bluuebunny/crossref_metadata_2025 huggingface dataset:
Interpretable Machine Learning Methods for Monitoring Polymer Degradation in Extrusion of Polylactic Acid
Authors: Munir Nimra , McMorrow Ross , Mulrennan Konrad , Whitaker Darren , McLoone Seán , Kellomäki Minna , Talvitie Elina , Lyyra Inari , McAfee Marion
Published: August 2023

This work investigates real-time monitoring of extrusion-induced degradation in different grades of PLA across a range of process conditions and machine set-ups. Data on machine settings together with in-process sensor data, including temperature, pressure, and near-infrared (NIR) spectra, are used as inputs to predict the molecular weight and mechanical properties of the product. Many soft sensor approaches based on complex spectral data are essentially ‘black-box’ in nature, which can limit industrial acceptability. Hence, the focus here is on identifying an optimal approach to developing interpretable models while achieving high predictive accuracy and robustness across different process settings. The performance of a Recursive Feature Elimination (RFE) approach was compared to more common dimension reduction and regression approaches including Partial Least Squares (PLS), iterative PLS (i-PLS), Principal Component Regression (PCR), ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). It is shown that for medical-grade PLA processed under moisture-controlled conditions, accurate prediction of molecular weight is possible over a wide range of process conditions and different machine settings (different nozzle types for downstream fibre spinning) with an RFE-RF algorithm. Similarly, for the prediction of yield stress, RFE-RF achieved excellent predictive performance, outperforming the other approaches in terms of simplicity, interpretability, and accuracy. The features selected by the RFE model provide important insights to the process. It was found that change in molecular weight was not an important factor affecting the mechanical properties of the PLA, which is primarily related to the pressure and temperature at the latter stages of the extrusion process. The temperature at the extruder exit was also the most important predictor of degradation of the polymer molecular weight, highlighting the importance of accurate melt temperature control in the process. RFE not only outperforms more established methods as a soft sensor method, but also has significant advantages in terms of computational efficiency, simplicity, and interpretability. RFE-based soft sensors are promising for better quality control in processing thermally sensitive polymers such as PLA, in particular demonstrating for the first time the ability to monitor molecular weight degradation during processing across various machine settings.
Development of Silk Fibroin-Based Non-Crosslinking Thermosensitive Bioinks for 3D Bioprinting
Authors: Lee Juo , Park Sangbae , Lee Sungmin , Kweon Hae Yong , Jo You-Young , Kim Jungsil , Chung Jong Hoon , Seonwoo Hoon
Published: August 2023

Three-dimensional (3D) bioprinting holds great promise for tissue engineering, allowing cells to thrive in a 3D environment. However, the applicability of natural polymers such as silk fibroin (SF) in 3D bioprinting faces hurdles due to limited mechanical strength and printability. SF, derived from the silkworm Bombyx mori, is emerging as a potential bioink due to its inherent physical gelling properties. However, research on inducing thermosensitive behavior in SF-based bioinks and tailoring their mechanical properties to specific tissue requirements is notably lacking. This study addresses these gaps through the development of silk fibroin-based thermosensitive bioinks (SF-TPBs). Precise modulation of gelation time and mechanical robustness is achieved by manipulating glycerol content without recourse to cross-linkers. Chemical analysis confirms β-sheet conformation in SF-TPBs independent of glycerol concentration. Increased glycerol content improves gelation kinetics and results in rheological properties suitable for 3D printing. Overall, SF-TPBs offer promising prospects for realizing the potential of 3D bioprinting using natural polymers.
Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network
Authors: Jia Yuanxin , Zhang Xining , Xiang Ru , Ge Yong
Published: August 2023

With the development of agricultural and rural modernization, the informatization of rural roads has been an inevitable requirement for promoting rural revitalization. To date, however, the vast majority of road extraction methods mainly focus on urban areas and rely on very high-resolution satellite or aerial images, whose costs are not yet affordable for large-scale rural areas. Therefore, a deep learning (DL)-based super-resolution mapping (SRM) method has been considered to relieve this dilemma by using freely available Sentinel-2 imagery. However, few DL-based SRM methods are suitable due to these methods only relying on the spectral features derived from remote sensing images, which is insufficient for the complex rural road extraction task. To solve this problem, this paper proposes a spatial relationship-informed super-resolution mapping network (SRSNet) for extracting roads in rural areas which aims to generate 2.5 m fine-scale rural road maps from 10 m Sentinel-2 images. Based on the common sense that rural roads often lead to rural settlements, the method adopts a feature enhancement module to enhance the capture of road features by incorporating the relative position relation between roads and rural settlements into the model. Experimental results show that the SRSNet can effectively extract road information, with significantly better results for elongated rural roads. The intersection over union (IoU) of the mapping results is 68.9%, which is 4.7% higher than that of the method without fusing settlement features. The extracted roads show more details in the areas with strong spatial relationships between the settlements and roads.
Laboratory Characterisation of a Commercial RGB CMOS Camera for Measuring Night Sky Brightness
Authors: Fiorentin Pietro , Bertolo Andrea , Cavazzani Stefano , Ortolani Sergio
Published: August 2023

