bluuebunny/crossref_metadata_2025 huggingface dataset:
Board Gender Diversity and Risk Management in Corporate Financing: A Study on Debt Structure and Financial Decision-Making
Authors: Askarany Davood , Jafari Soleil , Pouryousof Azam , Habibi Sona , Yazdifar Hassan
Published: January 2025

Purpose: This study examines the role of board gender diversity in shaping corporate financial decisions, particularly in terms of debt structure and risk management. Focusing on the Tehran Stock Exchange, it explores how female representation on boards influences long-term and short-term leverage decisions, focusing on the moderating effect of board compensation. Design/Methodology: Utilising a quantitative ex post facto design, the study analyses data from 114 companies listed on the Tehran Stock Exchange between 2017 and 2021. Multivariate regression techniques, including year- and industry-fixed effects, are employed to investigate the relationship between board gender diversity, debt structure, and risk-taking behaviour. Findings: The results reveal a significant negative relationship between female board representation and long-term debt, suggesting that companies with more female directors tend to adopt more conservative debt structures, thereby reducing risk. Additionally, the findings demonstrate that board compensation moderates this relationship by curbing managerial risk-taking, further improving financial decision-making. Originality/Value: This research provides novel insights into the intersection of board gender diversity and risk management in financial decision-making, particularly in the context of a developing economy like Iran. It also offers practical implications for firms seeking to optimise their debt structures while maintaining sound risk management practices.
Analysis of Uneven Settlement of Long-Span Bridge Foundations Based on SBAS-InSAR
Authors: Zhang Kaixuan , Xiao Weifo , Zhu Haojie , Ning Shaowei , Huang Shenjiang , Jin Dongxing , A Rong , Thapa Bhesh Raj
Published: January 2025

Bridge foundation settlement monitoring is crucial for infrastructure safety management, as uneven settlement can lead to stress redistribution, structural damage, and potentially catastrophic collapse. While traditional contact sensors provide reliable measurements, their deployment is labor-intensive and costly, especially for long-span bridges. Current remote sensing methods have not been thoroughly evaluated for their capability to detect and analyze complex foundation settlement patterns in challenging environments with multiple influencing factors. Here, we applied Small Baseline Subsets Synthetic Aperture Radar Interferometry (SBAS-InSAR) technology to monitor foundation settlement of a long-span bridge. Our analysis revealed distinct deformation patterns: uplift in the north bank approach bridge foundation and the left-side main bridge foundation (maximum rate: 36.97 mm/year), concurrent with subsidence in the right-side main bridge foundation and south bank approach bridge foundation (maximum rate: 35.59 mm/year). We then investigated the relationship between these settlement patterns and various environmental factors, including geological conditions, Sediment Transport Index (STI), Topographic Wetness Index (TWI), precipitation, and temperature. The observed settlement patterns were attributed to the combined effects of stratigraphic heterogeneity, dynamic hydrological conditions, and seasonal climate variations. These findings demonstrate that SBAS-InSAR technology can effectively capture complex bridge foundation deformation processes, offering a cost-effective alternative to traditional monitoring methods. This advancement in bridge monitoring technology could enable more widespread and frequent assessment of bridge foundation stability, ultimately improving infrastructure safety management.
Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S
Authors: St. Peter Joseph , Drake Jason , Medley Paul , Broadbent Eben , Chen Gang , Ibeanusi Victor
Published: January 2025

Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.
Eliminating Inductive Coupling in Small-Loop TEM Through Differential Measurement with Opposing Coils
Authors: Chen Xinghai , Yang Haiyan , Xia Tong , Wu Xiaoping , Liu Shengdong
Published: January 2025

The small-loop transient electromagnetic method (TEM) refers to a system in which the coil frame length or diameter is less than 2 m. Due to the inductive effects of the multi-turn coils used for both transmission and reception, the induced electromotive force in the measuring coil increases, causing a reduction in the decay rate and an extension of the shutoff time. This results in coupling between the primary and secondary fields in early-time signals, making them difficult to separate and creating a detection blind spot in the shallow subsurface. The opposing coil TEM transmission and reception method can significantly reduce early-time signal distortion caused by coil inductance. However, this approach is constrained by the physical symmetry of the coil dimensions, which makes it challenging to achieve balance in a zero-field space. By performing both forward and reverse measurements at the same location using the opposing coil setup and calculating the difference between the signals, the inductive coupling between coils at the measurement site can theoretically be eliminated. This eliminates the induced potential of the TEM signal, enhancing the induced electromotive force from the formation. As a result, more accurate resistivity values are obtained, detection blind spots are eliminated, and the resolution in shallow TEM exploration is improved. Field experiments were conducted to validate the method on both high-resistivity and low-resistivity anomalies. The results demonstrated that this method effectively identified a high-resistivity corrugated pipe at a depth of 1.2 m and two low-resistivity gas pipelines at a depth of 2 m, thereby essentially eliminating detection blind spots in the shallow subsurface.
Three-Dimensional Deformation Prediction Based on the Improved Segmented Knothe–Dynamic Probabilistic Integral–Interferometric Synthetic Aperture Radar Model
Authors: Wang Shuang , Liu Genyuan , Song Zhihong , Yang Keming , Li Ming , Chen Yansi , Wang Minhua
Published: January 2025

