bluuebunny/crossref_metadata_2025 huggingface dataset:
Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
Authors: Sun Yifan , Chen Feiqiang , Lu Zukun , Wang Feixue
Published: September 2022

Global navigation satellite system (GNSS) array antenna receivers are effective for suppressing wideband jamming. However, its anti-jamming performance decreases sharply when the number of wideband interference surpasses the number of array elements. Since a large number of jammers are often used in navigation countermeasures, it is crucial to keep array antenna receivers available in such conditions. Aiming at this issue, two main tasks were performed in this research and are presented in this paper: Firstly, the direction sensitivity of the sup-freedom anti-jamming performance is revealed and an anti-jamming method for array antenna receivers based on antenna rotation is proposed. Secondly, in order to determine the optimal rotation angle rapidly, a variable-step iteration algorithm based on gradient descent is proposed. Theoretical analysis and simulation show the effectiveness of the proposed anti-jamming method and the efficiency of the implementation algorithm. In a typical airborne scenario with a maximum azimuth difference of 90°, the anti-jamming ability of the proposed method improved by 21~26 dB and 5~10 dB for arrays adopting the PI (power inversion) and MVDR (minimum variance distortionless response) algorithms, respectively. The iterative efficiency improved by 78.35–99.63% in comparison with a traversal of 0.1° search resolution. The proposed method and algorithm are not limited to airborne scenarios and they might be influential to anti-jamming algorithms in the data domain.
Urban Flood Risk Assessment in Zhengzhou, China, Based on a D-Number-Improved Analytic Hierarchy Process and a Self-Organizing Map Algorithm
Authors: Wu Zening , Xue Wanjie , Xu Hongshi , Yan Denghua , Wang Huiliang , Qi Wenchao
Published: September 2022

Flood risk assessment is an important tool for disaster warning and prevention. In this study, an integrated approach based on a D-number-improved analytic hierarchy process (D-AHP) and a self-organizing map (SOM) clustering algorithm are proposed for urban flooding risk assessment. The urban flood inundation model and geographic information system (GIS) technology were used to quantify the assessment indices of urban flood risk. The D-AHP approach was adopted to determine the weights of the indices, which effectively makes up for the shortcomings of the AHP in dealing with uncertain evaluation information (such as fuzzy and incomplete information). In addition, the SOM clustering algorithm was applied to determine the flood risk level. It is a data-driven approach that avoids the subjective determination of a flood risk classification threshold. The proposed approach for flood risk assessment was implemented in Zhengzhou, China. The flood risk was classified into five levels: highest risk, higher risk, medium risk, lower risk, and the lowest risk. The proportion of the highest risk areas was 9.86%; such areas were mainly distributed in the central and eastern parts of the Jinshui District, the eastern part of the Huiji District, and the northeastern part of the Guancheng District, where there were low terrain and serious waterlogging. The higher risk areas accounted for 24.26% of the study area, and were mainly distributed in the western and southern parts of the Jinshui District, the southern part of the Huiji District, the middle and eastern parts of the Zhongyuan District, the northeastern part of the Erqi District, and the northwestern part of the Guancheng District, which consisted of economically developed areas of dense population and buildings, matching well with historical flooding events. To verify the effectiveness of the proposed approach, traditional approaches for risk assessment were compared. The comparison indicated that the proposed approach is more reasonable and accurate than the traditional approaches. This study showed the potential of a novel approach to flood risk assessment. The results can provide a reference for urban flood management and disaster reduction in the study area.
The Approximate Analytical Solution for the Top-of-Atmosphere Spectral Reflectance of Atmosphere—Underlying Snow System over Antarctica
Authors: Kokhanovsky Alexander
Published: September 2022

The analytical solutions of the radiative transfer equation are needed for the solution of various applied atmospheric and snow optics problems. In this paper, we propose a simple analytical equation for the top-of-atmosphere (TOA) spectral reflectance. To simplify the problem under study we consider the case of Antarctica, where both snow and atmosphere are almost free of pollutants. This work is focused on the simulation of the moderate spectral resolution TOA measurements (1 nm or so) and the spectral range 400–1000 nm. The values of the coefficient of variance (CV) between the measured by the Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A and modelled spectra are smaller than 10% for most cases in Antarctica. There are regions in Eastern Antarctica, where the values of CV are smaller than 5%. The areas with larger deviations between measured and retrieved spectra could be due to the presence of clouds or structures on the snow surface not captured by the proposed model.
The Effects of Rainfall on Over-the-Horizon Propagation in the Evaporation Duct over the South China Sea
Authors: Yang Fan , Yang Kunde , Shi Yang , Wang Shuwen , Zhang Hao , Zhao Yaming
Published: September 2022

