bluuebunny/crossref_metadata_2025 huggingface dataset:
Photoconductive Dynamics of Photorefractive Poly((4-Diphenylamino)benzyl Acrylate)-Based Composites Sensitized by Perylene Bisimide
Authors: Tsutsumi Naoto , Sassa Takafumi , Nguyen Tam Van , Giang Ha Ngoc , Tsujimura Sho , Jackin Boaz Jessie , Kinashi Kenji , Sakai Wataru
Published: January 2025

The transient dynamics of photocurrents for poly((4-diphenylamino)benzyl acrylate) (PDAA)-based photorefractive (PR) polymers sensitized with perylene bisimide derivative N,N′-diisopropylphenyl-1,6,7,12-tetrachloroperylene-3,4,9,10-tetracarboxyl bisimide (PBI) at various composition ratios were studied. The PR polymer included (4-(diphenylamino)phenyl)methanol (TPAOH) photoconductive plasticizer and (4-(azepan-1-yl)-benzylidene) malononitrile nonlinear optical dye as well, which are needed for inducing PR effects. All the photocurrents measured at 640 nm were well simulated by a two-trapping site model considering photocarrier generation and recombination processes of the charge transfer (CT) complex between PBI and PDAA. The process of photocurrent simulation allowed for analyses of the dependences of hole mobility, quantum efficiency (QE) of photocarrier generation, trapping parameters, and recombination coefficient on the PDAA/TPAOH content. Finally, the PDAA content dependences of the trapping and recombination properties were compared with those of the PR parameters of the optical diffraction efficiency, optical gain, and response time.
Solution Casting Effect of PMMA-Based Polymer Electrolyte on the Performances of Solid-State Electrochromic Devices
Authors: Abdelhamed Abdelrahman Hamed Ebrahem , Thien Gregory Soon How , Lee Chu-Liang , Au Benedict Wen-Cheun , Tan Kar Ban , Murthy H. C. Ananda , Chan Kah-Yoong
Published: January 2025

Electrochromic devices (ECDs) are devices that change their optical properties in response to a low applied voltage. These devices typically consist of an electrochromic layer, a transparent conducting substrate, and an electrolyte. The advancement in solid-state ECDs has been driven by the need for improved durability, optical performance, and energy efficiency. In this study, we investigate varying the temperature to the casting solution for polymethylmethacrylate (PMMA)-based electrolytes for solid-state ECDs with a structure of glass/ITO/WO3/PMMA electrolyte/ITO/glass. The electrochromic layer, composed of WO3, was deposited using the sol-gel method, while the electrolyte, comprising lithium perchlorate (LiClO4) in propylene carbonate (PC) with PMMA, was prepared via solution casting. Various electrolyte samples were heated at different temperatures of 25, 40, 60, 80, and 100 °C to analyze the impact on the devices’ performance. Our findings indicate that the devices with electrolytes at 25 °C exhibited superior anodic and cathodic diffusion. An increase in heating temperature corresponded with an increase in switching time. Notably, the sample heated at higher temperatures (60, 80, and 100 °C) demonstrated exceptional cycle stability. Nevertheless, samples with higher temperatures displayed a decrease in optical modulation. Additionally, the 100 °C sample exhibited the highest coloration efficiency compared to other samples at lower temperatures. This research highlights the potential of varying the temperature of solution casting on PMMA-based electrolytes in optimizing the performance of solid-state ECDs, particularly regarding coloration efficiency and durability.
Sustainable Films Derived from Eucalyptus spp. Bark: Improving Properties Through Chemical and Physical Pretreatments
Authors: Rodrigues Débora da S. , Schmitt Patricia O. , Cordeiro Lincoln Audrew , Rodrigues Marlon B. B. , Ribeiro Ana Carolina R. , Bosenbecker Mariane W. , Silva Sarah Kalli S. , Carreno Neftali L. , Gatto Darci A. , Silva Silvia H. F. da , Cholant Camila M. , Missio André Luiz
Published: January 2025

This study investigates the sustainable use of Eucalyptus spp. bark through different chemical (hydrothermal, acid, alkaline, and bleaching) and physical (milling) pretreatments in the production of sustainable films. Valorization of agro-industrial residues and the demand for sustainable materials pose challenges for environmentally responsible solutions. Eucalyptus spp. bark, rich in cellulose, hemicellulose, and lignin, is a promising source for creating sustainable materials like films. In this study, the use of chemical and physical treatments aims to optimize biomass extraction and improve the chemical, thermal, mechanical, and optical properties of the films. The films showed an excellent light barrier capacity, with a transmittance below 1%. Crystallinity indices varied with the pretreatment: 8.15% for hydrothermal, 7.01% for alkaline, 7.63% for acid, and 10.80% for bleaching. The highest crystallinity value was obtained through bleaching, by removing amorphous components like lignin and hemicellulose. The alkaline pretreatment yielded stronger films (maximum stress of 8.8 MPa, Young’s modulus of 331.3 MPa) owing to the retained lignin and the hemicellulose reinforcing the material. This study contributes to the field of sustainable development by converting residues into valuable materials and by advancing the circular economy. The films’ specific properties make them suitable for applications like sustainable packaging, addressing environmental and industrial challenges.
Optimizing Charging and Discharging at Bus Battery Swap Stations Under Varying Environmental Temperatures
Authors: Hu Zihan , Zeng Xiangjun , Feng Chen , Diao Haoran
Published: January 2025