Coal is the main mineral resource, but over-exploitation will cause a series of geological disasters. Interferometric synthetic aperture radar (InSAR) technology provides a superior monitoring method to compensate for the inadequacy of traditional measurements for mine surface deformation monitoring. In this study, the whole process of mining a working face in Huaibei Mining District, Anhui Province, is taken as the object of study. The ALOS PALSAR satellite radar image data and ground measurements were acquired, and the ISK-DPIM-InSAR deformation monitoring model with the dynamic probabilistic integral model (DPIM) was proposed by combining the probabilistic integral method (PIM) and the improved segmented Knothe time function (ISK). The ISK-DPIM-InSAR model constructs the inversion equations of InSAR line-of-sight deformation, north–south and east–west horizontal movement deformation, vertical deformation, inverts the optimal values of the predicted parameters of the workforce through the particle swarm algorithm, and substitutes it into the ISK-DPIM-InSAR model for predicting the three-dimensional dynamic deformation of a mining face. Simulated workface experiments determined the feasibility of the model, and by comparing the level observation results of the working face, it is confirmed that the ISK-DPIM-InSAR model can accurately monitor the three-dimensional deformation of the surface in the mining area.
A Spectroscopic Methodology to Early Detection of Urinary Tract Infections
Authors: Mendes Ana F. N. S. , Matela Nuno , Coelho João M. P. , Marquês Joaquim T.
Published: January 2025

Healthcare-associated infections (HAI) are a critical public health problem, with 30 to 40% of infections related to the urinary tract system. These urinary tract infections (UTIs) are considered one of the most common microbial infections in hospital settings and everyday community contexts, where approximately 80% are highly correlated with urinary catheter insertion, i.e., catheter-associated urinary tract infections (CAUTIs). Considering that 15 to 25% of hospitalised patients need to be catheterised during their treatments and most CAUTIs are asymptomatic, it results in a tremendous challenge to provide an early diagnosis of CAUTI and therefore initiate its treatment. The lack of standardised methods as a first step for urine monitoring and early detection of UTIs is the driving force of this work, which aims to explore the potential of absorption and fluorescence spectroscopic methodologies to detect UTIs. Urine samples were used without any previous treatment to target the most straightforward testing protocol possible. In this work, we successfully developed a powerful methodology that combines ratiometric fluorescence spectroscopy measurements and transmittance at 600 nm to distinguish healthy urine from infected urine. The complementary use of fluorescence spectroscopy and transmittance is what makes the new methodology we propose such a powerful approach to monitor urine samples and provide early detection of UTIs since it provides a quantitative analysis of both healthy and infected urine.
Effect of the Current-Collecting Carbon Nanotubes Layer on the Properties of the Lead Zirconate Titanate Film for Vibration Sensors
Authors: Petrov Victor V. , Sysoev Victor V. , Rudyk Nikolay N. , Varzarev Yuri N. , Nesterenko Andrey V.
Published: January 2025

One of the challenging problems in the research and development of vibration sensors relates to the formation of Ohmic contacts for the removal of an electrical signal. In some cases, it is proposed to use arrays of carbon nanotubes (CNTs), which can serve as highly elastic electrode materials for vibration sensors. The purpose of this work is to study the effect of a current-collecting layer of CNTs grown over silicon on the properties of a lead zirconate titanate (PZT) film, which is frequently employed in mechanical vibration sensors or energy harvesters. For the experiments, a vibration sensor mock-up was created with the PZT-CNT-Ni-V-SiO2-Si and PZT-CNT-Ni-V-Si structures where an array of vertically oriented CNTs was grown over an oxidized or high-alloyed silicon substrates by plasma chemical deposition from a gas phase. Then, a thin film of PZT was applied to the CNT layer with a high-frequency reactive plasma spraying. For comparison, the PZT film was applied to silicon without a CNT layer (PZT-Si structure). The calculated average value of the piezoelectric module is 112 pm/V for the Ni-PZT-PT-Ni-Si-SiO2 sample, and 35 pm/V for PZT-Ni-SiO2-Si. It can be seen that the contact realized with the help of CNT ensures more than three times the best efficiency in terms of the piezoelectric module. The value of the piezoelectric module of the vibration sensor with the PZT-CNT-Ni-V-Si structure was 186 pm/V, and the value of the residual polarization was 23.2 µC/cm2, which is more than eight and three times, respectively, higher than the values of these properties for the vibration sensor with the PZT-Si structure. It is shown that the vibration sensor can operate in the frequency range of 0.1–10 kHz.
Incremental Structure from Motion for Small-Scale Scenes Based on Auxiliary Calibration
Authors: Li Sixu , Li Jiatian , Yang Tao , A Xiaohui , Liu Jiayin
Published: January 2025

Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas. At the same time, we obtain a coarse matching set of feature points using pairwise polar geometric constraints. The positional results from the geometric constraints of the calibration plate are then used to filter out high-precision matching points, thereby improving the accuracy of the three-dimensional reconstruction. Experimental results demonstrate that the proposed method achieves superior reconstruction completeness and accuracy. In three real-world experiments, the average re-projection errors were 0.5245, 0.4151, and 0.4996 pixels, outperforming competing methods. This approach ensures robust pose estimation and facilitates precise 3D reconstructions.
TTSNet: Transformer–Temporal Convolutional Network–Self-Attention with Feature Fusion for Prediction of Remaining Useful Life of Aircraft Engines
Authors: Li Zhaofei , Luo Shilin , Liu Haiqing , Tang Chaobin , Miao Jianguo
Published: January 2025

Accurately predicting the remaining useful life (RUL) is crucial for ensuring the safety and reliability of aircraft engine operation. However, aircraft engines operate in harsh conditions, with the characteristics of high speed, high temperature, and high load, resulting in high-dimensional and noisy data. This makes feature extraction inadequate, leading to low accuracy in the prediction of the RUL of aircraft engines. To address this issue, Transformer-TCN-Self-attention network (TTSNet) with feature fusion, as a parallel three-branch network, is proposed for predicting the RUL of aircraft engines. The model first applies exponential smoothing to smooth the data and suppress noise to the original signal, followed by normalization. Then, it uses a parallel transformer encoder, temporal convolutional network (TCN), and multi-head attention three-branch network to capture both global and local features of the time series. The model further completes feature dimension weight allocation and fusion through a multi-head self-attention mechanism, emphasizing the contribution of different features to the model. Subsequently, it fuses the three parts of features through a linear layer and concatenation. Finally, a fully connected layer is used to establish the mapping relationship between the feature matrix and the RUL label, obtaining the RUL prediction value. The model was validated on the C-MAPSS aircraft engine dataset. Experimental results show that compared to other related RUL models, the RMSE and Score reached 11.02 and 194.6 on dataset FD001, respectively; on dataset FD002, the RMSE and Score reached 13.25 and 874.1, respectively. On dataset FD003, the RMSE and Score reached 11.06 and 200.1 and on dataset FD004, the RMSE and Score reached 18.26 and 1968.5, respectively, demonstrating better performance of RUL prediction.
MSCD-YOLO: A Lightweight Dense Pedestrian Detection Model with Finer-Grained Feature Information Interaction
Authors: Liu Qiang , Li Zhongmin , Zhang Lei , Deng Jin
Published: January 2025

Pedestrian detection is widely used in real-time surveillance, urban traffic, and other fields. As a crucial direction in pedestrian detection, dense pedestrian detection still faces many unresolved challenges. Existing methods suffer from low detection accuracy, high miss rates, large model parameters, and poor robustness. In this paper, to address these issues, we propose a lightweight dense pedestrian detection model with finer-grained feature information interaction called MSCD-YOLO, which can achieve high accuracy, high performance and robustness with only a small number of parameters. In our model, the light-weight backbone network MobileViT is used to reduce the number of parameters while efficiently extracting both local and global features; the SCNeck neck network is designed to fuse the extracted features without losing information; and the DEHead detection head is utilized for multi-scale feature fusion to detect the targets. To demonstrate the effectiveness of our model, we conducted tests on the highly challenging dense pedestrian detection datasets Crowdhuman and Widerperson. Compared to the baseline model YOLOv8n, MSCD-YOLO achieved a 4.6% and 1.8% improvement in mAP@0.5, and a 5.3% and 2.6% improvement in mAP@0.5:0.95 on the Crowdhuman and Widerperson datasets, respectively. The experimental results show that under the same experimental conditions, MSCD-YOLO significantly outperforms the original model in terms of detection accuracy, efficiency, and model complexity.
Deepening the Relationship Between the Need for Epistemic Certainty and People’s Compliance with Social Power: The Moderating Role of Work Unit Tightness
Authors: Di Santo Daniela , Tesi Alessio , Aiello Antonio , Pierro Antonio
Published: January 2025

