bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Simulation and Experimental Verification of a Low-Temperature Differential Free-Piston Stirling Air Conditioner Under Multi-Harmonic Drive
Authors: Wang Yajuan , Zhang Junan , Guo Junde , Zhang Gao , Zhang Jianhua
Published: January 2025

This study seeks to improve the performance of a low-temperature differential free-piston Stirling air conditioner (FPSAC). To achieve this, a novel approach is proposed, which replaces the conventional simple harmonic drive with a multi-harmonic drive. This modification aims to optimize the motion of the driving piston, bringing it closer to the ideal movement pattern. The research involves both thermodynamic and dynamic coupling simulations of the FPSAC, complemented by experimental verification of its key performance parameters. A thermodynamic model for the gas medium, employing a quasi-one-dimensional dynamic approach for compressible fluids, and a nonlinear two-dimensional vibration dynamic model for the solid piston are developed, focusing on the low-temperature differential FPSAC physical model. The finite difference method is employed to numerically simulate the entire system, including the electromagnetic thrust of the multi-harmonic-driven linear oscillating motor, fluid transport equations, and the nonlinear dynamic equations of the power and gas control pistons. Variations in displacement, velocity, and pressure for each control volume at any given time are obtained, along with the indicator and temperature–entropy diagrams after the system stabilizes. The simulation results show that, in cooling mode, assuming no heat loss or mechanical friction, the Stirling cooler operates at a frequency of 80 Hz. Using the COPsin value for the simple harmonic drive as a baseline, performance is improved by altering the driving method. Under the multi-harmonic drive, the COPc5 increased by 10.03% and COPc7 by 14.23%. In heating mode, the COP under the multi-harmonic drive improved by 0.51% for COPh5 and 2.61% for COPh7. Performance experiments were conducted on the low-temperature differential FPSAC, and the key parameter test results showed good agreement with the simulation outcomes. The maximum deviation at the trough was found to be less than 2.45%, while at the peak, the maximum error did not exceed 3.61%. When compared to the simple harmonic drive, the application of the multi-harmonic drive significantly enhances the overall efficiency of the FPSAC, demonstrating its superior performance. The simulation analysis and experimental results indicate a significant improvement in the coefficient of performance of the Stirling cooler under the multi-harmonic drive at the same power level, demonstrating that the multi-harmonic drive is an effective approach for enhancing FPSAC performance. Furthermore, it should be noted that the method proposed in this study is applicable to other types of low-temperature differential free-piston Stirling air conditioners.
Fault Arc Detection Method Based on Improved ShuffleNet V2 Network
Authors: Huang Yuehua , Lu Yun , Fan Liping , Xiang Kun , Ma Hui
Published: January 2025

Fault arcs exhibit randomness, with current waveforms closely mirroring those of standard nonlinear load operations, posing challenges for traditional series fault arc detection methods. This study presents an improved detection approach using a lightweight convolutional neural network model, ShuffleNet V2. Current data from household loads were collected and preprocessed to establish a comprehensive database, leveraging one-dimensional convolution and channel attention mechanisms for precise analysis. Experimental results demonstrate a high fault arc detection accuracy of 97.8%, supporting real-time detection on the Jetson Nano embedded platform, with an efficient detection cycle time of 15.65 ms per sample. The proposed approach outperforms existing methods in both accuracy and speed, providing a robust foundation for developing advanced fault arc circuit breakers.
Influence of Oxygen Carrier on the Autothermicity of a Chemical-Looping Reforming Process for Hydrogen Production
Authors: López van der Horst Juliana , Volpe Giangiordano Maria Florencia , Suarez Felipe , Perez Federico M. , Gatti Martín N. , Santori Gerardo F. , Pompeo Francisco
Published: January 2025

The chemical-looping reforming (CLR) of methane for hydrogen production employs a solid oxygen carrier (OC) and combines endothermic and exothermic stages, allowing for potential autothermal operation. This study conducted a thermodynamic analysis using Gibbs free energy minimization and energy balances to assess the behavior of WO3, MnWO4, and NiWO4 as OCs in the CLR process. The effects of CH4:OC ratios and reactor temperatures on equilibrium composition and the energy performance were examined. The results demonstrated that elevated reduction temperatures promote OC conversion and the formation of more reduced solid products. Molar ratios above stoichiometric prevent carbon formation, whereas stoichiometric ratios result in higher H2 yield, achieving 98% at 1000 °C. However, these conditions do not support autothermal operation, which requires CH4:OC molar ratios above stoichiometric. Additionally, lower oxidation temperatures are preferred regardless of the OC, due to the lower heat needed to preheat the air, which has a greater effect on the net heat. For the reduction temperature, its effect depends on the type of OC analyzed. The maximum H2 yield obtained under autothermal operation was 88% for the three OCs, at 875 °C for MnWO4 and 775 °C for both WO3 and NiWO4.
Rivers and Lakes: Zhuangzi’s Critique of Just War and the Zhuangzian War Ethics and Peace Strategy
Authors: Lee Ting-mien
Published: January 2025