The grid ancillary service capability of bus swapping stations (BSSs) is significantly affected by environmental temperature fluctuations and the disorderly charging and discharging of batteries. This study addressed these challenges by developing a comprehensive optimization model for scheduling BSS operations under varying environmental conditions. First, based on real-world energy consumption and temperature data, an energy consumption coefficient correction method was proposed to improve the accuracy of energy consumption calculations. Next, a greedy strategy was utilized to match vehicle battery swapping demands with available batteries in the BSS. A two-layer multi-objective optimization model was then constructed to optimize the charging and discharging power. The upper layer minimizes the total costs, including charging/discharging costs and battery degradation costs, while the lower layer minimizes the load peak-valley difference and maximizes the charging/discharging balance among all batteries. Numerical simulations of the proposed energy consumption correction coefficient improved the accuracy of electric bus energy consumption estimation. Additionally, the proposed optimization strategy reduced the operational cost of the bus battery swapping station by 53.09% and decreased the transformer’s maximum load rate by 28.28%.
Violence, Politics and Religion: A Case Study of the Black Panther Party
Authors: García-Magariño Sergio , Yates Aaron
Published: January 2025

The majority of US Black social movement organizations during the second half of the twentieth century had explicit ties to either Christian or Islamic religious institutions. The Black Panther Party (BPP) was a notable outlier in its secularism. Through the lens of radicalization, this paper examines the place of violence in the Party’s ideological platform and political practice relative to the Party’s secularism and experience of state repression. Drawing on newly available archival materials, we examine how Party members conceptualized their own programs, made sense of, and responded to the repressive intervention of state actors and institutions in their attempts to create social change.
Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China
Authors: Chen Zeqiang , Wu Lei , Chen Nengcheng , Wan Ke
Published: January 2025

In estimating the global carbon cycle, the net ecosystem exchange (NEE) is crucial. The understanding of the mechanism of interaction between NEE and various environmental factors of ecosystems has been very limited, and the interactions between the factors are intricate and complex, which leads to difficulties in accurately estimating NEE. In this study, we propose the A-DMLP (attention-deep multilayer perceptron)-deep learning model for NEE simulation as well as an interpretability study using the SHapley Additive exPlanations (SHAP) model. The attention mechanism was introduced into the deep multilayer perceptual machine, and the important information in the original input data was extracted using the attention mechanism. Good results were obtained on nine eddy covariance sites in China. The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. The SHAP method was used to investigate the relationship between the input features of the A-DMLP model and the simulated NEE, and to enhance the interpretability of the model. The results show that the normalized difference vegetation index, the enhanced vegetation index, and the leaf area index play a dominant role at most sites. This study provides new ideas and methods for analyzing the intricate relationship between NEE and environmental factors by introducing the SHAP interpretable model. These advancements are crucial in achieving carbon reduction targets.
An Approach for Spatial Statistical Modelling Remote Sensing Data of Land Cover by Fusing Data of Different Types
Authors: Belmonte Antonella , Riefolo Carmela , Buttafuoco Gabriele , Castrignanò Annamaria
Published: January 2025

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data.
Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities
Authors: Zhang Renzhong , Li Haorui , Shen Yunxiao , Yang Jiayi , Li Wang , Zhao Dongsheng , Hu Andong
Published: January 2025

With the continuous advancement of deep learning algorithms and the rapid growth of computational resources, deep learning technology has undergone numerous milestone developments, evolving from simple BP neural networks into more complex and powerful network models such as CNNs, LSTMs, RNNs, and GANs. In recent years, the application of deep learning technology in ionospheric modeling has achieved breakthrough advancements, significantly impacting navigation, communication, and space weather forecasting. Nevertheless, due to limitations in observational networks and the dynamic complexity of the ionosphere, deep learning-based ionospheric models still face challenges in terms of accuracy, resolution, and interpretability. This paper systematically reviews the development of deep learning applications in ionospheric modeling, summarizing findings that demonstrate how integrating multi-source data and employing multi-model ensemble strategies has substantially improved the stability of spatiotemporal predictions, especially in handling complex space weather events. Additionally, this study explores the potential of deep learning in ionospheric modeling for the early warning of geological hazards such as earthquakes, volcanic eruptions, and tsunamis, offering new insights for constructing ionospheric-geological activity warning models. Looking ahead, research will focus on developing hybrid models that integrate physical modeling with deep learning, exploring adaptive learning algorithms and multi-modal data fusion techniques to enhance long-term predictive capabilities, particularly in addressing the impact of climate change on the ionosphere. Overall, deep learning provides a powerful tool for ionospheric modeling and indicates promising prospects for its application in early warning systems and future research.
Confidence-Aware Ship Classification Using Contour Features in SAR Images
Authors: Al Hinai Al Adil , Guida Raffaella
Published: January 2025

