bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Active Learning Methodologies into Clinical Nutrition Education for Nursing Students: A Quasi-Experimental Study
Authors: Mancin Stefano , Cangelosi Giovanni , Sguanci Marco , Matteucci Sofia , Morenghi Emanuela , Lopane Diego , Cattani Daniela , Cosmai Simone , Vinciguerra Giulia , Parozzi Mauro , Morales Palomares Sara , Mazzoleni Beatrice
Published: February 2025

Background: Clinical nutrition is essential in nursing care, with nurses playing a key role in supporting patients’ dietary needs. Aim: To assess the impact of active learning methodologies on clinical nutrition education, focusing on knowledge retention among nursing students. Methods: Using a quasi-experimental research design, we enrolled 131 s-year nursing students. Both groups attended an eight-hour seminar on clinical nutrition, incorporating critical thinking. The experimental group had prior exposure to active learning and ICT in their first-year course, while the control group received traditional teaching. Knowledge was assessed using a validated questionnaire on basic and clinical nutrition. Results: The experimental group scored higher in both basic and clinical nutrition, indicating that active learning improves immediate learning and long-term retention. One year later, they retained significant knowledge, particularly regarding nutrient–disease relationships. Conclusions: Active learning, particularly critical thinking, enhances short- and long-term outcomes in clinical nutrition education. Future studies should refine assessment tools and explore further integration of active learning into nursing curricula.
A Spatial Structure of Key Tree Species Metrodorea nigra St. Hill. (Rutaceae) Is Associated with Historical Disturbance and Isolation in Southeastern Brazil
Authors: Moraes Filho Rômulo Maciel de , Bonifácio-Anacleto Fernando , Alzate-Martinez Fabio Alberto , Martinez Carlos Alberto , Alzate-Marin Ana Lilia
Published: February 2025

The semi-deciduous Brazilian Atlantic Forest has faced intense fragmentation, impacting Metrodorea nigra St. Hill., a fly-pollinated and autochorous tree. We investigated population structure, inbreeding, and spatial genetic structure (SGS) across adult (Adu) and juvenile (Juv) generations in three fragmented populations of M. nigra in Ribeirão Preto, São Paulo, Brazil. We tested whether the magnitude of these effects could result from its mating system, seed dispersal, anthropogenic disturbances, matrix, and fragment size. Populations affected by selective logging, fire, and trail openings include M13-Rib (84 ha) and FAC-Crav (8 ha), both surrounded by sugar cane and BSQ-Rib (3 ha) in an urban matrix. We evaluated phenological events and germination rates in the BSQ-Rib fragment. We sampled leaves and amplified their DNA using ISSR (UBC 1, 2, 820, 834, 851, 858, 860, 886) and SSR (Mtn 1, 3, 13, 16, 19, 87, 95) molecular markers. Fst, PCoA, and AMOVA values suggest a lack of generational isolation, with most variance within generations. Inbreeding values were significant in all populations (Fis and Fit, p = 0.001), probably intensified by natural seed dispersal and pollinator behavior favoring geitonogamy. However, fragmentation, anthropogenic disturbances, and the surrounding matrix influenced SGS. The urban BSQ-Rib fragment recorded the highest SGS values (26 m Juv, 24 m Adu [ISSR]; 7 m Juv, 9 m Adu [SSR]), which may result in low fruit and seed production and germination rates. Despite being the largest fragment, M13-Rib shows SGS in the first distance class (19 m Juv, 24 m Adu [ISSR]; 0 m Juv, and 10 m Adu [SSR]), possibly due to selective logging and fire. FAC-Crav, a more conserved fragment, showed no SGS in adults but punctual SGS in juveniles (27 m [ISSR] and 8 m [SSR]), pointing to it as a promising source for seed collections for reforestation purposes. In summary, inbreeding in M. nigra, influenced by pollinator behavior and seed dispersal, along with fragmentation, anthropogenic disturbances, and the surrounding matrix, are critical in shaping SGS. These factors potentially impact the reproductive success of M. nigra and their long-term survival in the face of climate change.
Chemical and Biological Study of the Essential Oil Isolated from Fruits of Citrus x limonia
Authors: Valarezo Eduardo , Paucar-Costa Jailene , Morales-Guamán Belén , Caraguay-Martínez Alfredo , Jaramillo-Fierro Ximena , Cumbicus Nixon , Meneses Miguel Angel
Published: February 2025