Just war theory, significantly shaped by Christian moral theology, has provided a prominent ethical framework for discussions on the justice of war. Numerous works have been published on classical Chinese counterparts, including passages from the Mengzi, Mozi, Xunzi, and Huangdi Sijing. Although the Zhuangzi frequently discusses warfare, it has been largely overlooked in this field of research because its war ethics do not revolve around any concept similar to that of justice. Based on Zhuangzian critiques of Confucian and Mohist views on justice, this paper tentatively reconstructs Zhuangzian war ethics. This reconstruction reveals a perspective that the concept of just war or justified war is inherently problematic, and promoting such ideas can be perilous and even morally questionable. The belief that just war theory can help us identify a “just side” to morally condemn or legally restrain aggressors is a dangerous illusion. In practice, this approach fails to prevent wars and often exacerbates conflict. According to the Zhuangzian perspective, during wars, states do not operate as traditional political entities with universal jurisdiction and centralized authority. Instead, they resemble rivers and lakes with indistinct boundaries, rendering the concepts of justice and punishment largely irrelevant. A practical peace strategy, therefore, must abandon the pursuit of administering justice and punishing wrongdoers. Instead, it should focus on strategies that facilitate the involved parties to “forget” their grudges and start their peaceful relationship anew.
Diffuse Large B-Cell Lymphoma with t(1;22)(q21;q11.2) and t(6;18)(p25;q21): A Case Report
Authors: Nagaie Toshiaki , Kubota Yasushi , Hanamura Ichiro , Karnan Sivasundaram , Tomimasu Rika , Akashi Michiaki , Tsuruda Shiho , Takami Akiyoshi , Kimura Shinya , Miyahara Masaharu
Published: January 2025

Background and Clinical Significance: This should include a brief introduction about the general medical condition or relevant symptoms that will be discussed in the case report and should succinctly summarize the critical essential clinical information of the case report and emphasize its new and vital aspects. Case Presentation: A 72-year-old man diagnosed with DLBCL involving chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) showed primary refractory disease after the fourth cycle of R-CHOP. The patient ultimately experienced cardiac involvement due to the lymphoma and received salvage chemotherapy. He passed away about 15 months after the diagnosis of DLBCL. We conducted fluorescence in situ hybridization (FISH) for further analysis of the chromosomal translocations. The breakpoint of chromosome 1q21 was located at a distance of around 151 Mb from the telomeric end of chromosome 1p. The breakpoint in chromosome 22q11 contains the immunoglobulin lambda locus. Furthermore, the breakpoint of chromosome 6p was in the telomeric region of chromosome 6p21. The breakpoint of chromosome 18q21 contains BCL2. Conclusions: This case report presents the first documented co-occurrence of chromosomal translocations t(1;22)(q21;q11.2) and t(6;18)(p25;q21) in a patient with DLBCL. These chromosomal translocations may indicate a worse clinical outcome.
Minimizing Seam Lines in UAV Multispectral Image Mosaics Utilizing Irradiance, Vignette, and BRDF
Authors: Ahn Hoyong , Kim Chansol , Lim Seungchan , Jin Cheonggil , Kim Jinsu , Choi Chuluong
Published: January 2025

Unmanned aerial vehicle (UAV) imaging provides the ability to obtain high-resolution images at a lower cost than satellite imagery and aerial photography. However, multiple UAV images need to be mosaicked to obtain images of large areas, and the resulting UAV multispectral image mosaics typically contain seam lines. To address this problem, we applied irradiance, vignette, and bidirectional reflectance distribution function (BRDF) filters and performed field work using a DJI Mavic 3 Multispectral (M3M) camera to collect data. We installed a calibrated reference tarp (CRT) in the center of the collection area and conducted three types of flights (BRDF, vignette, and validation) to measure the irradiance, radiance, and reflectance—which are essential for irradiance correction—using a custom reflectance box (ROX). A vignette filter was generated from the vignette parameter, and the anisotropy factor (ANIF) was calculated by measuring the radiance at the nadir, following which the BRDF model parameters were calculated. The calibration approaches were divided into the following categories: a vignette-only process, which solely applied vignette and irradiance corrections, and the full process, which included irradiance, vignette, and BRDF. The accuracy was verified through a validation flight. The radiance uncertainty at the seam line ranged from 3.00 to 5.26% in the 80% lap mode when using nine images around the CRT, and from 4.06 to 6.93% in the 50% lap mode when using all images with the CRT. The term ‘lap’ in ‘lap mode’ refers to both overlap and sidelap. The images that were subjected to the vignette-only process had a radiance difference of 4.48–6.98%, while that of the full process images was 1.44–2.40%, indicating that the seam lines were difficult to find with the naked eye and that the process was successful.
Distribution and Structure of China–ASEAN’s Intertidal Ecosystems: Insights from High-Precision, Satellite-Based Mapping
Authors: Zheng Zhang , Jia Renming
Published: January 2025