The present paper builds on previous research exploring the relationship between the need for cognitive closure (NCC) and employees’ compliance with harsh social power to propose a moderating role of perceived tightness within a work unit in organizational settings. Specifically, the study aimed to test the cross-level interaction between NCC and the perceived work unit tightness in fostering employee compliance with harsh power. Using a convenience sampling method, we enrolled 290 employees from pre-existing work units in Italian organizations in a cross-sectional study. We obtained employee scores on the NCC scale, willingness to comply with harsh social power tactics, and ratings of their perceived work unit tightness. Multilevel modeling was applied to test cross-level interaction. The model revealed a positive effect of NCC on the willingness to comply with harsh social power tactics when employees perceived their unit culture as tight. This study advances previous research by showing the role of tight culture in shaping the relationship between NCC and power compliance.
Popliteal Artery Injury Following Knee Dislocation: Anatomy, Diagnosis, Treatment, and Outcomes
Authors: Vyas Kunj C. , Abaskaron Michael , Carpenter Mikaila , Manes Taylor , Turnow Morgan , DeGenova Daniel T. , Taylor Benjamin C.
Published: January 2025

Background/Objectives: Popliteal artery injury is a rare but devastating complication of knee dislocations, significantly increasing the risk of limb ischemia, amputation, and poor functional outcomes if not promptly managed. This systematic review primarily evaluates the functional outcomes associated with this injury but also reviews current research on diagnostic modalities and treatment strategies to provide a comprehensive understanding of this severe orthopedic and vascular injury. Methods: A systematic search of PubMed, in accordance with PRISMA Guidelines, identified 144 studies, of which 13 full-text articles were assessed for eligibility after excluding 131 during the title and abstract screening. Six studies were excluded due to missing vascular injury or functional outcome data or being written in a foreign language, leaving seven studies for inclusion. These studies were predominantly retrospective, focusing on knee dislocations with popliteal artery injury and reporting validated functional outcomes such as the Lysholm and International Knee Documentation Committee (IKDC) scores. The data were synthesized narratively due to heterogeneity in the study designs, interventions, and outcome reporting. Results: Patients with vascular injuries consistently demonstrated poorer functional outcomes compared to those without, with mean or median Lysholm and IKDC scores consistently being lower than non-vascular injury patients. Increased BMI, delayed intervention, and multi-ligamentous injury were associated with worse outcomes, highlighting the importance of timely surgical management. Early repair and grafting techniques improved functional recovery, while diagnostic modalities such as Doppler ultrasound and CT angiography showed high sensitivity in detecting vascular injury. Complications included limb ischemia, prolonged rehabilitation, and amputation, often linked to delayed diagnosis. Conclusions: Knee dislocations with popliteal artery injury require rapid diagnosis and early surgical intervention to optimize functional outcomes and reduce complications. Standardized outcome measures and high-quality prospective research are needed to refine management strategies and address patient-specific factors like BMI.
Full Competition and Innovation
Authors: Palacio-Morena Juan Ignacio , Mungaray-Lagarda Alejandro , Salgado-Beltrán Lizbeth , Méndez-León Jaciel Ramsés
Published: January 2025

Economic science is built on the basis of the concept of competition. Classical School economists have not defined this concept with precision. The “Neoclassic Revolution” defined the concept of perfect competition as an ideal combination of productive resources accessible to all permanently (optimal size). Within this framework, competition ensures that suppliers will cover demand by matching market prices to both average and marginal costs corresponding to the optimal size. Large and persistent differences in profit and wage rates, when interpreted from a perfect competition perspective, will always be considered a sign of the absence of competition. Only an alternative concept of competition can modify such an interpretation. In this context, the objective of this work was to develop an alternative concept of full competition, which is formally formulated, to foster a more sustainable environment for the coexistence of enterprises of different sizes. The full competition definition assumes that firms maximize long-term benefits (fixed-capital amortization period) by trying to ensure full use of productive capacity and achieving relative advantages through innovation instead of merely adjusting prices to maximize short-term benefits. This approach provides a different explanation for inequalities in profit and wage rates, from simple adjustment in prices (perfect competition) to the search for new and better opportunities through innovation (full competition). This could change the meaning and scope of economic policies, which are influenced by both Neoclassical and Keynesian approaches, when addressing the entrepreneurial challenges of sustainable development.
Risk Assessment in Mass Housing Projects Using the Integrated Method of Fuzzy Shannon Entropy and Fuzzy EDAS
Authors: Hatefi Seyed Morteza , Ahmadi Hanieh , Tamošaitienė Jolanta
Published: January 2025