The evaporation duct (ED) is generated by the evaporation of seawater and can be an influential factor of electromagnetic (EM)-wave propagation. Rainfall also affects atmospheric factors and EM-wave propagation. However, the distribution of the ED and path loss (PL) during rainfall has rarely been reported. This paper analyzes the distribution of the atmospheric factors and ED in the South China Sea (SCS). The results show that the evaporation duct height (EDH) in the area of rainfall is generally lower. The effect of the ED on the over-the-horizon (OTH) propagation reaches 0.69 dB km−1 on average, which is 4.3 times stronger than the maximum rain attenuation (0.16 dB km−1) when the rainfall is less than 5 mm h−1. In the SCS, a 53 km long OTH link was established between Donghai Island and Jizhao Bay to observe the PL. The measurement results show that the nearly saturated relative humidity (RH) leads to a high PL. The results also show that the change in the direction of the sea–land breeze causes a 42.4 dB decrease of PL by transferring the moist patches. Rainfall has an attenuation effect on OTH propagation in ED, mainly owing to the high RH.
Spatial Patterns of Errors in GPM IMERG Summer Precipitation Estimates and Their Connections to Geographical Features in Complex Topographical Area
Authors: Li Rui , Tang Shunxian , Shi Zhao , He Jianxin , Shi Wenjing , Li Xuehua
Published: September 2022

Error evaluation is essential for the improvement and application of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) Version 06 daily precipitation estimates, including early-, late-, and final-run products (IMERG-DE, IMERG-DL, and IMERG-DF, respectively), especially for summer precipitation in complex topographical areas. However, many existing works mainly focus on comparing the error statistical metrics of precipitation estimates, but few further analyze the internal relationships between these error statistics and geographical features. Therefore, taking Sichuan Province of China as a case study of the complex topographic and mountainous area, we adopt statistical metrics, error decomposition schemes, systematic and random error separation models, and regression methods to analyze the relationships between the spatial distribution of IMERG summer precipitation error metrics and geographical features. These features include longitude, latitude, distance from Sichuan Basin edge (DFBE), digital elevation model (DEM), normalized difference vegetation index (NDVI), slope, aspect, and topographic position index (TPI). The results show that: (1) DEM and DFBE are the two most important geographical features affecting the spatial distribution of error metrics, while both aspect and TPI have negligible effects on these metrics; (2) the variations in DEM, DFBE, and latitude have the negative relationships with error metrics; (3) longitude and DFBE do not have a direct impact on the errors, but indirectly affect the precipitation errors through the changing DEM; (4) slope shows a strong negative correlation with hit bias, and its increase significantly amplifies the sensitivity of systematic errors of hit bias from IMERG-DE and DL; and (5) the high detection probability and small missed precipitation error of the three IMERG estimates are virtually unaffected by changes in geographical features.
Evaluation of the Influence of Field Conditions on Aerial Multispectral Images and Vegetation Indices
Authors: Änäkkälä Mikael , Lajunen Antti , Hakojärvi Mikko , Alakukku Laura
Published: September 2022

Remote sensing is a method used for monitoring and measuring agricultural crop fields. Unmanned aerial vehicles (UAV) are used to effectively monitor crops via different camera technologies. Even though aerial imaging can be considered a rather straightforward process, more focus should be given to data quality and processing. This research focuses on evaluating the influences of field conditions on raw data quality and commonly used vegetation indices. The aerial images were taken with a custom-built UAV by using a multispectral camera at four different times of the day and during multiple times of the season. Measurements were carried out in the summer seasons of 2019 and 2020. The imaging data were processed with different software to calculate vegetation indices for 10 reference areas inside the fields. The results clearly show that NDVI (normalized difference vegetation index) was the least affected vegetation index by the field conditions. The coefficient of variation (CV) was determined to evaluate the variations in vegetation index values within a day. Vegetation index TVI (transformed vegetation index) and NDVI had coefficient of variation values under 5%, whereas with GNDVI (green normalized difference vegetation index), the value was under 10%. Overall, the vegetation indices that include near-infrared (NIR) bands are less affected by field condition changes.
Near Infrared Emitting Semiconductor Polymer Dots for Bioimaging and Sensing
Authors: Riahin Connor , Mendis Kushani , Busick Brandon , Ptaszek Marcin , Yang Mengran , Stacey Gary , Parvate Amar , Evans James E. , Traeger Jeremiah , Hu Dehong , Orr Galya , Rosenzweig Zeev
Published: September 2022