In this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. Additionally, the information entropy is presented as a valuable metric for quantifying the confidence (or uncertainty) associated with classification predictions. Two segmentation methods for the contour extraction were investigated: a classical approach using the watershed algorithm and a U-Net architecture. The features were tested using a support vector machine (SVM) on the OpenSARShip and FUSAR-Ship datasets, demonstrating improved results compared to existing handcrafted features in the literature. Alongside the SVM, a random forest (RF) and a Gaussian process classifier (GPC) were used to examine the effect of entropy derivation from different classifiers while assessing feature robustness. The results show that when aggregating predictions of an ensemble, techniques such as entropy-weighted averaging are shown to produce higher accuracies than methods like majority voting. It is also found that the aggregation of individual entropies within an ensemble leads to a normal distribution, effectively minimizing outliers. This characteristic was utilized to model the entropy distributions, from which confidence levels were established based on Gaussian parameters. Predictions were then assigned to one of three confidence levels (high, moderate, or low), with the Gaussian-based approach showing superior correlation with classification accuracy compared to other methods.
A Scalable Approach to Internet of Things and Industrial Internet of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor for Zero-Day Attack Detection
Authors: Agbedanu Promise Ricardo , Yang Shanchieh Jay , Musabe Richard , Gatare Ignace , Rwigema James
Published: January 2025

The Internet of Things (IoT) and Industrial Internet of Things (IIoT) have drastically transformed industries by enhancing efficiency and flexibility but have also introduced substantial cybersecurity risks. The rise of zero-day attacks, which exploit unknown vulnerabilities, poses significant threats to these interconnected systems. Traditional signature-based intrusion detection systems (IDSs) are insufficient for detecting such attacks due to their reliance on pre-defined attack signatures. This study investigates the effectiveness of Adaptive SAMKNN, an adaptive k-nearest neighbor with self-adjusting memory (SAM), in detecting and responding to various attack types in Internet of Things (IoT) environments. Through extensive testing, our proposed method demonstrates superior memory efficiency, with a memory footprint as low as 0.05 MB, while maintaining high accuracy and F1 scores across all datasets. The proposed method also recorded a detection rate of 1.00 across all simulated zero-day attacks. In scalability tests, the proposed technique sustains its performance even as data volume scales up to 500,000 samples, maintaining low CPU and memory consumption. However, while it excels under gradual, recurring, and incremental drift, its sensitivity to sudden drift highlights an area for further improvement. This study confirms the feasibility of Adaptive SAMKNN as a real-time, scalable, and memory-efficient solution for IoT and IIoT security, providing reliable anomaly detection without overwhelming computational resources. Our proposed method has the potential to significantly increase the security of IoT and IIoT environments by enabling the real-time, scalable, and efficient detection of sophisticated cyber threats, thereby safeguarding critical interconnected systems against emerging vulnerabilities.
Uncertainty-Aware Multimodal Trajectory Prediction via a Single Inference from a Single Model
Authors: Suk Ho , Kim Shiho
Published: January 2025

In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference. Our approach employs deterministic single forward pass methods, optimizing computational efficiency while retaining robust prediction accuracy. By decomposing trajectory prediction into velocity and yaw components and quantifying uncertainty in both, the UAMTP model generates multimodal predictions that account for environmental randomness and intention ambiguity. Evaluation on datasets collected by CARLA simulator demonstrates that our model not only outperforms Deep Ensembles-based multimodal trajectory prediction method in terms of accuracy such as minFDE and miss rate metrics but also offers enhanced time to react for collision avoidance scenarios. This research marks a step forward in integrating efficient uncertainty quantification into multimodal trajectory prediction tasks within resource-constrained autonomous driving platforms.
Long-Gauge Fiber Optic Sensors: Strain Measurement Comparison for Reinforced Concrete Columns
Authors: Lin Haoran , Xu Zhaowen , Hong Wan , Yang Zhihong , Wang Yixin , Li Bing
Published: January 2025