Mass building projects play a key role in the economic prosperity of any country. Furthermore, these projects are among the main drivers of environmental and social problems. In recent years, with the spread of the concept of sustainable development in the life cycle of construction projects and the dynamic and eventful nature of these projects, the issue of risk management in the sustainable construction industry has received more and more attention among researchers. The construction industry, like other industries, faces various risks. Therefore, it is crucial to identify and evaluate risks in mass construction projects due to the high volume of work. In this study, an integrated model based on fuzzy Shannon entropy and fuzzy EDAS is proposed for risk assessment in large-scale building projects. Initially, by reviewing related articles, 66 effective sub-indicators are identified and classified into 18 risk categories, including 6 external risks and 12 internal risks. Subsequently, a questionnaire is designed to assess the three factors of detection, probability of occurrence, and severity risks for each risk index. This questionnaire distributes to 15 mass production companies in the construction field in Isfahan. The fuzzy Shannon entropy method is then applied to determine the weight of risk factors. The weights of each factor, detection, probability of occurrence, and severity, are calculated as 0.386, 0.342, and 0.273, respectively. These weights are used in the fuzzy EDAS method to prioritize the identified risks in mass-building projects. The results of the fuzzy EDAS method determined the three most critical risks: “inflation rate volatility”, “import/export restrictions”, and “unforeseen climatic conditions”. Additionally, three low-risk sub-indicators are obtained: “limitation on working hours”, “collapse of the structure”, and “unpredictable fire”.
Timber Biogenic Carbon Stock in the Urban Environment: Santiago City as a Second Forest
Authors: Victorero Felipe , Bustamante Waldo
Published: January 2025

Urban environments significantly contribute to carbon emissions, both through operational processes and the embodied emissions of construction materials, thus exacerbating climate change. Nevertheless, urban timber structures offer a viable alternative by acting as carbon sinks, capable of sequestering carbon for decades or even centuries. This study develops and applies a methodology to quantify the biogenic carbon stored in Santiago City’s timber-based buildings, conceptualized as a “Second Forest” that transfers and preserves the carbon capture capacity of trees in the built environment. The analysis estimates that Santiago’s urban timber constructions have expanded their wood-built surface area by 192,831 m2 over the past eight years, reflecting the growing adoption of timber in urban construction. During the same period, biogenic carbon storage increased from 199.78 kt to 202.73 kt, equivalent to 10.84 kt of CO2 under average conditions. These findings highlight the potential of urban planning strategies, such as promoting taller timber buildings and adopting circular timber practices, to enhance carbon sequestration and reduce reliance on carbon-intensive materials. This research highlights the fundamental role that timber buildings play in urban climate change mitigation, positioning them as active contributors to global carbon management efforts.
Advancing Middle East Construction Sustainability: A Framework for Addressing Logistics Challenges Through Solutions and Critical Success Factors
Authors: Ruzieh Abdulla Subhi
Published: January 2025

The Middle Eastern construction industry significantly contributes to regional economic development but faces persistent sustainability challenges due to logistical inefficiencies. This study aims to address the gap in the existing literature, which often treats logistics challenges, solutions, and critical success factors (CSFs) in isolation without offering a unified framework to integrate these elements. By leveraging structural equation modelling (SEM), this research provides a comprehensive approach to understanding and addressing the interconnections between logistics inefficiencies, proposed solutions, and sustainability outcomes. Using SEM, this study identifies and analyses 23 logistics challenges, 18 solutions, and 8 CSFs, revealing their relationships and collective impact on sustainability in the construction sector. SEM’s robust analytical capabilities enable simultaneous testing of complex interdependencies, offering insights that traditional methodologies cannot achieve. The findings emphasise the critical role of lean methodologies, advanced technologies, and collaboration strategies in overcoming inefficiencies, reducing environmental impact, and optimising resource use. This research fills a crucial gap by developing a cohesive framework that integrates logistics challenges, solutions, and CSFs to advance sustainable practices. The outcomes provide actionable guidance for stakeholders to improve sustainability performance while enhancing competitiveness in Middle Eastern construction logistics
Understanding Congestion Risk and Emissions of Various Travel Behavior Patterns Based on License Plate Recognition Data
Authors: Wang Yuting , He Zhaocheng , Xing Wangyong , Lin Chengchuang
Published: January 2025