The use of RGB cameras in photometric applications has grown over the last few decades in many fields such as industrial applications, light engineering and the analysis of the quality of the night sky. In this last field, they are often used in conjunction with a Sky Quality Meter (SQM), an instrument used for the measurement of night sky brightness (NSB), mainly when there is a significant amount of artificial light at night (ALAN). The performances of these two instruments are compared here. A simple source composed of nine narrowband LEDs in an integrating sphere was used to excite the two instruments and therefore measure the spectral responsivity of the SQM and of the three channels of the camera. The estimated uncertainties regarding spectral responsivity were less than 10%. A synthetic instrument approximating the SQM’s responsivity can be created using a combination of the R, G and B channels. The outputs of the two instruments were compared by measuring the spectral radiance of the night sky. An evaluation of the spectral mismatch between the two instruments completed the analysis of their spectral sensitivity. Finally, the measurements of real SQMs in four sites experiencing different levels of light pollution were compared with the values obtained by processing the recorded RGB images. Overall, the analysis shows that the two instruments have significantly different levels of spectral responsivity, and the alignment of their outputs requires the use of a correction which depends on the spectral distribution of the light coming from the sky. A synthetic SQM will always underestimate real SQM measures; an average correction factor was evaluated considering nine sky spectra under low and medium levels of light pollution; this was determined to be 1.11 and, on average, compensated for the gap. A linear correction was also supposed based on the correlation between the NSB levels measured by the two instruments; the mean squared error after the correction was 0.03 mag arcsec−2.
Infrared Small Target Detection Based on a Temporally-Aware Fully Convolutional Neural Network
Authors: Zhang Lei , Han Peng , Xi Jiahua , Zuo Zhengrong
Published: August 2023

In the field of computer vision, the detection of infrared small targets (IRSTD) is a crucial research area that plays an important role in space exploration, infrared warning systems, and other applications. However, the existing IRSTD methods are prone to generating a higher number of false alarms and an inability to accurately locate the target, especially in scenarios with low signal-to-noise ratio or high noise interference. To address this issue, we proposes a fully convolutional-based small target detection algorithm (FCST). The algorithm builds on the anchor-free detection method FCOS and adds a focus structure and a single aggregation approach to design a lightweight feature extraction network that efficiently extracts features for small targets. Furthermore, we propose a feature refinement mechanism to emphasize the target and suppress conflicting information at multiple scales, enhancing the detection of infrared small targets. Experimental results demonstrate that the proposed algorithm achieves a detection rate of 95% and a false alarm rate of 2.32% for IRSTD tasks. To tackle even more complex scenarios, we propose a temporally-aware fully convolutional infrared small target detection (TFCST) algorithm that leverages both spatial and temporal information from sequence images. Building on a single-frame detection network, the algorithm incorporates ConvLSTM units to extract spatiotemporal contextual information from the sequence images, boosting the detection of infrared small targets. The proposed algorithm shows fast detection speed and achieves a 2.73% improvement in detection rate and an 8.13% reduction in false alarm rate relative to the baseline single-frame detection networks.
Towards the Mitigation of Discrepancies in Sea Surface Parameters Estimated from Low- and High-Resolution Satellite Altimetry
Authors: Buchhaupt Christopher K. , Egido Alejandro , Vandemark Douglas , Smith Walter H. F. , Fenoglio Luciana , Leuliette Eric
Published: August 2023