The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity and essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, and anthropogenic impacts. Accurately mapping intertidal ecosystems and differentiating mangroves, salt marshes, and tidal flats remains a challenge due to inconsistencies in classification frameworks. Here, we present a high-precision mapping approach for intertidal ecosystems using multi-source satellite data, including Sentinel-1, Sentinel-2, and Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, to enable the detailed mapping of intertidal zones across China–ASEAN. Our findings indicate a total intertidal area of 73,461 km2 in China–ASEAN, with an average width of 1.16 km. Analyses of patch area, abundance, and perimeter relationships reveal a power-law distribution with a scaling exponent of 1.52, suggesting self-organizing characteristics shaped by both natural and human pressures. Our findings offer foundational data to guide conservation and management strategies in the region’s intertidal zones and present a novel perspective to propel research on global coastal ecosystems.
Inter-Calibration and Limb Correction of FY-3D/E MWTS for Long-Term Gridded Microwave Brightness Temperature Dataset
Authors: Xia Xinlu , Zeng Mingjian , Luo Xiaochun , Shi Xiao , Jiang Rongsheng , Yuan Xinyi , Sang Xiaozhuo , Tang Fei , Xu Xu
Published: January 2025

The Microwave Temperature Sounder-3 (MWTS-3) onboard the Chinese FengYun-3E (FY-3E) satellite is the third generation of Chinese microwave temperature sounder. Based on MWTS-2, the number of MWTS-3 channels has been increased from 13 to 17, which can observe the atmospheric temperature and water vapor profiles from the surface to the lower stratosphere. In this study, two generations of MWTSs onboard FY-3D/3E were inter-calibrated by the Double Difference (DD) method to eliminate bias. The results showed that the biases of tropospheric channels were stable (within 1 K) and the biases of stratospheric channels were relatively large (over 2 K). In addition, the weighting functions of all MWTS channels varied with fields of view (FOVs) due to different optical paths, causing the brightness temperature (TB) observations to display strong scan-dependent features, i.e., the limb effect. This work used a limb correction method to remove scan-dependent patterns so that the underlying weather signals could be uncovered. After inter-calibration and limb correction, this work converted the TB observations from MWTS-2/3 onto a global gridded dataset at 0.5° × 0.5° latitudinal and longitudinal resolutions using a method of nested interpolation. Based on this research, more long-term FengYun series satellite climate datasets can be established in the future.
Can Measurement and Input Uncertainty Explain Discrepancies Between the Wheat Canopy Scattering Model and SMAPVEX12 Observations?
Authors: Wijesinghe Lilangi , Western Andrew W. , Aryal Jagannath , Ryu Dongryeol
Published: January 2025

Realistic representation of microwave backscattering from vegetated surfaces is important for developing accurate soil moisture retrieval algorithms that use synthetic aperture radar (SAR) imagery. Many studies have reported considerable discrepancies between the simulated and observed backscatter. However, there has been limited effort to identify the sources of errors and contributions quantitatively using process-based backscatter simulation in comparison with extensive ground observations. This study examined the influence of input uncertainties on simulated backscatter from a first-order radiative transfer model, named the Wheat Canopy Scattering Model (WCSM), using ground-based and airborne data collected during the SMAPVEX12 campaign. Input uncertainties to WCSM were simulated using error statistics for two crop growth stages. The Sobol’ method was adopted to analyze the uncertainty in WCSM-simulated backscatters originating from different inputs before and after the wheat ear emergence. The results show that despite the presence of wheat ears, uncertainty in root mean square (RMS) height of 0.2 cm significantly influences simulated co-polarized backscatter uncertainty. After ear emergence, uncertainty in ears dominates simulated cross-polarized backscatter uncertainty. In contrast, uncertainty in RMS height before ear emergence dominates the accuracy of simulated cross-polarized backscatter. These findings suggest that considering wheat ears in the model structure and precise representation of surface roughness is essential to accurately simulate backscatter from a wheat field. Since the discrepancy between the simulated and observed backscatter coefficients is due to both model and observation uncertainty, the uncertainty of the UAVSAR data was estimated by analyzing the scatter between multiple backscatter coefficients obtained from the same targets near-simultaneously, assuming the scatter represents the observation uncertainty. Observation uncertainty of UAVSAR backscatter for HH, VV, and HV polarizations are 0.8 dB, 0.87 dB, and 0.86 dB, respectively. Discrepancies between WCSM-simulated backscatter and UAVSAR observations are discussed in terms of simulation and observation uncertainty.
Comparative Analysis of Carbon Density Simulation Methods in Grassland Ecosystems: A Case Study from Gansu Province, China
Authors: Wu Luyao , Du Jiaqiang , Liu Xinying , Li Lijuan , Zhu Xiaoqian , Chen Xiya , Gong Yue , Li Yushuo
Published: January 2025