Long-gauge fiber optic sensors have proven to be valuable tools for structural health monitoring, especially in reinforced concrete (RC) beam structures. While their application in this area has been well-documented, their use in RC columns remains relatively unexplored. This suggests a promising avenue for further research and development. This paper presents a thorough comparison of long-gauge fiber optic sensors and traditional measurement tools when used to monitor RC columns under small eccentric compressive loading. The evaluation focuses on the stability and precision of each sensor type. A monitoring system was developed for laboratory testing to assess the performance of various sensor types under specific conditions. The system incorporated four measurement schemes, utilizing a combination of embedded and surface-mounted long-gauge fiber optic sensors, linear variable differential transformers (LVDTs), and point sensors (strain gauges). Long-gauge fiber optic sensors, securely mounted on the concrete surface near the tensile side, were found to accurately measure both large and small deformations, outperforming LVDTs. Compared to strain gauges and embedded optic sensors, the long-gauge fiber optic sensors demonstrated superior average strain measurement and minimal interference from protective covers.
External Load Variability in Elite Futsal: Positional Demands and Microcycle Structuring for Player Well-Being and Performance
Authors: Gadea-Uribarri Héctor , Mainer-Pardos Elena , Bores-Arce Ainhoa , Albalad-Aiguabella Rafael , López-García Sergio , Lago-Fuentes Carlos
Published: January 2025

The aim of this study was to compare the external load of each session along competitive microcycles on an elite futsal team, considering the positions and relationships of the players. The external load of 10 elite players from a First Division team in the Spanish Futsal League (age 27.5 ± 7 years, height 1.73 ± 0.05 m, weight 70.1 ± 3.8 kg) were recorded across 30 microcycles. The players’ external loads were monitored using OLIVER devices. To analyse the external load, Levene’s test was conducted to assess the homogeneity of variances, followed by one-way analysis of variance (ANOVA) to identify differences in dependent variables across the different microcycle days and player positions. Regarding external load during the microcycle, the day with the lowest external load was MD-1, and the days with the highest external load were MD-3 and MD-4. In addition, considering playing positions, pivots exhibited the lowest loads throughout the microcycle, whereas wingers and defenders exhibited the highest loads, depending on the variables analysed. By providing reference values from elite contexts, this study offers practical insights for S&C coaches to optimize microcycles. Furthermore, it contributes to workload management strategies within sport science and public health frameworks, promoting sustainable performance and athlete wellness in futsal.
Sources and Application Modes of Phosphorus in a No-Till Wheat–Soybean Cropping System
Authors: Duart Vanderson M. , Finkler Victor G. , Caires Eduardo F.
Published: January 2025

Phosphate fertilization management under no-till farming has important implications for sustainable agriculture, especially in highly weathered soils. A field experiment examined the effects of phosphorus (P) sources and application modes on soil P levels, plant P nutrition, and grain yields of a wheat–soybean cropping system under no-till. Five cycles of a wheat–soybean crop succession were evaluated on an Oxisol in the period from 2016 to 2021 in the State of Parana, Brazil. The treatments consisted of fertilization with monoammonium phosphate (MAP) and single superphosphate (SSP), in addition to a control without P, to subplots within plots with in-furrow and broadcast P applications. The annual application of 100 kg of P2O5 ha−1 from phosphate sources, either broadcast or in the sowing furrow, was sufficient to maintain an adequate level of P in the soil, supply P demand for the secession of wheat–soybean crops, and obtain high grain yields. In a wheat–soybean cropping system, the application of the fertilizers MAP or SSP-based phosphates in the sowing furrow or broadcast in wheat crop is a strategy that should be encouraged in highly weathered soils under no-till to minimize P fixation to soil particles, improve P-leaf concentration, and increase wheat and soybean grain yields.
Sustainable Entrepreneurial Intention Among University Students: Synergetic Moderation of Entrepreneurial Fear and Use of Artificial Intelligence in Teaching
Authors: Asad Muzaffar , Fryan Latefa Hamad Al , Shomo Mahasin Ibrahim
Published: January 2025

Today, the world is facing two major issues: unemployment and environmental degradation. To promote employment, universities and higher educational institutions all over the world are boosting entrepreneurial intentions among students to overcome the challenge of unemployment. Moreover, in higher educational institutions in developed and developing countries, sustainability is being promoted. However, Pakistan is far behind in applying these concepts simultaneously. Hence, the objective of the study is to promote sustainable entrepreneurial intentions among the students. For the said purpose, considering the inconsistent findings in the literature where the theory of planned behavior is used to cater to entrepreneurial intentions, entrepreneurial fear and the use of artificial intelligence are added to the theory of planned behavior as a moderator with the support of protection motivation theory and experiential learning theory. The data have been collected from 384 students studying at higher educational institutions in Pakistan. The developed framework has been evaluated using SMART PLS3. The findings confirmed that personal attitude, perceived behavioral control, and perceived feasibility significantly influence sustainable entrepreneurial intentions. Moreover, the synergistic moderation of entrepreneurial fear, as well as the use of artificial intelligence in teaching, is significant. This study ends up with policy guidelines and limitations along with future directions.
The Characterization of Sliding/Pinning Behaviors of Water Droplets on Highly Adhesive Hydrophobic Surfaces
Authors: Zhang Jiaru , Zhang Liang , You Jichun
Published: January 2025