In this study, we present an extension to existing numerical retrackers of synthetic-aperture radar (SAR) altimetry signals. To our knowledge at the time of writing this manuscript, it offers the most consistent retrieval of geophysical parameters compared to low-resolution mode (LRM) retracking results. We achieve this by additionally estimating the standard deviation of vertical wave-particle velocities σv and a new parameter ux, linked to a residual Doppler in the returned radar echoes, which can be related to wind speed and direction. Including this new parameter into the SAR stack retracker mitigates sea surface height estimation errors by up to two centimeters for Sentinel-6MF SAR mode results. Additionally, we found a closed-form equation to describe ux as a function of eastward and northward wind variables, which allows mitigating the effects of this parameter on a SAR stack within level 1B processing and generating a lookup table to correct sea surface height estimates in SAR mode. This additionally opens up the door to estimating the wind speed and direction from SAR altimetry stacks. Additionally, we discuss how this new retracker performs with respect to different planned future baseline processor changes of Sentinel-6MF, namely F09 and F10, by attempting to imitate their level 2 processing. This is achieved by processing cycles 017 to 051 (nearly a full year) of Sentinel-6MF level 1A data on a global scale. We observe that the new retracking method is, on average, more accurate with respect to LRM. However, there is a slight increase in measurement noise due to the introduction of an additional parameter. To ensure that the results of the new retracker are not biased, we retrack using both the new method and the SINCS-OV ZSK retracker on Sentinel-6MF stack data produced in a Monte Carlo simulation. We analyze the simulation results with respect to accuracy, precision, and correlations between estimated parameters. We show that the accuracy of the new retracker is better than SINCS-OV ZSK but less precise, which could be related to higher correlation coefficients—especially with respect to the new parameter ux—between estimated parameters.
UCTNet with Dual-Flow Architecture: Snow Coverage Mapping with Sentinel-2 Satellite Imagery
Authors: Ma Jinge , Shen Haoran , Cai Yuanxiu , Zhang Tianxiang , Su Jinya , Chen Wen-Hua , Li Jiangyun
Published: August 2023

Satellite remote sensing (RS) has been drawing considerable research interest in land-cover classification due to its low price, short revisit time, and large coverage. However, clouds pose a significant challenge, occluding the objects on satellite RS images. In addition, snow coverage mapping plays a vital role in studying hydrology and climatology and investigating crop disease overwintering for smart agriculture. Distinguishing snow from clouds is challenging since they share similar color and reflection characteristics. Conventional approaches with manual thresholding and machine learning algorithms (e.g., SVM and Random Forest) could not fully extract useful information, while current deep-learning methods, e.g., CNNs or Transformer models, still have limitations in fully exploiting abundant spatial/spectral information of RS images. Therefore, this work aims to develop an efficient snow and cloud classification algorithm using satellite multispectral RS images. In particular, we propose an innovative algorithm entitled UCTNet by adopting a dual-flow structure to integrate information extracted via Transformer and CNN branches. Particularly, CNN and Transformer integration Module (CTIM) is designed to maximally integrate the information extracted via two branches. Meanwhile, Final Information Fusion Module and Auxiliary Information Fusion Head are designed for better performance. The four-band satellite multispectral RS dataset for snow coverage mapping is adopted for performance evaluation. Compared with previous methods (e.g., U-Net, Swin, and CSDNet), the experimental results show that the proposed UCTNet achieves the best performance in terms of accuracy (95.72%) and mean IoU score (91.21%) while with the smallest model size (3.93 M). The confirmed efficiency of UCTNet shows great potential for dual-flow architecture on snow and cloud classification.
GNSS-5G Hybrid Positioning Based on Joint Estimation of Multiple Signals in a Highly Dependable Spatio-Temporal Network
Authors: Liu Jingrong , Deng Zhongliang , Hu Enwen , Huang Yunfei , Deng Xiwen , Zhang Zhichao , Ding  Zhenke , Liu Bingxun
Published: August 2023

The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.
Investigating Expectancy Values in Online Apparel Rental during and after the COVID-19 Pandemic: Moderating Effects of Fashion Leadership
Authors: Seo Sukyung , Watchravesringkan Kittichai , Swamy Uma , Lang Chunmin
Published: August 2023

Online apparel renting has become a popular type of consumption. However, the COVID-19 pandemic has disrupted the sharing economy, including online renting. This cross-sectional study examined the effects of expectancy values on intention towards online apparel rental during and after the COVID-19 pandemic and investigated the moderating role of personal traits of fashion leadership. A total of 431 valid samples from college students in the US were collected. Structural equation modeling (SEM) was conducted to test the hypothesized relationships, and moderation analysis was performed to test the moderating effects of fashion leadership. The results indicated that expectancy values affecting consumer intention toward online apparel renting varied during and post-pandemic. The results also indicated that fashion leadership moderated the links between relative advantage and intention to rent apparel online during the pandemic, while it moderated the relationships between compatibility and psychological ownership and intention toward online apparel rental after the pandemic. This study sheds light on the expectancy values that shape consumers’ intentions to rent apparel online, considering moderating effects of fashion leadership from which strategic marketing and communication plans could be developed to enhance consumer experience and engagement and expand the online apparel rental market.
Sustainability Reporting during the Crisis—What Was Disclosed by Companies in Response to the COVID-19 Pandemic Based on Evidence from Poland
Authors: Janik Agnieszka , Ryszko Adam
Published: August 2023