Semiconducting polymer dots (Pdots) are rapidly becoming one of the most studied nanoparticles in fluorescence bioimaging and sensing. Their small size, high brightness, and resistance to photobleaching make them one of the most attractive fluorophores for fluorescence imaging and sensing applications. This paper highlights our recent advances in fluorescence bioimaging and sensing with nanoscale luminescent Pdots, specifically the use of organic dyes as dopant molecules to modify the optical properties of Pdots to enable deep red and near infrared fluorescence bioimaging applications and to impart sensitivity of dye doped Pdots towards selected analytes. Building on our earlier work, we report the formation of secondary antibody-conjugated Pdots and provide Cryo-TEM evidence for their formation. We demonstrate the selective targeting of the antibody-conjugated Pdots to FLAG-tagged FLS2 membrane receptors in genetically engineered plant leaf cells. We also report the formation of a new class of luminescent Pdots with emission wavelengths of around 1000 nm. Finally, we demonstrate the formation and utility of oxygen sensing Pdots in aqueous media.
A Trade-Off Analysis between Sensor Quality and Data Intervals for Prognostics Performance
Authors: Park Hyung Jun , Kim Nam Ho , Choi Joo-Ho
Published: September 2022

In safety-critical systems such as industrial plants or aircraft, failure occurs inevitably during operation, and it is important to prevent it in order to maintain high availability. To reduce this risk, a lot of efforts are directed from developing sensing technologies to failure prognosis algorithms to enable predictive maintenance. The success of effective and reliable predictive maintenance not only relies on robust prognosis algorithms but also on the selection of sensors or data acquisition strategy. However, there are not many in-depth studies on a trade-off between sensor quality and data storage in the view of prognosis performance. The information about (1) how often data should be measured and (2) how good sensor quality should be for reliable failure prediction can be highly impactful for practitioners. In this paper, the authors evaluate the efficacy of the two factors in terms of remaining useful life (RUL) prediction accuracy and its uncertainty. In addition, since knowing true degradation information is almost impossible in practice, the authors validated the use of the prognosis metric without requiring the true degradation information. A numerical case study is conducted to identify the relationship between sensor quality and data storage. Then, real bearing run-to-failure (RTF) datasets acquired from accelerometer (contact type) and microphone (non-contact type) sensors are evaluated based on the prognosis performance metric and compared in terms of the sensors’ cost-effectiveness for predictive maintenance.
A Sensitive Frequency Range Method Based on Laser Ultrasounds for Micro-Crack Depth Determination
Authors: Li Haiyang , Jiang Wenxin , Deng Jin , Yu Ruien , Pan Qianghua
Published: September 2022

The laser ultrasonic method using the characteristics of transmitted Rayleigh waves in the frequency domain to determine micro-crack depth is proposed. A low-pass filter model based on the interaction between Rayleigh waves and surface cracks is built and shows that the stop band, called the sensitive frequency range, is sensitive to the depth of surface cracks. The sum of transmission coefficients in the sensitive frequency range is defined as an evaluated parameter to determine crack depth. Moreover, the effects of the sensitive frequency range and measured distance on the evaluated results are analyzed by the finite-element method to validate the robustness of this depth-evaluating method. The estimated results of surface cracks with depths ranging from 0.08 mm to ~0.5 mm on the FEM models and aluminum-alloy samples demonstrate that the laser ultrasounds using the characteristics of Rayleigh waves in the frequency domain do work for quantitative crack depth.
An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model
Authors: Muhetaer Nuerbiye , Nurmemet Ilyas , Abulaiti Adilai , Xiao Sentian , Zhao Jing
Published: September 2022

Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI3 (R2 = 0.656) shows the highest correlation, and it is followed by indexes ORSDI1 (R2 = 0.642), ORSDI4 (R2 = 0.628), and ORSDI2 (R2 = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.
The Development of a Digital Twin Framework for an Industrial Robotic Drilling Process
Authors: Farhadi Ahmad , Lee Stephen K. H. , Hinchy Eoin P. , O’Dowd Noel P. , McCarthy Conor T.
Published: September 2022