Sliding angle (SA) has been widely employed to describe the sliding behaviors of water droplets on substrates. However, it does not work on highly adhesive surfaces since droplets cannot slide along the substrate even at a 90° tilt. In this work, a novel strategy has been developed to characterize the sliding/pinning behaviors based on the collision between a tilted substrate and falling droplets released from a certain height. The critical tilted angle of substrate (i.e., secondary sliding angle, SSA) and the critical releasing height (i.e., Hc) of water droplets have been introduced and measured with the help of commercial equipment (drop shape analysis, DSA). Our results indicate that SSA, Hc and the phase diagram based on the combination of them work well in describing the sliding/pinning behaviors of droplets along highly adhesive substrates. The developed strategy makes it possible to distinguish sliding/pinning behaviors of water droplets along different surfaces as well as different adhesions between them. It can act as an efficient supplement to conventional methods.
Multi-Scale Feature Enhancement Method for Underwater Object Detection
Authors: Li Mengpan , Liu Wenhao , Shao Changbin , Qin Bin , Tian Ali , Yu Hualong
Published: January 2025

With deep-learning-based object detection methods reaching industrial-level performance, underwater object detection has emerged as a significant application. However, it is often challenged by dense small instances and image blurring due to the water medium. In this paper, a Multi-Scale Feature Enhancement(MSFE) method is presented to address the challenges triggered by water bodies. In brief, MSFE attempts to achieve dual multi-scale information integration through the internal structural design of the basic C2F module in the Backbone network and the external global design of the feature pyramid network (FPN). For the internal multi-scale implementation, a LABNK module is constructed to address the vanishing or weakening phenomenon of fine-grained features during feature extraction. Specifically, it adopts a symmetrical structure to collaboratively capture two types of local receptive field information. Furthermore, to enhance the information integration ability between inter-layer features in FPN, a shallow feature branch is injected to supplement detailed features for the subsequent integration of multi-scale features. This operation is mainly supported by the fact that large-sized features from the shallow layer usually carry rich, fine-grained information. Taking the typical YOLOv8n as the benchmark model, extensive experimental comparisons on public underwater datasets (DUO and RUOD) demonstrated the effectiveness of the presented MSFE method. For example, taking the rigorous mAP (50:95) as an evaluation metric, it can achieve an accuracy improvement of about 2.8%.
Larger Size Subspace Codes with Low Communication Overhead
Authors: Wu Lingling , Niu Yongfeng
Published: January 2025

Köetter and Kschischang proposed a coding algorithm for network error correction based on subspace codes, which, however, has a high communication overhead (100%). This paper improves upon their coding algorithm and presents a coding algorithm for network error correction with lower communication overhead, which is similar to the communication overhead of classical random network coding. In particular, we utilize the inherent symmetry in subspace codes to optimize the construction process, leading to a more efficient algorithm. At the same time, this paper also studies the construction problem of constant dimension subspace codes, utilizing parallel construction and multilevel construction. By exploiting the symmetry in these methods, we generalize previous results and derive new lower bounds for constant dimension subspace codes.
Systemic Impacts of National Civilized Cities on Sustainable Development: A Quasi-Experimental Analysis of Economic and Environmental Outcomes in China
Authors: Yang Weixin , Yang Yunpeng , Chen Zhili , Gu Yuxun
Published: January 2025

This paper explores the systemic relationship between urban civilization initiatives and economic prosperity by examining the economic impact of China’s “National Civilized City” (NCC) designation. We begin by synthesizing literature that addresses the complex interactions between urban development and environmental sustainability, emphasizing a systems approach. A theoretical model is developed to capture the dynamic interplay between urban environmental strategies and socio-economic outcomes, framed within the context of the Sustainable Development Goals (SDGs). Employing a quasi-experimental difference-in-differences (DID) method, we analyze data from a panel of Chinese cities (1997–2019) to assess how the NCC title influences economic growth and urban sustainability. Our results indicate a notable 2.88% increase in GDP per capita associated with the designation; however, this effect varies based on the city’s administrative status and geographical location, highlighting the complex and conditional nature of policy impact on economic development. We conclude by offering policy recommendations that promote a holistic approach to urban planning, fostering synergies between environmental conservation and economic growth. These insights contribute to a deeper understanding of sustainable urban development strategies with relevance to both emerging and developed economies.
A Local–Transit Percolation and Clustering-Based Method for Highway Segment Importance Ranking
Authors: Lyu Huizhe , Li Yang , Liu Chenxu , Li Zhonghao , Xu Lin , Wang Wei , Chen Jun
Published: January 2025