This study presents the results of sustainability reporting investigations in the context of the COVID-19 pandemic. It is based on a content analysis of 297 reports published by 161 companies operating in Poland in the 2020–2021 period. This study covers specific disclosures on the impact of the pandemic on economic conditions and business activity of companies, corporate environmental performance, the safety and well-being of employees, and companies’ community and stakeholder engagement. Moreover, an analysis is conducted of disclosures on changes in business strategies and environmental activities undertaken to respond to the pandemic, initiatives improving the safety and well-being of employees, and companies’ social involvement during the COVID-19 crisis. Our study demonstrates that ensuring the safety and well-being of employees was the highest priority in the analyzed sustainability reports. Furthermore, the investigated companies exerted considerable efforts to harmonize adverse economic challenges created by the pandemic with altruistic actions manifested in active and wide-ranging social involvement addressed to local communities and primary stakeholders during the period under analysis. We believe that our study offers intriguing insights into disclosures on sustainability issues during the COVID-19 pandemic and addresses the call for research on the sustainability initiatives that emerged during that time to help companies remain resilient and sustainable.
Hyperconnected Logistic Platform for Heavy-Duty Machinery: Leveraging Physical Internet Principles to Drive the Composting Industry
Authors: Cichocki Max , Barenji Ali V. , Montreuil Benoit , Landschützer Christian
Published: August 2023

The Physical Internet (PI) envisions a global logistics system that integrates physical, digital and operational connections. This study aims to develop a hyperconnected logistic platform for heavy-duty machinery (HDM) in the composting industry by utilizing a systematic methodology. The proposed architecture consists of four layers: the Domain Model, the MBSE Model, the Information Sharing Model and the Agent-based Simulation Platform. The Domain Model analyzes the current situation and investigates stakeholder viewpoints, and the MBSE Model reduces complexity and describes mutual interactions between requirements and needs. The Information Sharing Model focuses on the information exchange among the main components, and the Agent-based Simulation Platform implements the proposed platform. The feasibility of the proposed architecture is demonstrated through a use case in Styria, Austria. Three simulation-based scenarios are analyzed, starting from the semi-hyperconnected approach up to the hyperconnected approach with PI vision integration. The results indicate that the hyperconnected platform is successful in serving all composting facilities, leveraging underutilized resources and promoting high-quality compost production. Thus, the platform provides support in a local, communal setting, resulting in enhancing the circular economy within the composting sector. Our efforts aim to contribute to the realization of the Physical Internet vision and promote composting to ultimately achieve a more sustainable future.
Functional End-Use of Hemp Seed Waste: Technological, Qualitative, Nutritional, and Sensorial Characterization of Fortified Bread
Authors: Sciacca Fabiola , Virzì Nino , Pecchioni Nicola , Melilli Maria Grazia , Buzzanca Carla , Bonacci Sonia , Di Stefano Vita
Published: August 2023

Due to its multipurpose usability, short production cycle, and low capital requirement in cultivation, hemp represents an excellent material applicable to the Sustainable Development Goals (SDGs) defined by the United Nations Organization as a strategy “to achieve a future better and more sustainable for all”. Hemp seeds represent the only edible part of Cannabis sativa and have a distinctly different nutritional composition from other representative foods such as rice and wheat (high protein content, low carbohydrate content, polyunsaturated fatty acids, dietary fiber, and gluten-free). Hemp seeds are mainly used for the production of oil; the waste obtained after extraction, reduced to a fine powder and rich in bioactive components, is added to durum wheat flour and used for the preparation of fortified bread. The aim of this study was to use varying percentages of hemp seed flour for bread production and determine the impact of fortification on texture, organoleptic characteristics, crumb color, changes in crumb texture, total polyphenols, the scavenging activity of free radicals, and amino acid content. The solid residue remaining after oil extraction from hemp seeds (generally discarded as waste or added to feed) was triturated and sieved to 0.530 mm (Hemp 1) or 0.236 mm (Hemp 2). Samples of fortified bread were obtained by replacing variable percentages of durum wheat semolina with the two hemp flours (5%, 7.5%, and 10%). The total phenolic content of the fortified bread was between 0.73 and 1.73 mg GAE/g, and the antiradical activity was between 1.17 and 3.18 mmol TEAC/100 g on the basis of the growing fortification. A comparison of Ciclope semolina bread with hemp flour-enriched bread showed a large increase in amino acid content in the fortified samples. In particular, bread enriched with 10% hemp flour 2 showed a higher content of glutamic acid, tyrosine, proline, and essential amino acids such as leucine and isoleucine compared to other samples with the same percentage of substitution. The amount of hemp seed flour influenced the color of the crumb by increasing the yellow index from 18.24 (100% Ciclope) to 21.33 (bread with 5% hemp flour 2). The results of the sensory analysis were very good, demonstrating the high acceptability of fortified breads at higher percentages.
A Simplified Finite Element Model for Seismic Analysis of a Single-Layer Reticulated Dome Coupled with an Air Duct System
Authors: Zhang Tianlong , Zhou Yuwei
Published: August 2023