A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic reference model is proposed to highlight elements of the digital twin relevant to robotic drilling. Then, a precise reference digital twin architecture model is developed, based on available standards and technologies. Finally, real-time visualisation of drilling process parameters is demonstrated as an initial step towards implementing a digital twin of a robotic drilling process.
Evaluation of Effective Class-Balancing Techniques for CNN-Based Assessment of Aphanomyces Root Rot Resistance in Pea (Pisum sativum L.)
Authors: Divyanth L. G. , Marzougui Afef , González-Bernal Maria Jose , McGee Rebecca J. , Rubiales Diego , Sankaran Sindhuja
Published: September 2022

Aphanomyces root rot (ARR) is a devastating disease that affects the production of pea. The plants are prone to infection at any growth stage, and there are no chemical or cultural controls. Thus, the development of resistant pea cultivars is important. Phenomics technologies to support the selection of resistant cultivars through phenotyping can be valuable. One such approach is to couple imaging technologies with deep learning algorithms that are considered efficient for the assessment of disease resistance across a large number of plant genotypes. In this study, the resistance to ARR was evaluated through a CNN-based assessment of pea root images. The proposed model, DeepARRNet, was designed to classify the pea root images into three classes based on ARR severity scores, namely, resistant, intermediate, and susceptible classes. The dataset consisted of 1581 pea root images with a skewed distribution. Hence, three effective data-balancing techniques were identified to solve the prevalent problem of unbalanced datasets. Random oversampling with image transformations, generative adversarial network (GAN)-based image synthesis, and loss function with class-weighted ratio were implemented during the training process. The result indicated that the classification F1-score was 0.92 ± 0.03 when GAN-synthesized images were added, 0.91 ± 0.04 for random resampling, and 0.88 ± 0.05 when class-weighted loss function was implemented, which was higher than when an unbalanced dataset without these techniques were used (0.83 ± 0.03). The systematic approaches evaluated in this study can be applied to other image-based phenotyping datasets, which can aid the development of deep-learning models with improved performance.
Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines
Authors: Kuo Chung-Feng Jeffrey , Wang Wei-Ren , Barman Jagadish
Published: September 2022

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.
Naphthalene Detection in Air by Highly Sensitive TiO2 Sensor: Real Time Response to Concentration Changes Monitored by Simultaneous UV Spectrophotometry
Authors: Torres Jorge H. , Rosa Vincent A. , Barreto Patricia D. , Barreto Jose C.
Published: September 2022

Volatile low-weight polycyclic aromatic hydrocarbons (PAHs) are known to be potentially toxic to humans and animals. Their detection in ambient air has been of great interest in recent years and various detection methods have been implemented. In this study, we used naphthalene as a basic model of such compounds and constructed our own version of a titanium oxide-based sensor system for its detection. The main goal of the study was to clearly demonstrate the effectiveness of this type of sensor, record its response under well-controlled conditions, and compare that response to concentration measurements made by the widely accepted spectrophotometric method. With that goal in mind, we recorded the sensor response while monitoring naphthalene vapor concentrations down to 95 nM as measured by spectrophotometry. Air flow over the sensor was passed continuously and sample measurements were made every 3 min for a period of up to 2 h. Over that period, several cycles of naphthalene contamination and cleaning were implemented and measurements were recorded. The relative humidity and temperature of the air being sampled were also monitored to assure no major variations occurred that could affect the measurements. The sensor showed high sensitivity and a reproducible response pattern to changes in naphthalene concentration. It could be easily “cleaned” of the compound in ten minutes by means of the application of UV light and the passing of fresh air. Pending testing with other volatile PAH, this type of sensor proves to be an effective and inexpensive way to detect naphthalene in air.
Moral Panic over Fake Service Animals
Authors: Sorenson John , Matsuoka Atsuko
Published: September 2022

We use Stanley Cohen’s moral panic framework to examine concerns about fake service animals and to illuminate processes of intersectionality that shape our social relations and systems. Applying Critical Animal Studies and Critical Disability Theory, we examine media reports about fake service animals in North America to explore how these anxieties constitute a moral panic, the interests at work, and underlying ideology that motivates outrage about animals considered to be out of place. We found that classifying other animals as legitimate or not affects those animals but also impacts humans. The findings indicate that speciesist representations and restrictions imposed on nonhuman animals maintain ongoing discrimination against humans with disabilities. The study reveals how speciesism sustains ableism and advances particular economic interests. Thus, we encourage expanding research ontology to examine speciesist power relations in intersectional analysis to dismantle ableist oppressive relationships and achieve trans-species social justice (social justice beyond humans).
Creating Futuristic Heritage Experiences: An Exploratory Co-Design Study through Design Fiction
Authors: Zhao Shichao
Published: September 2022