The impact of disturbances on a transportation network varies depending on the location and characteristics of the affected highway segments. Given limited resources, it is crucial to prioritize the protection and repair of highway segments based on their importance to maintaining overall network performance during disruptions. This paper proposes a novel method for ranking the importance of highway segments, leveraging a novel local–transit percolation and clustering-based method. Initially, the highway network is constructed by Graph theory, and the k-means clustering method is applied considering each segment’s transit and local traffic flows. Subsequently, a local–transit percolation model is constructed to generate an initial ranking of segments based on the size of the second-largest clusters during the percolation phase transition. A secondary ranking is performed by refining the results from the clustering phase. Results of a control experiment show that, compared to baselines, the proposed ranking approach demonstrates a significantly improved ability to sustain network demand and connectivity when high-ranked segments are moved. The model uncertainty analysis was conducted by adding noise to the gantry records, and the experiments demonstrated that the model exhibits robustness under noisy conditions. These findings highlight the effectiveness and superiority of the proposed method.
Strategies for Mainstreaming Edible Cities with Focus on the City of Lincoln: A Critical Cross-Case Study Analysis of Community Growing Groups in the UK
Authors: Williams Hannah , Elnokaly Amira
Published: January 2025

The socio-political climate in the UK, compounded by government austerity measures, has intensified the cost-of-living crisis, significantly impacting the affordability of the food supply chain. This has led to an increasing disconnect from healthy dietary practices and has contributed to declines in mental and physical wellbeing. This paper examines the role of edible city (EC) initiatives in addressing these challenges by enhancing food security, fostering social resilience and supporting community wellbeing. The aim of this research is to understand the tangible links between EC mechanisms and how they align with local, cultural and geographical perspectives. Focusing on the City of Lincoln, UK, this study aims to develop a contextualised EC framework that is culturally and geographically relevant and aligns with global goals for sustainable urban development as outlined in the United Nation’s Sustainable Development Goal 11 (SDG11), promoting inclusive, environmentally safe and economically viable communities. Utilising a mixed methods approach, this research combines qualitative interviews and a critical cross-case analysis of four community growing groups in the UK, alongside quantitative socio-demographic data analysis. The findings reveal that successful EC initiatives adapt to their environment and socio-cultural conditions and highlight the importance of EC diversification for long-term sustainability. Four core themes emerged across the case studies: (1) social prescribing, (2) social capital (3) knowledge sharing and (4) social value. These findings inform the creation of a novel flexible EC framework, Lincoln, providing actionable insights and policy recommendations to support sustainable urban regeneration and enhance community resilience. This research contributes transferable knowledge to inform EC strategies across diverse global urban contexts, promoting a pathway toward resilient and socially cohesive urban environments.
Hepatitis B Virus in Polish Blood Donors in the Period 2005–2019—Significant Changes in Epidemiology and Demographic Characteristics of Infected Donors
Authors: Kopacz Aneta , Kubicka-Russel Dorota , Liszewski Grzegorz , Sulkowska Ewa , Chrzanowska Anna , Zwolińska Paulina , Noceń Ewa , Potępa Anna , Łętowska Magdalena , Grabarczyk Piotr ,   the Group of Polish Blood Transfusion Centers for Blood Borne Pathogens
Published: January 2025

In the 1980s, Poland was a medium-endemic country, with one of the highest incidences of hepatitis B in Europe (45/105 inhabitants). Pursuant to the WHO guidelines, obligatory vaccination was introduced in 1994–1996 (as a part of hepatitis B prophylaxis for newborns), and in 2000–2011, all 14-year-olds were vaccinated. To prevent transfusion-transmitted HBV infection (TT-HBV), since the 1970s, each donation has been tested for HBsAg and, since 2005, additionally for the presence of HBV DNA. Based on the data from the Blood Transfusion Centers, changes in HBV detection in Polish blood donors were analyzed, starting from the introduction of mandatory NAT screening until 2019. During the period under analysis, a total of 11,625 HBV-infected donors were identified: 97.95% were seropositive (confirmed HBsAg) and 2.05% were seronegative (NAT yields). The detection frequency for both categories of infections was significantly (p = 0.05) higher for men than for women (Residual Risk RR = 1.4 and RR = 2.63, respectively). Seropositive infections were detected more frequently (p < 0.05) in first-time donors than in repeat donors (RR = 360), while no significant differences were observed in the category of seronegative infections. A downward trend in HBsAg detection was observed in both first-time and repeat donors (Spearman’s coefficient R = −0.98 and R = −0.90, respectively). The frequency of HBsAg in first-time donors decreased 5-fold, and, in repeat donors, 30-fold. In both subpopulations, the largest decrease occurred in the age group ≤ 20 years (i.e., donors born between 1985 and 2001). The incidence of window period (WP) infections in the repeat donor group demonstrated a downward trend (R = −0.54, p < 0.05), and in the first-time donor group, no significant trend was recorded. For occult hepatitis B infection (OBI), no significant trend was observed in either donor subpopulation. WP infections were detected significantly more often in donors aged 21–50 years than in donors ≤20 years, most often in the 41–50 age group. The frequency of OBI increased with donor age and was the highest in the 51–60 age group. A spectacular decrease in the frequency of HBsAg(+) infections was observed in current study, indicating the effectiveness of the hepatitis prevention strategy applied in Poland. We expect that the improvement in the epidemiological situation among blood donors causes a reduction in the risk of TT-HBV. Confirmation of this hypothesis by the analysis of residual risk should be a subject of further studies.
Anti-Idiotypic Antibody as a Booster Vaccine Against Respiratory Syncytial Virus
Authors: Mukhopadhyay Shreya , Manolaridis Ioannis , Warren Christopher , Tang Aimin , O’Donnell Gregory , Luo Bin , Staupe Ryan P. , Vora Kalpit A. , Chen Zhifeng
Published: January 2025