Damage to nonstructural components, such as air ducts, in buildings during earthquakes, which are more fragile than single-layer reticulated domes, has a significant impact on the sustainability of the building’s functionality. To study the coupling effect and failure mode of a single-layer reticulated dome with an air duct system, then, simplified finite element models of air ducts and flange bolt joints were established and validated against the solid element model. Moreover, the simplified finite element models of support hangers were also built and validated against the existing experiment. Three kinds of support hanger layout schemes were studied to analyze the dynamic characteristics and seismic responses of a single-layer reticulated dome with an air duct system from earthquakes at different intensities. The results showed that the simplified finite element model can effectively simulate the coupling effect and failure mode of the single-layer reticulated dome with an air duct system. The coupling effect of the air duct system reduces the natural vibration frequency in the dome and increases the number of damaged members in the dome by strong earthquakes. The rate of falling air ducts with all the seismic support hangers is the highest compared to the two other support hanger layout schemes.
Delineating the Dichotomy and Synergistic Dynamics of Environmental Determinants on Temporally Responsive Park Vitality
Authors: Zeng Liguo , Liu Chunqing , Wang Mo , Zhou Chengling , Xie Guanhong , Wu Binsheng
Published: August 2023

Promoting park vitality is fundamental for advancing both residents’ well-being and sustainable urban development. Current research often sidesteps the temporal fluctuations and combined effects of environmental factors on park vitality. Drawing on real-time user density data from Tencent, this investigation analyzed park vitality across 64 urban parks in Fuzhou, China, divided into five specific temporal periods on weekdays and weekends. Through the application of geographic detector models, this study examined the impact and interplay of both intrinsic and extrinsic environmental characteristics on park vitality over these different times. Our primary findings include: (1) environmental attributes affecting park vitality vary temporally, with aspects like commercial density, leisure facility density, and park size consistently influencing vitality; (2) on weekdays, external attributes linked to convenience are predominant, while on weekends, internal attributes connected to recreation take precedence; and (3) there is a synergetic interaction between environmental determinants, often leading to either additive or more intricate effects on park vitality. Based on these insights, we propose recommendations for spatial planning and time-based policies to enhance the alignment between urban settings and park quality. This research provides actionable strategies for enhancing park vitality, both within China and internationally.
An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration
Authors: Zhao Haoteng , Di Liping , Guo Liying , Zhang Chen , Lin Li
Published: August 2023

Given the increasing prevalence of droughts, unpredictable rainfall patterns, and limited access to dependable water sources in the United States and worldwide, it has become crucial to implement effective irrigation scheduling strategies. Irrigation is triggered when some variables, such as soil moisture or accumulated water deficit, exceed a given threshold in the most common approaches applied in irrigation scheduling. A High-Resolution Land Data Assimilation System (HRLDAS) was used in this study to generate timely and accurate soil moisture and evapotranspiration (ET) data for irrigation management. By integrating HRLDAS products and the crop growth model (AquaCrop), an automated data-driven irrigation scheduling approach was developed and evaluated. For HRLDAS ET and soil moisture, the ET-water balance (ET-WB)-based method and soil-moisture-based method were applied accordingly. The ET-WB-based method showed a 10.6~33.5% water-saving result in dry and set seasons, whereas the soil moisture-based method saved 7.2~37.4% of irrigation water in different weather conditions. Both of these methods demonstrated good results in saving water (with a varying range of 10~40%) without harming crop yield. The optimized thresholds in the two approaches were partially consistent with the default values from the Food and Agriculture Organization and showed a similar trend in the growing season. Furthermore, the forecasted rainfall was integrated into this model to see its water-saving effect. The results showed that an additional 10% of irrigation water, which is 20~50%, can be saved without harming the crop yield. This study automated the data-driven approach for irrigation scheduling by taking advantage of HRLDAS products, which can be generated in a near-real-time manner. The results indicated the great potential of this automated approach for saving water and irrigation decision making.
Green Household Technology and Its Impacts on Environmental Sustainability in China
Authors: Meng Qin , Zhang Jing-Wen , Wang Yunxu , Chang Hsu-Ling , Su Chi-Wei
Published: August 2023