This study adopted design fiction and co-design as the two main methodological approaches for designers and artists to explore the complex relationships and intersections of futuristic heritage experience and engagement. A literature-based seminar and a co-design workshop were conducted with 26 participants from multidisciplinary backgrounds, via Miro Board. A series of collaborative design practices and science fiction films were utilised as key media for propelling thinking, raising awareness or questions, provoking actions, inaugurating discussions, and offering alternatives necessary for a heritage setting. Additionally, this study reflects upon the complexities of using design fiction activities to re-think the technological future of heritage engagement and experience.
No Place for Pointless Jobs: How Social Responsibility Impacts Job Performance
Authors: Scholten Marc , Correia Manuela Faia , Esteves Teresa , Gonçalves Sónia P.
Published: September 2022

We address the question of how organizations’ practices of social responsibility impact their employees’ job performance. Independent studies have shown that job performance is influenced by how employees perceive the organization they work for and how they perceive the work they perform for the organization. Moreover, studies on the relationship between social responsibility and job performance have shown that employees’ perceptions of their organization mediate the relationship. What is thus far neglected, however, is whether and how their perceptions of work itself mediate the relationship as well. We derive a sequential mediation model according to which social responsibility improves job performance by contributing to a supportive and trustworthy work context (employees’ perceptions of the organization they work for), in turn promoting work meaningfulness and engagement (employees’ perceptions of work itself). We collect survey data and test the sequential mediation model against a series of alternative models, each of which challenges a specific assumption of the proposed model. Our model provides the best tradeoff between the accuracy and the parsimony with which it describes the data collected, and is, therefore, expected to generalize best to other data.
A Q-Learning-Based Approximate Solving Algorithm for Vehicular Route Game
Authors: Zhang Le , Lyu Lijing , Zheng Shanshui , Ding Li , Xu Lang
Published: September 2022

Route game is recognized as an effective method to alleviate Braess’ paradox, which generates a new traffic congestion since numerous vehicles obey the same guidance from the selfish route guidance (such as Google Maps). The conventional route games have symmetry since vehicles’ payoffs depend only on the selected route distribution but not who chose, which leads to the precise Nash equilibrium being able to be solved by constructing a special potential function. However, with the arrival of smart cities, the real-time of route schemes is more of a concerned of engineers than the absolute optimality in real traffic. It is not an easy task to re-construct the new potential functions of the route games due to the dynamic traffic conditions. In this paper, compared with the hard-solvable potential function-based precise method, a matched Q-learning algorithm is designed to generate the approximate Nash equilibrium of the classic route game for real-time traffic. An experimental study shows that the Nash equilibrium coefficients generated by the Q-learning-based approximate solving algorithm all converge to 1.00, and still have the required convergence in the different traffic parameters.
Are Citizens Ready for Active Climate Engagement or Stuck in a Game of Blame? Local Perceptions of Climate Action and Citizen Participation in Chilean Patagonia
Authors: Sapiains Rodolfo , Azócar Gabriela , Moraga Pilar , Valenzuela Catalina , Aldunce Paulina , Cornejo Camilo , Rojas Maisa , Pulgar Antonio , Medina Loreto , Bozkurt Deniz
Published: September 2022

Deep structural transformations aimed at strengthening climate action and community participation are occurring in Chile, especially after the social unrest of October 2019. The ongoing political crisis has even generated the unprecedented possibility of writing a new constitution through an entirely democratic process. This article explores to what extent these structural transformations are also associated with cognitive and relational changes in the population, especially in terms of community participation. An online survey (n = 1.117) was applied to people over 18 years old in Punta Arenas in November 2020. This is the southernmost city of the American continent, one of the areas most affected by climate change, highly isolated from the rest of the country, and with a strong regional ecological identity. Results show that climate change is perceived as the main environmental problem affecting the city, with multiple negative consequences, but also with some potentially positive impacts. At the same time, environmental and constitutional expectations suggest the state of the environment is deemed to be critical for the future of the city. However, a traditional top-down understanding of community participation still prevails as most participants perceive the citizens’ role in dealing with environmental issues as limited to individual, passive, and reactive actions, or reduced to being responsible consumers. These results show that transforming institutions, rules and regulations alone does not guarantee a broader engagement of local communities in more ambitious, committed, and lasting climate action, even with a high climate change concern in the population. Creating strategies aimed at more profound cognitive and relational changes from a bottom-up perspective will also be necessary to avoid negative transformation trajectories.
Research Perceived Competency Scale: A New Psychometric Adaptation for University Students’ Research Learning
Authors: Merino-Soto César , Fernández-Arata Manuel , Fuentes-Balderrama Jaime , Chans Guillermo M. , Toledano-Toledano Filiberto
Published: September 2022