Understanding vehicle travel behavior patterns is crucial for effectively managing urban traffic congestion and mitigating the associated risks and excessive emissions. Existing research predominantly focuses on commuting patterns, with limited attention given to the spatiotemporal characteristics of other travel behaviors, and sparse investigation into the congestion risks and emissions associated with these patterns. To address this gap, the present study examines various travel behavior patterns and their associated congestion risks and emissions, using one week of License Plate Recognition (LPR) data from the megacity expressway network. First, we classify vehicles into different travel modes based on spatiotemporal features extracted from the LPR data and propose a scalable mode recognition method suitable for large-scale applications. We then assess the congestion risks associated with each mode and estimate the excessive emissions resulting from congestion. The findings reveal notable differences in congestion risks among travel modes, with a bimodal distribution influenced by the temporal rhythm of traffic flow. Furthermore, although commercial vehicles constitute only one-third of the total vehicle population, the excessive emissions attributed to congestion from commercial vehicles are comparable to those from privately owned vehicles. This suggests that focusing exclusively on commuting patterns may underestimate both the congestion risks and excessive emissions. The results of this study not only deepen our understanding of the relationship between individual travel behavior and traffic congestion but also support the optimization of personal travel time and health management, providing a foundation for the development of personalized and proactive traffic demand management strategies.
From Waste to Resource: Evaluation of the Technical and Environmental Performance of Concrete Blocks Made from Iron Ore Tailings
Authors: Weba Luciana Chaves , Oliveira Júlia Maria Medalha Resende , Souza Alberto José Corrêa de , Antunes Ludimila Gomes , Franco de Carvalho José Maria , Fontes Wanna Carvalho
Published: January 2025

This study investigates the use of iron ore tailings (IOTs) as recycled aggregates in segmental blocks, focusing on technical performance, CO2 emissions, and embodied energy using the cradle-to-gate approach. IOTs replaced fine aggregates in concrete at 25%, 50%, and 75% by volume, achieving compressive strengths of 16.23 MPa, 10.02 MPa, and 3.93 MPa, respectively. Raw material production accounted for 98% of CO2 emissions and 86% of embodied energy. Producing blocks at mining sites offered limited environmental benefits due to longer transport distances. Despite this, the results showed a 6% reduction in CO2 emissions and a 35% improvement in mechanical–environmental performance (CO2 emissions weighted by compressive strength) compared to traditional concrete. These findings underscore the potential of IOT-based concrete for segmental block production.
The Role of Learning Analytics in Evaluating Course Effectiveness
Authors: Wong Billy T. M. , Li Kam Cheong , Liu Mengjin
Published: January 2025

This study aims to examine the use of learning analytics in course evaluation within higher education institutions, in order to identify effective methodologies and best practices for leveraging data to improve educational effectiveness. Following the PRISMA guidelines, a systematic literature search was conducted in Scopus, yielding 34 relevant studies published between 2015 and 2024 for analysis. The results reveal six key categories of learning analytics applications: sentiment analysis, questionnaire analysis, engagement analysis, topic classification, predictive modelling, and performance analysis. The data sources for learning analytics applications primarily include questionnaires and learning management systems. While descriptive analysis was found to be the most commonly employed analytical technique, advanced techniques such as machine learning, artificial intelligence, and social network analysis are becoming more prominent. The studies addressed a wide range of elements associated with course evaluation, including course design, content quality, assignments, instructional strategies, workload, feedback mechanisms, and the integration of technology. These findings highlight the importance of adopting holistic approaches to capture the multifaceted nature of student experiences. This study also uncovers major limitations in the existing research, such as small sample sizes, potential biases due to the use of survey-based methods, and challenges in generalising findings across disciplines. These insights underscore the need for further research to enhance the methodologies used in course evaluations. This study contributes to advancing learning analytics practices and emphasises the importance of innovative approaches for evaluating and improving course effectiveness.
A Thematic Analysis, Definition, and a Green Aviation Conceptual Model—Putting It All Together
Authors: George Saade Raafat , Zhang Xue , Yu Ce , Yao Junchen
Published: January 2025

The notion of ‘Green Aviation’ has gained interest over the last three years. However, there is no clear definition of what the term green aviation means. Many interpret the meaning to describe the focus of their study, resulting in a diverse understanding of what it entails. In this study, we present our systematic approach to proposing a definition that meets all stakeholders’ understanding and extract from the definition an actionable framework. Focusing on total quality management and circular economy frameworks, we followed a two-step approach towards the definition: Conduct a thematic analysis on the body of literature, followed by consultations with green aviation professionals. Green aviation is defined as sustainable aviation practices to minimize environmental impact and continuously improve social wellbeing. This definition is used to extract a conceptual framework to help future research work and sustainability integration into the practitioner’s strategic planning. The proposed framework adapts the triple bottom line theory to include an organization’s level of social, environmental, and economic integration towards increased ‘sustainability value’. Our study findings provide organizations with insight into pathways towards the successful integration of their environmental goals into their strategic planning, where continuous improvement and social responsibility play a central role.
Can New Quality Productivity Promote the Carbon Emission Performance—Empirical Evidence from China
Authors: Wang Shubin , Chen Feng
Published: January 2025