China has made a commitment to achieve carbon neutrality by 2060, and promoting a green lifestyle is an essential means to this end. The primary aim of this study is to investigate the asymmetric impact of green household technology on environmental sustainability in China. To that end, we have employed linear and non-linear auto-regressive distributed lag models to identify this complicated effect. The empirical results suggest that green household technology’s positive change exerts significant and negative effect on carbon emission in the short and long terms. And the impacts of green household technology’s negative change on carbon emission are significantly negative but smaller than its positive change in the long run, while insignificant in the short term. The estimates endorse the asymmetric impact of green household technology on carbon emissions both in the short and long term. This finding suggests that the improvement of green household technology can reduce carbon emissions, while a decline in it causes carbon emissions to rise, and technological retrogression plays a less influential role than its development. This research is a groundbreaking point in discussing the way towards environmental sustainability from a green household technology perspective, which considers the asymmetric effect and provides meaningful insights for China to achieve sustainable development.
Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization
Authors: Chang Daniel H. , Lin Michael Pin-Chuan , Hajian Shiva , Wang Quincy Q.
Published: August 2023

The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.
The Social Construction of Living Space: The Role of Place Attachment and Neighbourhood Perception
Authors: Mesa-Pedrazas Ángela , Torrado José Manuel , Duque-Calvache Ricardo
Published: August 2023

This study provides a comprehensive assessment of the social construction of living spaces by considering commonly studied variables such as sociodemographic characteristics, socioeconomic factors, and residential location. Additionally, it tests the significance of deeper sociological factors, specifically place attachment, bonds with the local community, and perceptions of the neighbourhood. An analysis, based on a description of the spatial structure of daily activities globally, the construction of living space dispersion indices, and a stepwise linear regression model, identified three types of living spaces: commuting spaces, self-realization spaces, and spaces of care, with different concentration and dispersion patterns. Commuting spaces are typically larger; self-realization takes place in the neighbourhood but is also frequently dispersed across multiple locations; and spaces of care are heavily concentrated. The analysis of subpopulations reveals distinct living space patterns based on two main factors: work and children. However, there are important differences in the age and gender composition of the subpopulations. The concentration/dispersion of living spaces is mainly driven by sociodemographic and socioeconomic factors, but place attachment measures significantly affect their construction. The results encourage further exploration of the subjective, experienced dimension of urban phenomena, going beyond the concept of “living spaces” to think of them as “lived-in spaces”.
Key Success Factors for the Practical Application of New Geomaterials
Authors: Alelvan Giovanna Monique , Santos Hugo Naves Coelho , Pierozan Rodrigo Cesar , Oliveira Leise Kelli de
Published: August 2023

Geomaterials comprise naturally formed materials through geological processes, such as soils and rocks, or artificially processed materials, including mineral waste and geosynthetics. These materials find extensive use in geotechnical structures, such as slopes, dams, and pavements, among others. However, two issues commonly arise in earthworks: the materials available in the region do not meet the minimum engineering requirements, resulting in high transportation costs, and the exploitation of new deposits increases environmental impacts. Consequently, there is a need to develop stabilization and reinforcement techniques aimed at creating new geomaterials (NGs) to expand the range of local material applications. In this context, the present study evaluates the key success factors (KSFs) related to the application of NGs in geotechnical structures. The Delphi method was employed through a structured questionnaire developed after an extensive literature review. Brazilian experts from the public, private, and academic sectors were selected to identify the obstacles and potential pathways for the practical application of NGs. The outcomes of the study indicated that the lack of standardization, the complex behavior of geomaterials under varying conditions, as well as technical and economic limitations serve as barriers impeding the widespread adoption of NGs. Finally, a roadmap proposal was devised, encompassing a series of actions intended to facilitate the broader utilization of NGs.
Spatial Diversity and Impact of Selected Factors on Women’s Labour Force Participation Rate in Poland during 2000–2020
Authors: Zienkiewicz Tadeusz , Zalewska Aleksandra , Zienkiewicz Ewa
Published: August 2023