An accurate assessment of grassland carbon stocks is essential for understanding their role in China’s terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in theoretical foundations, variable selection, and algorithmic implementation, introduces significant uncertainty into estimating grassland carbon density. This study focuses on the grassland ecosystems in Gansu Province, China, employing both an overall approach (without distinguishing between grassland types) and a stratified approach, classifying the grassland into seven distinct types: alpine meadow steppe, temperate steppe, lowland meadow, alpine meadow, mountain meadow, shrubby grassland, and temperate desert. Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). The applicability of each model was then systematically compared and analyzed. The main conclusions are as follows: (1) The overall estimation results demonstrate that the GWR model is the optimal choice for inverting AGBC, DOMC, and SOC, with coefficients of determination (R2) of 0.67, 0.60, and 0.92, respectively. In contrast, the MGWR model is best suited for BGBC, with an R2 value of 0.73. (2) The stratified estimation results suggest that the optimal inversion models for AGBC and BGBC are predominantly the MGWR and RF models selected through the recursive feature elimination algorithm. For DOMC, the optimal model is a spatial model, while SOC is most accurately estimated using the GWR and RF models selected via the Boruta algorithm. (3) When comparing the inversion results of the optimal overall and stratified approaches, the stratified estimations of AGBC, BGBC, and DOMC (R2 = 0.80, 0.78, and 0.73, respectively) outperformed those of the overall approach. In contrast, the SOC estimates followed an opposite trend, with the overall approach yielding a higher R2 value of 0.92. (4) Generally, variable selection significantly enhanced model accuracy, with spatial and non-parametric models demonstrating superior precision and stability in estimating the four carbon density components of grassland. These findings provide methodological guidance for converting sample point carbon density data into regional-scale estimates of grassland carbon storage.
Reconstruction of Sea Surface Chlorophyll-a Concentration in the Bohai and Yellow Seas Using LSTM Neural Network
Authors: Xu Qing , Yang Guiying , Yin Xiaobin , Sun Tong
Published: January 2025

In order to improve the spatiotemporal coverage of satellite Chlorophyll-a (Chl-a) concentration products in marginal seas, a physically constrained deep learning model was established in this work to reconstruct sea surface Chl-a concentration in the Bohai and Yellow Seas using a Long Short-Term Memory (LSTM) neural network. Adopting the permutation feature importance method, time sequences of several geographical and physical variables, including longitude, latitude, time, sea surface temperature, salinity, sea level anomaly, wind field, etc., were selected and integrated to the reconstruction model as input parameters. Performance inter-comparisons between LSTM and other machine learning or deep learning models was conducted based on OC-CCI (Ocean Color Climate Change Initiative) Chl-a product. Compared with Gated Recurrent Unit, Random Forest, XGBoost, and Extra Trees models, the LSTM model exhibits the highest accuracy. The average unbiased percentage difference (UPD) of reconstructed Chl-a concentration is 11.7%, which is 2.9%, 7.6%, 10.6%, and 10.5% smaller than that of the other four models, respectively. Over the majority of the study area, the root mean square error is less than 0.05 mg/m3 and the UPD is below 10%, indicating that the LSTM model has considerable potential in accurately reconstructing sea surface Chl-a concentrations in shallow waters.
Detection Method for Bolt Loosening Based on Summation Coefficient of Absolute Spectrum Ratio
Authors: Guo Haoyang , Zhong Jianfeng , Feng Bin , Chen Yulong , Zhong Shuncong
Published: January 2025