This research aimed to adapt and validate a measuring scale of perceived research competencies among undergraduate students. Perceived research competencies of undergraduate learning can be measured with a new scale adapted from self-determination theory. We assessed the validity of this new measure applied to 307 participating undergraduates from Lima (Peru). The instrument’s survey items in the perceived competencies scale were first translated from English to Spanish and then adapted to focus on participation in research activities. We obtained evidence for (a) content validity (through item analysis), (b) internal structure with Mokken Scaling Analysis and structural equation modeling to examine the item–construct relationship, differential item functioning, and reliability, and (c) association with external variables. The items were found to function one-dimensionally, with strong item–construct relationships and no differential functioning (academic semester and general self-esteem groups). Theoretically consistent associations were found between study satisfaction and anxiety symptoms (controlling for gender, semester, and social support). We also discussed the theoretical implications and practices of this newly adapted measurement instrument.
The Perception Scale for the 7E Model-Based Augmented Reality Enriched Computer Course (7EMAGBAÖ): Validity and Reliability Study
Authors: Erçağ Erinç , Yasakcı Aykut
Published: September 2022

Education, teaching, and learning topics, known to have gained an international dimension with technological developments, are still seen as the most discussed themes and subject to change. It is clear that in the 21st century, the increasing information density, the means of transfer, and the technological adaptation skills of the teacher and the learner are at the forefront, and more efforts are required to develop them. The integration of technologies in education and training is related to the necessity of preparing learners in the most suitable way for future work and lifelong learning within the information society. For this reason, for the COVID-19 pandemic process and thereafter, starting with known education models makes it necessary to enable the development of education, teaching, and learning under better conditions and situations by blending them with technological developments. Everyone has understood the ever-changing and developing universal digital world much better during this pandemic. The 7E model of the Constructivist Learning Theory (CLT), known as the student-centered model based on distance education, has been mandatory for the entire education community during the first global pandemic of the digital age. Augmented Reality (AR) is another web-based technological development that can work in harmony with the 7E model. In the 7E model, the teaching of the lessons was at the forefront since the learners learn by doing, experiencing, and applying, directly participating in the lesson, and sharing opinions. For the present study, a scale was developed to determine the perceptions of the learners about the 7E model-based AR-enriched computer lesson. Validity and reliability studies were also conducted on the data obtained from the developed scale. The scale, which was prepared using a five-point Likert scale, was applied to 400 students who fit the profile of the sample group. A statistical analysis of the results concluded that 26 low-factor loading items should be removed from the questionnaire, and the final version of the 28-item scale was a six-factor structure. The statistical analysis concluded that the scale was suitable for all criteria in terms of validity and reliability. Considering the values revealed in the study, it was concluded that the overall scale (α = 0.932) was highly reliable.
Reliability Analysis on Multiple Failure Modes of Underground Chambers Based on the Narrow Boundary Method
Authors: Yin Huadong , Zhang Daobing , Zhang Jiahua , Yu Biao , Yuan Xiaomeng
Published: September 2022

This paper proposes to study the stability of underground chambers while taking into account the nonlinear characteristics of geotechnical materials and pore water. According to the upper bound theorem of nonlinear limit analysis and the reliability theory, the failure mode and reliability model of underground chambers are established considering the pore water effect. The upper bound solution expression of the surrounding rock pressure in the underground chamber is deduced. The variation law of the surrounding rock pressure is analyzed under different parameters. At the same time, based on the narrow boundary method considering the correlation of multiple failure modes, the influence of different random parameters on the failure probability and reliability index of underground chambers is studied. The results show that the water-level line height, pore water pressure coefficient and Hoek-Brown failure criterion parameter have significant effects on the surrounding rock pressure and underground chamber reliability. In addition, the concept of the safety level is introduced, and the minimum-support force range of the underground chamber with safety level 1 is obtained under different random parameters. The research results can provide a theoretical basis and reference for the structural safety evaluation of underground chambers.
Youth Awareness and Attitudes towards a Circular Economy to Achieve the Green Deal Goals
Authors: Krajnc Damjan , Kovačič Darko , Žunec Eva , Brglez Kristijan , Kovačič Lukman Rebeka
Published: September 2022