The sustainable development (SD) policy is also a family-friendly policy. Its crucial element is the use of social transfers (STs) as cash benefits for children. This study aims to explain the existence of regional variation in Poland in terms of the rate of working women and to conduct an assessment of the selected factors’ impact including the impact of STs on women’s propensity to work in Poland between 2000 and 2020. The survey showed the existence of SD through a strong convergence of the study area in terms of the rate of working women (RWW). However, an increase in the RWW with a decreasing fertility rate (FR) was observed between 2017 and 2020. The study showed a negative correlation between the RWW and STs. This means that the higher the transfers, the lower the propensity of women to return to the labour market. This situation could have negative consequences in the future. Long-lasting non-working periods mean a shorter period of pension contributions. As a result, with a low fertility rate, an accelerated pauperisation of society and a growing group of people living below the poverty line can be expected, which could disrupt or halt the country’s sustainable development. Appropriate reforms are needed to enable women to return to the labour market as soon as possible.
Supply–Demand Imbalance in School Land: An Eigenvector Spatial Filtering Approach
Authors: Sun Wenwen , Murakami Daisuke , Hu Xin , Li Zhuoran , Kidd Akari Nakai , Liu Chunlu
Published: August 2023

The spatial flows of school-age children and educational resources have been driven by such factors as regional differences in population migration and the uneven development of the education quality and living standards of residents in urban and rural areas. This phenomenon further leads to a supply–demand imbalance between the area of school land and the number of school-age children in the geographical location of China. The georeferenced data characterizing supply–demand imbalance presents an obvious spatial autocorrelation. Therefore, a spatial data analysis technique named the Eigenvector Spatial Filtering (ESF) approach was employed to identify the driving factors of the supply–demand imbalance of school land. The eigenvectors generated by the geographical coordinates of all primary schools were selected and added into the ESF model to filter the spatial autocorrelation of the datasets to identify the driving factors of the supply–demand imbalance. To verify the performance of the technique, it was applied to a county in the southwest of Shandong Province, China. The results from this study showed that all the georeferenced indicators representing population migration and education quality were statistically significant, but no indicator of the living standards of residents showed statistical significance. The eigenvector spatial filtering approach can effectively filter out the positive spatial autocorrelation of the datasets. The findings of this research suggest that a sustainable school-land-allocation scheme should consider population migration and the possible preference for high-quality education.
Impact of Biochar on Fusarium Wilt of Cotton and the Dynamics of Soil Microbial Community
Authors: Asif Mamoona , Haider Muhammad Saleem , Akhter Adnan
Published: August 2023

The effects of biochar on leaf and soil-borne diseases of plants can be seen in addition to its ability to sequester carbon, improve soil quality, and enhance plant performance. However, the mechanisms by which soil-borne pathogens are suppressed and plant performance is enhanced are not well understood. The present work aims to comprehensively establish the links between biochar-induced changes in the richness of the rhizosphere microbial population, in association with the reduction of soil-borne Fusarium wilt disease (Fusarium oxysporum f. sp. vasinfectum), in cotton (Gossypium hirsutum), with improved plant performance. Biochar made from organic waste significantly decreased the colonization and survival of Fusarium in soil, raised the culture-able counts of numerous microbes with biocontrol potential (microorganisms that boost plant growth and development), and inhibited Fusarium wilt of cotton. The biochar amendment significantly enhanced the cotton plant development and physiological parameters such as chlorophyll content, etc. Overall, 9% organic waste biochar had shown a significant impact on cotton growth as compared to other treatments with or without biochar. Compared to the soil-only control, the disease index was considerably reduced in all biochar-amended treatments. In terms of the plant’s resistance to Fusarium wilt, biochar-induced increases in the level of overall chlorophyll content and biochemicals such as phenolics, flavonoids, etc. Additionally, cotton plants grown with a 9% biochar composition had considerably greater NPK levels than other treatments with or without biochar. The biochar addition resulted in increased counts of Pseudomonas spp., Actinomycetes spp., and Trichoderma spp., while Acidobacteriales, Rhodospirillales, and Frankiales were less when compared with an un-amended (without biochar) soil control. Thus, the composition of rhizosphere bacteria in the treatments with and without modified biochar was found to differ significantly.
Spatial Mapping of Soil Salinity Using Machine Learning and Remote Sensing in Kot Addu, Pakistan
Authors: Haq Yasin ul , Shahbaz Muhammad , Asif H. M. Shahzad , Al-Laith Ali , Alsabban Wesam H.
Published: August 2023