A bolt loosening detection method based on the summation coefficient of the absolute spectrum ratio technique is proposed to address the prevalent issue of bolt loosening in mechanical connections. This proposed method involves initially collecting vibration and rotation speed signals of the motor bolt connection structure, acquiring the baseline spectrum curve of a healthy structure and the spectrum curves of non-healthy structures under different degrees of bolt looseness through chirp Fourier transform (CFT). Subsequently, the spectrum ratio curves between healthy and non-healthy structures are calculated for different degrees of bolt loosening, and then the Summation Coefficient of Absolute Spectrum Ratio (SCASR) is defined to indicate the looseness. In the mathematical model, a linear relationship is observed between the SCASR and the frequency shift of the resonance peak. To standardize the results of bolt loosening detection, the SCASR could be divided by the number of points in the fixed frequency band to obtain the average of SCASR as the detection index for bolt loosening. Finally, a linear fitting equation is established between the bolt torque and the average of SCASR, so that the change in the average value can be used to determine whether the bolt is loose and evaluate the severity of bolt looseness. The detection performance of this proposed method has been effectively validated through both simulation and experiments. Experimental results indicate that the proposed method can effectively detect bolt loosening, particularly in its early stages, using low-frequency band data in three axes.
MDAPT: Multi-Modal Depth Adversarial Prompt Tuning to Enhance the Adversarial Robustness of Visual Language Models
Authors: Li Chao , Liao Yonghao , Ding Caichang , Ye Zhiwei
Published: January 2025

Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models. We conducted extensive experiments and significantly improved performance on three datasets (ϵ=4/255). Compared with traditional manual design prompts, the accuracy and robustness increased by an average of 17.84% and 10.85%, respectively. Not only that, our method still has a very good performance improvement under different attack methods. With our efficient settings, compared with traditional manual prompts, our average accuracy and robustness are improved by 32.16% and 21.00%, respectively, under three different attacks.
Description and Classification of Training Drills, Based on Biomechanical and Physiological Load, in Elite Basketball
Authors: Sosa Carlos , Alonso-Pérez-Chao Enrique , Ribas Carlos , Schelling Xavier , Lorenzo Alberto
Published: January 2025

The aim of this study was to understand and describe the physiological and biomechanical demands of various tasks used in basketball training and, subsequently, to provide a practical application of these tasks in a typical training week. Twelve basketball players had their external load variables monitored across 179 training sessions (2896 samples) using local positioning system technology. These variables included total distance covered, distance covered at various intensity levels, accelerations, decelerations, PlayerLoad™, and explosive efforts. The analysis revealed significant differences in both physiological and biomechanical loads across various drills. Specifically, tasks with more space and fewer defenders, such as 3v0 full court, impose higher physiological loads compared to tasks with less space and more defenders, like 5v5 full court. The difference in physiological load between these tasks was statistically significant (p < 0.05) with a moderate effect size (ES: −0.60, 95% CI: [−0.99, −0.22]). In terms of biomechanical load, drills with increased defensive pressure, such as 5v5 full court, exhibited significantly higher values compared to less specific drills, such as 5v0 full court, with a very large effect size (ES: 1.37, 95% CI: [1.04, 1.70], p < 0.01). Additionally, comparisons between 5v5 full court and 3v0 full court for biomechanical load produced a very large effect size (ES: 1.67, 95% CI: [1.37, 1.97], p < 0.01), indicating a substantial difference in load demands. The results indicate that tasks with more space and fewer defenders impose higher physiological loads, while those with less space and more defenders increase the biomechanical load. For training design, it is recommended to schedule tasks with a higher biomechanical load at the beginning of the session and those with a physiological orientation toward the end. Understanding the distinct demands of different drills can help coaches structure training sessions more effectively to optimize player load and performance development throughout the week.
Review of Short-Wavelength Infrared Flip-Chip Bump Bonding Process Technology
Authors: Du Junhao , Zhao Xuewei , Su Jiale , Li Ben , Duan Xiangliang , Dong Tianyu , Lin Hongxiao , Ren Yuhui , Miao Yuanhao , Radamson Henry H.
Published: January 2025

Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm. These advancements stimulate new prospects across a wide array of fields including life sciences, medical diagnostics, defense, surveillance, security, free-space optics (FSO), thermography, agriculture, food inspection, and LiDAR applications. In this review, we begin by introducing monolithic SWIR image sensors and hybrid SWIR image sensors and indicate that flip-chip bump bonding technology remains the predominant integration method for hybrid SWIR image sensors owing to its outstanding performance, adaptable integration with innovative epitaxial SWIR materials, long-term stability, and long-term reliability. Subsequently, we comprehensively summarize recent advancements in epitaxial thin-film SWIR sensors, encompassing FPAs and flip-chip bump bonding technology for epitaxial InGaAs and Ge (Sn) thin-film SWIR sensors. Finally, a summary and outlook regarding the development of InGaAs and Ge (Sn) SWIR sensors are provided and discussed. The ongoing evolution of epitaxial thin-film SWIR sensors with flip-chip bump bonding technology is poised to foster new applications in both academic and industry fields.
Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite–Ground Scenario
Authors: Cao Hui , Han Shujun , Meng Rui , Xu Xiaodong , Zhang Ping
Published: January 2025