Background/Objectives: The respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections in children and adults. With nearly everyone infected by the age of five, there is an opportunity to develop booster vaccines that enhance B-cell immunity, promoting potent and broadly neutralizing antibodies. One potential approach involves using anti-idiotypic antibodies (anti-IDs) to mimic specific antigenic sites and enhance preexisting immunity in an epitope-specific manner. RB1, a monoclonal antibody (mAb) that binds to site IV of the RSV fusion (RSV F) protein, is a potent and broadly neutralizing against RSV A and B viruses. It is the precursor for MK1654 (clesrovimab), which successfully completed a Phase III clinical trial. Methods: In this study, we isolated two anti-IDs, 1A6 and 1D4, targeting RB1 CDR regions, demonstrating that 1A6 competes fully with RSV F in binding to RB1. Results: We resolved the RB1-1A6 and RB1-1D4 Fab-Fab complex structures and proved that 1A6 mimics the RSV F site IV better than 1D4. In an immunogenicity study, mice primed with RSV F and boosted with 1A6 Fab showed a site IV-specific antibody response with a concurrent increase in RSV virus neutralization. Conclusions: These results suggest that anti-IDs could be potentially used as booster vaccines for specific epitopes.
Influence of Spacing on the Retention Process of Cascade Permeable Dams for Upstream Sediment-Laden Flow
Authors: Liu Jian , Zhou Hongwei , Pan Longyang , Li Niannian , Wang Mingyang , Gao Xing , Yang Haoxiang
Published: January 2025

Permeable dams are an important means for river management and ecology protection. Reasonable dam spacing will help regulate sediment transport and reduce sediment load in lakes. Flume experiments were carried out to investigate the effects of hydrological sediment conditions and dam spacing on sediment retention performance and permeability of the cascade permeable dams. The experimental results show that the permeability coefficient of the 1# dam decreased by about 30–40% with a large rate during the initial experiment stage. The decrease amplitude in the permeability coefficient and rising rate of the water level in front of the 1# dam for a large dam spacing (D/L) are positively correlated with the flow rate. At D/L = 5, the water level difference of 1# dam at the end of the experiment was significantly higher than that of other spacing. The sediment mass retained by 1# dam accounts for about 41–65% of the total sediment mass retained, which is about twice that of 2# dam, and plays a major role in cascade permeable dams. A mathematical model for predicting the spatial-temporal sediment concentration inside 1# dam is proposed based on the seepage theory of porous media. The research results are of great guiding significance for the design of the dam parameters.
Optimising Salt Recovery—Four-Year Operational Insights into Na2SO4 Recovery from Saline Waters Using Pipe Freeze-Crystallization
Authors: More Kagiso S. , Maree Johannes P. , Mahlangu Mlungisi
Published: January 2025

Managing high-salinity industrial wastewater poses environmental and operational challenges, particularly in recovering valuable salts like Na2SO4. Traditional methods such as evaporation and distillation are energy-intensive (2200 kJ/kg) and environmentally unsustainable. Addressing these limitations, this study investigates the application and optimisation of pipe freeze-crystallization (PFC), an innovative, energy efficient technology operating at 330 kJ/kg, to achieve zero-waste treatment objectives. This research used OLI ESP software to model the crystallization dynamics, accurately predicting Na2SO4 recovery and reductions in sulphate concentrations from 74.3 g/L to 6.9 g/L at temperatures below −2 °C. The recovered Na2SO4 was analysed using X-ray diffraction with its purity increasing over the years from 50% to 84.9%. Over a four-year operational period at a demonstration plant in Olifantsfontein, South Africa, modifications including extending pipe length from 90 m to 120 m and increasing pipe diameter from 20 mm to 25 mm improved salt recovery rates from 3.5 t/month to 9.1 t/month. Enhanced chiller performance sustained sub-zero temperatures, achieving a cooling capacity of 7 kW while enabling consistent salt and ice recovery. Results showed that feedwater composition substantially influenced crystallization dynamics, with high NaCl concentrations delaying Na2SO4 crystallization. The plant’s adaptability to diverse feedwaters and scalability for broader industrial applications highlights its potential as a cost-effective solution. These findings establish PFC as a transformative technology for sustainable saline wastewater treatment, offering industry compliance with environmental regulations, and economic benefits through resource recovery.
Thoughts on Servant-Leadership in the Modern Era
Authors: Spears Larry C.
Published: January 2025