In order to approach a system change towards a circular economy model (CE) in Europe, the entrepreneurial activities of young people need to be encouraged to achieve the Green Deal (GD) goals. This article presents the evaluation results of young people’s awareness and attitudes towards the CE and the GD. The data used in this study was collected by surveying 286 young people from five European countries. The questionnaire covered four areas: (1) belief, (2) knowledge, (3) competence, and (4) young people’s ability to deal with the CE. Descriptive and exploratory factor analysis (EFA) was conducted to analyse the questionnaire on the basic factors that motivate young people to the CE and GD. A literature review was conducted to identify the main research concepts and connections. A review included two phases, the first encompassing a search through the Web of Science (WoS) Core Collection for research papers meaningful for further research, followed by Leximancer analysis. The analysis showed that young people believe in the principles and priorities of CE but do not receive enough support to participate and implement the priorities actively. Formal education does not provide enough knowledge to work in this field actively. They are primarily undecided about their understanding of the principles of CE. Responses to whether they can implement the project, business, or entrepreneurial ideas are also uninspiring. Young people do not see themselves as creators of change towards the CE. However, it is encouraging that they approach the CE from different angles and show responsible behaviour in everyday life. Young people’s ability to reason, think, evaluate, connect, and create new solutions for the CE is not enviable. Most respondents did not comment on their competence in concepts of the GD and CE. It is encouraging to know that they have already heard about the CE’s goals and action plan. Young people do not show significant skills in the practical implementation of CE principles. The analysis has shown that young people are particularly unable to reason, think, and evaluate in an entrepreneurial way when linking the CE to business opportunities. Relating them to business models and entrepreneurship also seems to be problematic. As a result, the majority of them are unable to design, develop, and create new business solutions.
Application of the InTIME Methodology for the Transition of Office Buildings to Low Carbon—A Case Study
Authors: Andrade Isabel , Land Johann , Gallardo Patricio , Krumdieck Susan
Published: September 2022

The COP21 Paris Agreement requires urgent abatement of 80% of the current fossil-based energy consumption to keep global warming below dangerous levels. Heating loads in commercial buildings can be reduced by retrofitting the building envelope, upgrading the efficiency of heating equipment, implementing energy management strategies, substituting renewable energy sources, and influencing energy-saving behavior. However, achieving the downshift of gas or coal heat is a wicked problem. The Interdisciplinary Transition Innovation Management and Engineering (InTIME) methodology was applied to address the wicked problem of district heating of campus buildings of the University of Canterbury, in Christchurch, New Zealand. The carbon downshift scenario requires a reduction in coal purchase by 80% from the first year through the engineering of adaptive measures for facility operators and occupants. Accordingly, a successful downshift of fossil-fuel energy would depend on the effective adaptation of the office workers. Adaptation plans to facilitate demand participation and sustained worker productivity could be designed once the actual heating behaviour is known. The contribution of this work is a novel fossil fuel abatement concept: the Targeted Heating Energy—Assessment and Intervention Design (THE-AID), which focuses on the assessment of the heating behavioural patterns of office workers. Building services engineers can use the THE-AID concept to develop adaptation plans through intervention design and resource facilitation focused on building occupants. THE-AID projects could achieve significant emissions reduction in the near term at a low cost and increase resilience to heat supply disruptions.
Autonomous Innovations in Rural Communities of Developing Countries II—Causal Network and Leverage Point Analyses of Transformations
Authors: Takemura Shion , Tajima Hidetomo , Hori Juri , Makino Mitsutaku , Matewere John Banana , Rampisela Dorothea Agnes , Sato Tetsu
Published: September 2022

Solving complex system challenges such as natural resource management in social-ecological systems (SESs) is fraught with great uncertainty. To cope with these challenges, socially vulnerable people in developing countries have created various autonomous innovations. This study focuses on the concept of leverage point (LP) to understand the emergence of innovations and the transformation mechanism of SESs. An LP is a part of a complex system where a small change can cause transformations in the entire system and can be identified from causal networks in an SES. This study analyzed the emergence processes of autonomous innovations as causal networks through transdisciplinary collaboration with innovators in communities and succeeded in visualizing the initial conditions, outcomes, and challenges. We constructed a new definition of LPs based on graph theory and classified LPs into three types based on their characteristics. The network analysis of the causal networks of two innovations in developing countries revealed that the three types of LPs functioned synthetically in complex systems to promote transformation. Based on these results, we propose the potential ways of interventions for the transformation of complex system networks including plural LPs and discuss their effectiveness as boundary objects in transdisciplinary processes collaborating with diverse stakeholders. Further research is expected to accumulate knowledge for solving the various challenges faced by SESs.
The Ecological Footprint of Happiness: A Case Study of a Low-Income Community in the City of São Paulo, Brazil
Authors: Giannetti Biagio F. , De Souza Rose Reis , Alves-Pinto Marcos J. , Almeida Cecília M. V. B. , Agostinho Feni , Coscieme Luca
Published: September 2022

