrete housing as less safe in hurricanes and earthquakes, respectively. Yet, respondents appeared uncertain about the importance of mitigation measures for concrete houses in earthquakes, likely due to a combination of limited earthquake experience and “hidden” reinforcement detailing in a reinforced concrete house. Interestingly, our results also show that respondents perceive technical construction capacity as a major barrier to safer informal housing construction rather than resource constraints alone. These findings suggest areas for technical construction capacity development for Puerto Rico’s informal construction sector.
Coupling of Urban Economic Development and Transportation System: An Urban Agglomeration Case
Authors: Hu Yaqi , Chen Yingzi
Published: March 2022

Urban agglomeration is a new carrier of regional economic development, whose spatial structure can be reflected by the transportation system. The coordination between urban economic development and the transportation system is conducive to promoting balanced urban economic development. As an important urban cluster of China, the Harbin-Changchun urban agglomeration plays an important role in promoting the revitalization of northeast China. Targeting 11 cities of the Harbin-Changchun urban agglomeration, this paper adopts the coupling coordination degree model to study the coordination level of urban economic development and the transportation system. The results show that large differences exist among the cities, with Changchun at the outstanding position. A more developed transportation system exists in the western Harbin-Changchun urban agglomeration, while the east is in a worse condition. The coupling coordination degree of the urban economic development and transportation system shows obvious stratification. Further adjusting the industrial structure, expanding the degree of opening to the outside world, and increasing investment in transportation technological innovation are recommended to promote an integrated development pattern in the Harbin-Changchun urban agglomeration.