Thoughts on Servant-Leadership in the Modern Era
The Nature of Grace: An Introduction
Authors: Ferch Shann Ray , Song Jiying
Published: January 2025

The Nature of Grace: An Introduction
Persuasion vs. Coercion or Persuasion and Coercion
Authors: Song Jiying
Published: January 2025

This article explores the nuanced distinctions between persuasion and coercion through Robert K. Greenleaf’s 1977 article “Overcome Evil with Good.” Greenleaf’s comparisons of John Woolman, John Whittier, and Mohandas Gandhi highlight the complexities and consequences of their methods in combating societal evils. Woolman’s gentle persuasion contrasts with Whittier’s fiery abolitionism and Gandhi’s nonviolent resistance, which Greenleaf critiques as coercive and leading to violence. The article examines the controversy sparked by Greenleaf’s views, including reader responses and the impact on his subsequent writings, supported by unpublished manuscripts and correspondence from Greenleaf’s archives. It engages with philosophical perspectives on persuasion and coercion, referencing thinkers like Confucius, Sun Tzu, and phenomenologists such as Husserl and Gadamer, and critiques Greenleaf’s idealistic definition of persuasion, considering historical and social contexts. Ultimately, it argues for a balanced approach, recognizing the potential necessity of both persuasion and coercion in achieving social change. The article concludes by emphasizing the importance of appreciating differing perspectives and the need for both idealists and pragmatists to work together for the common good.
Serendipity in Non-Profit Founding and the Healing Response of Two Servant-Leaders to Crises of Migration
Authors: Von Behren Michael T.
Published: January 2025

This article explores the intersection of non-profit founding with elements of servant-leadership. Drawing upon current research in social entrepreneurship and non-profit founding, the article reflects on these in light of the stories of two selfless individuals, Varian Fry and Enrique Morones, whose lives of servanthood spawned the founding of two influential organizations: the Emergency Rescue Committee (ERC) and the Border Angels. While sharing the stories of these organizations, and of their founding, this article highlights issues of immigration offering a healing perspective through five themes of servant-leadership articulated by Robert K. Greenleaf revealed in the lives of these non-profit founders. Along the way, this article introduces concepts of global citizenship as an antidote to fears that stoke nationalistic and isolationist mindsets, which are mindsets both the ERC and the Border Angels sought to overcome.
Lessons from Servant-Leadership to End Violence Against Women
Authors: Benazzo Federico
Published: January 2025

This article begins by recalling the tragic loss of Giulia Cecchettin, an Italian university student who was brutally murdered by her ex-boyfriend. The exploration delves into the cultural and societal factors that contribute to violence against women, with an emphasis on the role of personal responsibility in perpetuating these harmful dynamics. It proposes the concept of servant-leadership as a means to counteract male dominance and work towards achieving gender equality. The research underscores the importance of self-reflection and forgiveness in understanding and altering individual contributions to gender violence. The final section explores the intersection between servant-leadership and the ethics of care, advocating for these approaches as transformative methods to traditional power structures, with the aim of fostering a more equitable and compassionate society. The study seeks to raise awareness of gender violence and propose actionable strategies for both individual and societal change.
Servant-Leadership Practices of Nelson Mandela, African Culture and a Thriving Future for Africa
Authors: Koshal Jeremiah Ole
Published: January 2025

This paper examines the African cultural values and the virtues of servant-leadership as impetus of the successful leadership of Nelson Mandela, and how all these can be tapped by other African leaders in order to transition the continent into a more prosperous and promising future. Previously, authors have viewed Africa from the lenses of hopelessness due to its corrupt, self-centered and inept leadership that has led to sluggish economic growth and social disharmony. Yet since time immemorial, the African culture has had no dichotomy between service and leadership, as evidence in some of Africa’s cultural philosophies like ubuntu, which focuses on individuals pursuing collective interests and serving others. Ubuntu played a critical role in shaping the leadership journey of Nelson Mandela. Servant-leadership, on the other hand, which closely matches the principles of ubuntu, is believed to have had an impact on the leadership practices of Nelson Mandela (humility, courage, perseverance, honesty and commitment, and forgiveness and reconciliation). It is argued that the leadership practices of Nelson Mandela, which have largely been informed by both ubuntu and servant-leadership, if adopted and practiced by African leaders are likely to make the continent realize wholistic development. For instance, economic integration can deepen the economic and political links in the continent, investment in education will bring about the much needed innovation and entrepreneurship, improved democratic and governance ideals will slay the giant of corruption, and a vibrant digital transformation will place the continent on a global pedestal.