Satellite–ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications. Nevertheless, current SemCom methods still struggle to adapt to varying channel conditions. To address this, we propose a SemCom transmission model based on a Channel Code-Book (CCB) for adaptive image transmission in diverse channel environments. Our model reconstructs and restores the original image by documenting fading and noise states under various channel conditions and dynamically adjusting the denoiser’s model parameters. Extensive experimental results demonstrate that our CCB model outperforms three representative baseline models, including Deep JSCC, ASCN, and WITT in various environments and task conditions, achieving an advantage of more than 10 dB under high signal-to-noise ratio conditions.
Efficacy of Segmentation for Hyperspectral Target Detection
Authors: Furth Yoram , Rotman Stanley R.
Published: January 2025

Algorithms for detecting point targets in hyperspectral imaging commonly employ the spectral inverse covariance matrix to whiten inherent image noise. Since data cubes often lack stationarity, segmentation appears to be an attractive preprocessing operation. Surprisingly, the literature reports both successful and unsuccessful segmentation cases, with no clear explanations for these divergent outcomes. This paper elucidates the conditions under which segmentation might improve detector performance. Focusing on a representative algorithm and assuming a target additive model, the study examines all influential factors through theoretical analysis and extensive simulations. The findings offer fundamental insights and practical guidelines for characterizing segmented datasets, enabling a thorough evaluation of segmentation’s utility for detector performance. They outline the range of target scenarios and parameters where segmentation may prove beneficial and help assess the potential impact of proposed segmentation strategies on detection outcomes.
Recurrent Flow Update Model Using Image Pyramid Structure for 4K Video Frame Interpolation
Authors: Lee Sangjin , Shin Chajin , Kang Hong-Goo , Lee Sangyoun
Published: January 2025

Video frame interpolation (VFI) is a task that generates intermediate frames from two consecutive frames. Previous studies have employed two main approaches to extract the necessary information from both frames: pixel-level synthesis and flow-based methods. However, when synthesizing high-resolution videos using VFI, each approach has its limitations. Pixel-level synthesis based on the transformer architecture requires high complexity to achieve 4K video results. In the case of flow-based methods, forward warping can produce holes where pixels are not allocated, while backward warping approaches struggle to obtain accurate backward flow. Additionally, there are challenges during the training stage; previous works have often generated suboptimal results by training multi-stage model architectures separately. To address these issues, we propose a Recurrent Flow Update (RFU) model trained in an end-to-end manner. We introduce a global flow update module that leverages global information to mitigate the weaknesses of forward flow and gradually correct errors. We demonstrate the effectiveness of our method through several ablation studies. Our approach achieves state-of-the-art performance not only on the XTest and Davis datasets, which have 4K resolution, but also on the SNU-FILM dataset, which features large motions at low resolution.
The Glocalization of Sport: A Research Field for Social Innovation
Authors: Li Zijing , Waquet Arnaud , Campillo Philippe
Published: January 2025

This article explores the emergence of the concept of glocalization in contemporary societies and more precisely, the glocalization of sport to highlight a social innovation borne through the interaction between global dynamics and local specificities in the development of sport. The glocalization, considered as a theorical framework to rethink local social innovation which answered to the limit of the globalization, is examined in this article through an in-depth bibliographic analysis conducted using the Lillocat metasearch engine (covering 354 articles since 1992 across 11 thematic areas, 29 of which are related to sports). The use of IRaMuTeQ software version 0.7 alpha2 (Interface for Multidimensional Text and Questionnaire Analysis) enabled comprehensive textual and lexical analyses, including frequency analyses, hierarchical classifications, and principal component analyses (PCA). These analyses were presented in the form of figures such as histograms, dendrograms, and word clouds, thereby facilitating the identification of lexical co-occurrence relationships and the understanding of emerging trends. Findings reveal that glocalization enables sports organizations to integrate global strategies with local cultural identities, fostering fan engagement, economic sustainability, and cultural diversity. Global events such as the FIFA World Cup, the NBA’s international expansion, and the Tour de France illustrate how global frameworks adapt to local contexts. Glocalization emerges as a strategic driver of social innovation, balancing internationalization with localized adaptation. This study provides insights for researchers and practitioners, considering glocalization as a theorical framework useful for the analysis of a local social innovation in the context of globalization and sport as a propitious field of research.
Enhancing CO2 Sequestration Through Corn Stalk Biochar-Enhanced Mortar: A Synergistic Approach with Algal Growth for Carbon Capture Applications
Authors: Sinyoung Suthatip , Jeeraro Ananya , Udomkun Patchimaporn , Kunchariyakun Kittipong , Graham Margaret , Kaewlom Puangrat
Published: January 2025