New quality productivity (NQP) has the possibility to enhance carbon emission performance which will fortify the groundwork for long-term economic expansion even further. The research examines the panel data of 30 provinces spanning the years 2012 to 2022 for an evaluation framework for NQP and carbon emission performance at the provincial level. Employing fixed effect models, mediation effect analysis, and spatial econometrics, the study explores the effect of NQP on carbon emission performance, its mediating mechanisms, and the spatial spillover effects. The findings indicate that (1) NQP significantly lowers carbon emissions for every unit of GDP and enhances carbon emission performance, and the result holds up when the instrumental variable methods are used. (2) The NQP had a significant contribution to improving carbon emission performance via advancements in green innovation. (3) The NQP does more than directly enhance the regional carbon emission performance; in contrast, it additionally positively influences the carbon emission performance level of the adjacent regions by the spatial spillover effect. (4) The impact of NQP on carbon emission performance is particularly pronounced in eastern and innovative regions. On this basis, we should vigorously develop the NQP, strengthen cross-regional policy coordination, and promote green and sustainable development.
The Role of Sustainable Business Environment in Shaping Entrepreneurs’ Performance: Evidence from Myanmar
Authors: Zhao Xiaokang , Hlaing Nyo Me , Shen Huali , Xiao Pan , Adugna Tessema Shimelis
Published: January 2025

This study explores how Myanmar’s sustainable business environment influences entrepreneurs’ performance, focusing on the mediating role of knowledge spillover effects of foreign direct investment (KFDI). Data were gathered from 308 entrepreneurs across micro, small, medium, and large enterprises in Myanmar using online surveys via Google Forms and Microsoft Forms. The analysis employed partial least squares structural equation modeling (PLS-SEM) with SPSS 29 and SmartPLS 4. The results reveal that (i) the economic environment exerts a substantial positive influence on entrepreneurs’ performance; (ii) access to credit and the social environment show no discernible impact on entrepreneurs’ performance; (iii) both economic and social environments positively influence KFDI; (iv) access to credit has no effect on KFDI; and (v) KFDI partially mediates the relationship between the economic environment and entrepreneurs’ performance while fully mediating the relationship between the social environment and entrepreneurs’ performance. However, KFDI does not mediate the effect of access to credit on entrepreneurs’ performance. These findings underscore the critical role of sustainable economic and social environments in enhancing entrepreneurs’ performance and attracting foreign firms. Policymakers should prioritize these dimensions of the business environment to foster growth, maximize KFDI, and support long-term entrepreneurial success. This approach will not only ensure the economic vitality of Myanmar’s entrepreneurial ecosystem but also contribute to broader social and environmental sustainability.
Family Strategies for Rural-to-Urban Migrant Children Under the Points-Based Admission Policy in China
Authors: Bo Xiaoqi , Wang Yijie
Published: January 2025

Family factors significantly influence rural-to-urban migrant children’s educational opportunities within the framework of education policies. In this research, we examined rural-to-urban migrant families’ strategies for the urban education of their children in the context of China’s points-based admission policy. We investigated how family capital and willingness to participate impact their children’s access to educational opportunities. The results reveal that the points-based admission policy prioritizes “ability first” while allowing for diverse indicators that accommodate various types of rural-to-urban migrant families. In the points-based admission process, different family types adopt distinct strategies, including the “capital transformation” strategy of high-capital–high-willingness families; the “quit voluntarily” strategy of high-capital–low-willingness families; the “try one’s best” strategy of low-capital–high-willingness families; and the “let nature take its course” strategy of low-capital–low-willingness families. The admission policy based on allocating points favors rural-to-urban migrant children from families with high capital. Nevertheless, those from families with low capital are not entirely excluded from educational opportunities in other locations. These families possess a strong capacity for action, propelled by their eagerness to engage. Within the framework of educational policies, the amalgamation of family capital and willingness to participate serves as the driving force behind rural-to-urban migrant children’s access to educational prospects, with educational expectations serving a regulatory function. Exploring the family strategies for rural-to-urban migrant children’s urban education can enhance educational policies for this demographic and offer valuable recommendations for their sustainable development.
Impact of Urbanization on Water Resource Competition Between Energy and Food: A Case Study of Jing-Jin-Ji
Authors: Liu Kuan , Wang Lichuan , Zhai Jiaqi , Zhao Yong , Deng Haodong , Li Xing
Published: January 2025