The accumulation of salt through natural causes and human artifice, such as saline inundation or mineral weathering, is marked as salinization, but the hindrance toward spatial mapping of soil salinity has somewhat remained a consistent riddle despite decades of efforts. The purpose of the current study is the spatial mapping of soil salinity in Kot Addu (situated in the south of the Punjab province, Pakistan) using Landsat 8 data in five advanced machine learning regression models, i.e., Random Forest Regressor, AdaBoost Regressor, Decision Tree Regressor, Partial Least Squares Regression and Ridge Regressor. For this purpose, spectral data were obtained between 20 and 27 of January 2017 and a field survey was carried out to gather a total of fifty-five soil samples. To evaluate and compare the model’s performances, the coefficient of determination (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE) and the Root-Mean-Squared Error (RMSE) were used. Spectral data of band values, salinity indices and vegetation indices were employed to study the salinity of soil. The results revealed that the Random Forest Regressor outperformed the other models in terms of prediction, achieving an R2 of 0.94, MAE of 1.42 dS/m, MSE of 3.58 dS/m and RMSE of 1.89 dS/m when using the Differential Vegetation Index (DVI). Alternatively, when using the Soil Adjusted Vegetation Index (SAVI), the Random Forest Regressor achieved an R2 of 0.93, MAE of 1.46 dS/m, MSE of 3.90 dS/m and RMSE of 1.97 dS/m. Hence, remote sensing technology with machine learning models is an efficient method for the assessment of soil salinity at local scales. This study will contribute to mitigating osmotic stress and minimizing the risk of soil erosion by providing early warnings regarding soil salinity. Additionally, it will assist agriculture officers in estimating soil salinity levels within a shorter time frame and at a reduced cost, enabling effective resource allocation.
Life Cycle Analysis of Hydrogen Powered Marine Vessels—Case Ship Comparison Study with Conventional Power System
Authors: Wang Haibin , Aung Myo Zin , Xu Xue , Boulougouris Evangelos
Published: August 2023

The latest International Maritime Organization strategies aim to reduce 70% of the CO2 emissions and 50% of the Greenhouse Gas (GHG) emissions from maritime activities by 2050, compared to 2008 levels. The EU has set up goals to reduce GHG emissions by at least 55% by 2030, compared to 1990, and achieve net-zero GHG emissions by 2050. The UK aims to achieve more than 68% GHG emission reduction by 2030 and net-zero GHG emissions by 2050. There are many solutions under development to tackle the challenge of meeting the latest decarbonization strategies from the IMO, EU, and UK, among which are hydrogen powered marine vessels. This paper presents a life cycle analysis study for hydrogen fuelled vessels by evaluating their performance in terms of environmental friendliness and economic feasibility. The LCA study will consider the gas emissions and costs during the life stages of the ships, including the construction, operation, maintenance, and recycling phases of the selected vessels. The results of the comparisons with the conventional version of the ships (driven by diesel generators) demonstrate the benefits of using hydrogen for marine transportation: over 80% emission reduction and around 60% life cycle cost savings. A sensitivity analysis shows that the prices of fuels and carbon credits can affect the life cycle cost, and recommendations for low H2 price and high carbon credit in the future are provided to attract the industry to adopt the new fuel.
Energy Consumption Forecasting in Commercial Buildings during the COVID-19 Pandemic: A Multivariate Multilayered Long-Short Term Memory Time-Series Model with Knowledge Injection
Authors: Dinh Tan Ngoc , Thirunavukkarasu Gokul Sidarth , Seyedmahmoudian Mehdi , Mekhilef Saad , Stojcevski Alex
Published: August 2023

The COVID-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this paper, we propose a new forecasting model called the multivariate multilayered long short-term memory (LSTM) with COVID-19 case injection (mv−M−LSTM−CI) for improved energy forecast during the next occurrence of a similar pandemic. We utilized data from commercial buildings in Melbourne, Australia, during the COVID-19 pandemic to predict energy consumption and evaluate the model’s performance against commonly used methods such as LSTM, bidirectional LSTM, linear regression, support vector machine, and multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics: mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and R2 score values. The model mv−M−LSTM−CI demonstrated superior performance, achieving the lowest mean percentage absolute error values of 0.061, 0.093, and 0.158 for DatasetS1, DatasetS2, and DatasetS3, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision making during the challenges posed by the occurrence of a pandemic like COVID-19 in the future.