This study examines corn stalk biochar (CSB)-enhanced mortar as an innovative material for carbon capture and CO2 sequestration. CSB, a renewable agricultural byproduct, was incorporated into cement mortar at varying concentrations (2.5% to 75%), and its effects on the mortar’s physicochemical properties, its ability to support algal growth, and the CO2 absorption capacity of the algae were analyzed. Characterization of CSB showed a high carbon content (62.3%), significant porosity, and a large surface area (680.3 m2 g−1), making it ideal for gas capture. At low concentrations (2.5%), CSB slightly improved the mortar’s compressive strength and density. However, higher CSB levels (5% to 75%) led to significant reductions (p < 0.05) in strength and density, while water absorption increased. CO2 sequestration monitored from algal growth studies revealed that both Chlorella sp. (TISTR 8262) and Scenedesmus sp. (TISTR 9384) thrived on CSB-enhanced mortars. At a 75% CSB concentration, Scenedesmus sp. achieved a 24.2-fold increase in biomass by day 12, outperforming Chlorella sp., which showed a 26.6-fold increase. CO2 absorption also improved with biochar. Mortars with 75% CSB achieved an 86% CO2 absorption ratio without algae, while adding algae boosted this to nearly 100%, highlighting the synergistic effect of biochar and algal photosynthesis. Higher CSB levels accelerated CO2 absorption stabilization, reaching saturation by day 8 at 75% CSB. Scenedesmus sp. showed slightly higher CO2 absorption efficiency than Chlorella sp., reaching peak absorption earlier and maintaining greater efficiency. Higher CSB concentrations accelerated CO2 absorption, indicating that biochar–mortar mixtures, particularly when combined with algae, provide a promising solution for enhancing carbon capture and sequestration in green infrastructure.
The Role of Neglected Grain Legumes in Food and Nutrition Security and Human Health
Authors: Vilakazi Busisiwe , Mafongoya Paramu L. , Odindo Alfred O. , Phophi Mutondwa M.
Published: January 2025

Increasing demand for nutritious, safe, and healthy food, including the need to preserve biodiversity and other resources, signifies a prodigious challenge for agriculture, which is already at risk from climate change. Diverse and healthy plant-based diets may significantly reduce food insecurity, malnutrition, diet-related diseases, and other health-related issues. More attention to agricultural systems diversity is mandatory to improve the economic, environmental, ecological, and social sustainability of food production in developing countries. In this context, neglected legume production could significantly provide nutritional and healthy benefits for people while adhering to sustainability principles. However, the contribution of neglected legumes to food and nutrition security is still limited due to socio-economic challenges faced by farmers that contribute to the underutilization of neglected legumes, leading to overreliance on a few legumes with poor resilience to climatic perturbations, thus posing a risk to sustainable food production. While major legumes offer higher economic returns and more developed value chains, they also contribute to environmental degradation and resource depletion. Neglected legumes, on the other hand, provide ecosystem services, promote biodiversity, and offer climate resilience but face economic challenges due to limited market demand and underdeveloped value chains. Consequently, food nutritional insecurity and human health concerns remain prevalent, especially in developing countries. There is an urgent need to promote neglected legumes in agricultural systems through policy change implementation, genetic improvement, and development, fostering international cooperation to share knowledge, technologies, and best practices in the production and utilization of neglected legumes. This review comprehensively explores the utility of neglected legumes for food, nutritional security, and human health. It identifies knowledge gaps that should be prioritized as part of research strategies for sustainable future food systems in sub-Saharan Africa.
Does China’s Low-Carbon City Pilot Policy Effectively Enhance Urban Ecological Efficiency?
Authors: Ma Xin , Sun Tianli
Published: January 2025

The low-carbon city pilot (LCCP) policy represents a pioneering approach to fostering sustainable development. It offers a scientific framework to reconcile the relationship between economic growth, resource utilization, and environmental protection. This study measures urban ecological efficiency (UEE) through the non-radial directional distance function (NDDF) model using the panel data of 284 cities in China, from 2007 to 2021, and analyzes the impact of the LCCP policy on UEE, adopting a multi-period difference-in-differences (DID) model. The results of the baseline regression indicate that the pilot cities exhibit an average ecological efficiency that is approximately 3.0% higher than that observed in non-pilot cities, which pass both the parallel trend test and the robustness test. Mechanism analysis reveals that industrial upgrading and energy consumption reduction are the primary pathways through which the LCCP policy enhances UEE. In addition, the policy effects are particularly significant in improving UEE in non-resource-based cities, large cities, and cities in the eastern region. Finally, the spatial spillover effects demonstrated by the LCCP policy can effectively inform neighboring cities of strategies to enhance their UEE. The research findings provide invaluable insight and direction for China’s efforts in the development of low-carbon cities and ecological sustainability.
Carbon Emission Evaluation System for Foundation Construction Based on Entropy–TOPSIS and K-Means Methods
Authors: Chen Yuan , He Genglong , Fang Yuan , Li Dongxu , Wang Xi
Published: January 2025