Water resources, energy, and food are important resources in China, which play an important role in the process of urban development and are important basic resources for sustainable urban development. This study applied water footprint theory to water–energy–food relations. The regional integration of the Jing-Jin-Ji region faced new challenges during urbanization, and unified measures were applied to quantify the urban water demands and energy and food competition in the Jing-Jin-Ji region from 2003 to 2017. The index was used to evaluate the intensity of the competition for water for food and energy. The results indicated that from 2003 to 2017, the water footprint of grain production in the Jing-Jin-Ji region decreased from 30.984 billion m3 to 21.36 billion m3, of which the blue water footprint decreased from 13.032 billion m3 to 9.854 billion m3. The water footprint of energy production increased from 578 million m3 to 1.175 billion m3. The competition relation between cities in the Jing-Jin-Ji region was obtained according to the competition index, and corresponding measures were identified according to different competition levels. This study provides valuable insights for policymakers in designing sustainable urban development strategies for cities facing similar challenges of water resource, energy, and food competition during rapid urbanization.
A Framework for Sustainable and Fair Demand-Supply Matchmaking Through Auctioning
Authors: Fernández Shai , Bodin Ulf , Synnes Kåre
Published: January 2025

Environmental sustainability and fairness in auction systems are becoming increasingly important as systems evolve with the integration of digital technologies. This paper introduces a novel demand-supply matchmaking (DSM) framework designed to improve fairness and sustainability in auction environments, aligning with the principles of the circular economy. The framework addresses key challenges in supply chain management, such as equitable resource distribution and the reduction of environmental footprints. The framework integrates key aspects of environmental impact assessments, fairness assessments, and behavioral analytics. This enables the simulation of bidder behavior and assessment of auction scenarios. Our simulation results demonstrate that the platform can promote sustainable, fair, and informed auction practices. By comparing our approach with existing tools, we highlight the advantages of using the DSM framework to improve sustainability and fairness in digital marketplaces. This work supports the development of platforms that integrate economic efficiency with environmental responsibility and social equity.
Solutions of Cauchy Problems for the Gardner Equation in Three Spatial Dimensions
Authors: Zhang Yufeng , Gui Linlin , Feng Binlu
Published: January 2025

In this paper, we generalize the 2 + 1-dimensional Gardner (2DG) equation to three spatial dimensions, i.e., 3 + 1 and 3 + 2 dimensions, and construct the solutions of the Cauchy problems and Lax pairs for the Gardner equation in three spatial dimensions via a novel non-local d-bar formalism. Several new long derivative operators Dx, Dy and Dt are introduced to study the initial value problems for the Gardner equation in three spatial dimensions. It follows that Propositions 1 and 3 summarize the main results of this paper.
X1-Jacobi Differential Polynomial Systems and Related Double-Step Shape-Invariant Liouville Potentials Solvable by Exceptional Orthogonal Polynomials
Authors: Natanson Gregory
Published: January 2025

This paper develops a new formalism to treat both infinite and finite exceptional orthogonal polynomial (EOP) sequences as X-orthogonal subsets of X-Jacobi differential polynomial systems (DPSs). The new rational canonical Sturm–Liouville equations (RCSLEs) with quasi-rational solutions (q-RSs) were obtained by applying rational Rudjak–Zakhariev transformations (RRZTs) to the Jacobi equation re-written in the canonical form. The presented analysis was focused on the RRZTs leading to the canonical form of the Heun equation. It was demonstrated that the latter equation preserves its form under the second-order Darboux–Crum transformation. The associated Sturm–Liouville problems (SLPs) were formulated for the so-called ‘prime’ SLEs solved under the Dirichlet boundary conditions (DBCs). It was proven that one of the two X1-Jacobi DPSs composed of Heun polynomials contains both the X1-Jacobi orthogonal polynomial system (OPS) and the finite EOP sequence composed of the pseudo-Wronskian transforms of Romanovski–Jacobi (R-Jacobi) polynomials, while the second analytically solvable Heun equation does not have the discrete energy spectrum. The quantum-mechanical realizations of the developed formalism were obtained by applying the Liouville transformation to each of the SLPs formulated in such a way.
Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda
Authors: Popa Ion , Ștefan Simona Cătălina , Josan Andrei , Mircioiu Corina-Elena , Căruceru Nicoleta
Published: January 2025

Artificial intelligence (AI) is an increasingly notable presence in society, industries, and organizations, making its necessity felt more in managerial decisions and practices. This paper aims to outline the importance of the topic related to the increase in the adaptability, agility, and resilience of the management system as a result of AI integration, resorting to a bibliometric type of research. A total of 107 papers from the period 2007–2024 exported from the Web of Science Core Collection database were analyzed, with support of Biblioshiny software. This topic is proving to be one of heightened global interest, being comprehensively addressed by world leaders in AI research and technologies such as the United States, China, Great Britain, France, India, and beyond. Collaborative relationships established between geographic regions are captured, noting the power and expansion of the theme on all continents of the globe. Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.