An ecological footprint is an accounting tool that reports the balance between resource supply and demand to assess environmental sustainability. Among the many available indicators of social progress, happiness reflects how a person feels about their quality of life. We combined these two approaches to assess the ecological efficiency of social performance in the low-income community of Felicidade, in São Paulo, Brazil, in 2019. We assessed the ecological footprint and gross domestic happiness (GDH) through questionnaires. We found that the community has a lower environmental footprint than higher-income communities in Brazil. However, the per capita ecological footprint in the community is still above what is available per person globally. We found that the community has a high level of life satisfaction (GDH = 0.86) and that the main contributor to happiness is health, time use, psychological wellbeing, education, good governance, and community vitality. The results suggest that other contributors unrelated to income are more robust determinants of happiness. In Brazil, despite higher footprints characterizing higher-income communities, further efforts in low-income communities are needed to reduce environmental footprints, ensure dignified income, and nurture the underlying conditions for high levels of happiness and social capital.
Spatial Equity Priority Modeling of Elementary and Middle Schools through GIS Techniques, El-Taif City, Saudi Arabia
Authors: Ramadan Mona S. , Khairy Nesren , Alogayell Haya M. , Alkadi Ibtesam I. , Ismail Ismail Y. , Ramadan Rasha H.
Published: September 2022

Spatial equity in the delivery of educational services is a critical element in creating healthful and joyful living circumstances in cities. The spatial distribution of public elementary and middle schools for girls in El-Taif city was investigated in this study using a variety of tools. Mean Center, Central Feature, Standard Distance, Directional Distribution, Point Density, Kernel Density, Nearest Neighbor Analysis, Ripley’s K Function, Moran Index, Buffer Zone, and Hotspot analysis are spatial techniques in Geographic Information Systems, which were used to analyze and show the spatial distribution of current public elementary and middle schools for females. Furthermore, the sufficiency and/or shortage of elementary and middle schools according to the criteria of the Ministry of Municipal and Rural Affairs (MOMRA) were studied to establish the regions with shortage or overcapacity. The findings reveal that the city’s population and public elementary and middle school numbers were not spread equally. Some districts had an overabundance and concentration of elementary and middle schools, particularly in older, fully developed, and densely inhabited districts, while most new north and eastern districts had a scarcity of schools. Furthermore, half of the districts lack public elementary and middle schools. Consequently, the study finished by developing a spatial equality priority model for public elementary and middle schools to find and highlight problem areas that require future corrective action and are a priority for spatial equality. In order to contribute to achieving goal 4 of the UN-Habitat Sustainable Development Goals, which aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all”, the model recommended that decision-makers supply elementary schools and middle schools in districts where a shortage was present and improve the equal distribution of elementary schools around the city.
How Design Technology Improves the Sustainability of Intangible Cultural Heritage Products: A Practical Study on Bamboo Basketry Craft
Authors: Sun Yan , Liu Xiaojian
Published: September 2022

The sustainability problem of many intangible cultural heritage (ICH) products stems from the shrinking of the core practitioner group, which is also the case for bamboo basketry craft. We believe that the problem in bamboo basketry originated in the lack of labor division between design and manufacturing, which prevents professional designers from entering this industry and results in the absence of several key stakeholders related to innovation and R&D. The lack of labor division is due to the technical difficulties associated with expressing the design concepts. The complexity of basket weaving structures makes it difficult to communicate between designer and manufacturer without precise expression tools, thus binding design and manufacturing into an integrated role. Guided by the user innovation theory, our team studied the design technology of bamboo basketry and developed a series of aiding tools, including the modeling of basic over–under structures and free weaving structures, automatic mapping techniques from 2D to 3D and several frequently used weaving skills, such as connecting, wrapping, plaiting and knotting. This technology enables designers to quickly design and express weaving structures with full details in digital models rather than to make samples. The application of the software shows that the technology considerably improved the designer interest and confidence. This technical solution makes designers, rather than programmers, able to do the development work, which also helps to create a sustainable ecological environment of technological research, also avoiding the difficulties associated with attracting business investment for such niche demands in the starting stage. Our practice shows that the sustainability of ICH products and the sustainability of the industry are closely related and that solving the latter supports the former.