Green construction evaluation systems can assist building stakeholders in scientifically evaluating the carbon emission performance of construction projects. However, most green construction evaluation tools and methods fail to explicitly incorporate construction carbon emission indicators, let alone a quantitative evaluation. Therefore, this study proposes a carbon emission evaluation system based on the entropy–TOPSIS and K-means methods for foundation construction projects. In this study, we innovatively divided the carbon emission of the foundation construction process into three phases, namely, transportation emission, excavation and earthwork emission, and pile work emission, considering their different emission characteristics and reduction difficulties by nature. Different from traditional carbon evaluation methods, the carbon emission of the three phases were evaluated separately against the baseline value obtained from local construction quota. After that, the emission performance of the three phases was weighted and evaluated based on the entropy–TOPSIS method, and then rated via the K-means method. Based on a case study of 19 residential buildings, the weights of the three construction phases were 27.66% (transportation), 42.34% (excavation and earthwork), and 29.99% (pile work). The carbon performance of the 19 cases were rated by the K-means method into four levels: six cases were rated “Excellent”, five were rated “Good”, five were rated “Fair”, and three were rated “Poor”. The proposed method was expected to objectively and scientifically evaluate and rate the carbon emission of the foundation construction process, and provided a theoretical basis for decision makers to identify emission hotspots and formulate specific carbon reduction measures.
Towards Sustainable Development: Can Industrial Intelligence Promote Carbon Emission Reduction
Authors: Xu Hanqing , Cao Zhengxu , Han Dongqing
Published: January 2025

The realization of intelligent transformation is an important path for the industry to move towards low-carbon development. Based on panel data from 30 provinces in China, this study utilizes the intermediate effect model and spatial econometric model to analyze the influence of industrial intelligence on carbon emissions. The research reveals that industrial intelligence helps with carbon reduction, and the result is still valid after undergoing various tests. Industrial intelligence relies on green technological innovation, industrial structure upgrading, and energy intensity to realize carbon reduction. There is a spatial spillover role of industrial intelligence on carbon emissions, which has a positive influence on carbon reduction in local and adjoining regions. The influence of industrial intelligence on carbon emissions exhibits heterogeneity in the regional dimension, time dimension, and industrial intelligence level dimension. The research provides empirical evidence and implications for using artificial intelligence to achieve carbon reduction.
Knowledge Graph Analysis in Climate Action Research
Authors: Ge Ran , Xia Yu , Ge Liquan , Li Fei
Published: January 2025

Climate change is a global challenge for humanity, and climate action is essential to address its impacts. With the purpose of building a clear theoretical framework for the research field of climate action and to gain a deeper understanding, this paper conducts a bibliometric analysis of 28,457 articles, which were selected from WoS data sources. These articles were analyzed through VOSviewer and CiteSpace, with the aims of exploring publication growth trends and categories, co-authorship analysis, national and regional collaboration, organization cooperation, co-citation journals, citations, keywords, and funding information. Subsequently, a knowledge graph for climate action was constructed, emerging trends were analyzed, and a clear theoretical framework was established. The research outcome offers effective, substantive, and forward-looking suggestions for the sustainable development of climate action.
GDT Framework: Integrating Generative Design and Design Thinking for Sustainable Development in the AI Era
Authors: Chen Yongliang , Qin Zhongzhi , Sun Li , Wu Jiantao , Ai Wen , Chao Jiayuan , Li Huaixin , Li Jiangnan
Published: January 2025

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts.
Efficiency Evaluation and Resource Optimization of Forestry Carbon Sequestration Projects: A Case Study of State-Owned Forest Farms in Fujian Province
Authors: You Meizhu , Huang Yan , Wu Nan , Yuan Xiangzhou
Published: January 2025

Forestry carbon sink projects are an important pathway for achieving China’s carbon neutrality goal, with state-owned forest farms playing a leading role in the development of projects. This study collected data from 14 forestry carbon sink projects in Fujian Province, which are primarily led by state-owned forest farms. Using Data Envelopment Analysis (DEA), the operational efficiency of each forestry carbon sink project was evaluated and calculated, followed by a study on resource optimization allocation based on the efficiency evaluation results. The conclusions are as follows: management capability and climate conditions are key factors affecting the efficiency of state-owned forest farms. The investment required for developing forestry carbon sink projects hinders small-scale forest farms from engaging in such projects.