Citrus x limonia is an aromatic species, known locally in Ecuador as limón mandarina or limón chino. In the present study, the chemical composition and biological activity of the essential oil isolated from this species were determined. The essential oil was extracted through hydrodistillation. The chemical composition and enantiomeric distribution of the essential oil were determined by gas chromatography. Antimicrobial activity was determined using the broth microdilution method against tree Gram-positive cocci, a Gram-positive bacillus, four Gram-negative bacilli, a fungus, and a yeast. The antioxidant activity was determined through ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. The spectrophotometric method was used to determine anticholinesterase activity. In the essential oil, thirty-nine compounds were identified, which represented 99.35% of the total composition. Monoterpene hydrocarbons were the most representative group in terms of number of compounds (thirteen) and in terms of relative abundance (91.39%). The main constituents were found to be limonene (57.38 ± 1.09%), γ-terpinene (13.01 ± 0.37%), and β-pinene (12.04 ± 0.63%). Five pairs of enantiomers were identified in the essential oil from fruits of Citrus x limonia. The essential oil presented a minimum inhibitory concentration of 4000 μg/mL against Aspergillus niger. The antioxidant activity of essential oil was weak per the ABTS method, with a SC50 of 1.26 mg/mL. Additionally, the essential oil exhibited moderate anticholinesterase activity, with an IC50 of 203.9 ± 1.03 µg/mL. This study provides the first comprehensive analysis of the chemical composition and biological activities of the essential oil from fruits of Citrus x limonia.
Mechanistic Aspects of the Chemical Reactions in a Three-Way Catalytic Converter Containing Cu and Platinum Group Metals
Authors: Papadopoulos Christos , Kourtelesis Marios , Dimaratos Athanasios , Moschovi Anastasia Maria , Yakoumis Iakovos , Samaras Zissis
Published: February 2025

Strict gaseous emission standards are applied globally to regulate the maximum amounts of pollutant emissions that can be produced from all vehicles. The exhaust aftertreatment systems used by automotive manufacturers rely on the utilization of precious metals (Pt, Pd, Rh). However, much effort has been devoted on the reduction or the replacement of the amount of Platinum Group Metals (PGMs) in three-way catalysts (TWC), both from a cost-effectiveness as well as an environmental point of view. PROMETHEUS catalyst, which was recently homologated for Euro 6 applications, is a low-cost, Cu-based TWC, which consists of a significantly lower quantity of PGMs compared to conventional state-of-the-art catalysts and achieves similar or even better catalytic efficiencies. In this review paper, a complex reaction scheme is proposed for the first time for a catalytic converter utilizing Cu and PGMs, following an extensive literature investigation of the available models. The scheme also accounts for the surface reaction mechanisms of the main processes and the side reactions potentially taking place during the TWC operation in the presence of Cu and at least one of the following PGMs: Pt, Pd or Rh. At a next step, the proposed reaction scheme will be validated based on experimental data, using mathematical modelling of a PROMETHEUS catalytic converter incorporating Cu and PGM nanoparticles.
Green Tea Polyphenols in Wound Healing: Therapeutic Mechanisms, Potential Applications and Challenges in Commercial Use for Diabetic Wound Healing
Authors: Tajammal Syeda Attiqa , Coffey Austin , Tan Shiau Pin
Published: February 2025

Wound healing remains a critical challenge in healthcare, especially with the increasing prevalence of diabetes and its associated complications, such as diabetic foot ulcers (DFUs). Delayed wound healing in diabetic patients is attributed to several factors, with a pathophysiology that is diverse and multifaceted, including decreased immune responses, microvascular dysfunction, oxidative stress and impaired collagen synthesis. Additionally, the development of an infection at the wound site further complicates and hinders the healing process, especially in DFUs. Epigallocatechin gallate (EGCG), a potent bioactive compound found in green tea, has shown promising effects in modulating various stages of wound healing by its antioxidant, anti-inflammatory and anti-bacterial properties in vitro and in vivo. This review discusses delayed wound healing in diabetic wounds, while highlighting the therapeutic potential of GT/EGCG in different stages of wound healing, including in diabetic wounds both in vitro and in vivo. Novel applications such as GT-/EGCG-loaded wound dressings have demonstrated significant positive impacts on wound healings, including in diabetic wounds, both in in vitro and in vivo studies. Despite its therapeutic potential, the commercial application of green tea in wound care faces significant challenges, including issues of bioavailability, stability, cytotoxicity, production costs, the lack of in-depth and complete in vivo studies and, most importantly, the lack of clinical trials. By consolidating current knowledge and identifying gaps, this review aims to inspire future research and innovations in using green tea for effective wound management and potential use in diabetic patients and DFUs, if the positive results from animal in vivo studies are equally effective in human clinical studies.
Confronting the Hidden Dimensions of the Moral Life: A Caribbean Catholic Contribution
Authors: Perkins Anna Kasafi
Published: February 2025

This article contributes to the reimagining of Roman Catholic ethics in the twenty-first century, building on the research of Sweeny Block, who argues that the unconscious dimensions of the moral life play a decisive role in moral agency. By taking account of the work of researchers in moral psychology, the traditional boundaries of moral theology can be reimagined to give a more accurate accounting of moral agency, leading to improved work in moral formation. This interdisciplinary approach engages the experiences of Catholic thinkers from the Global South, whose experiences are not usually countenanced in theorising on the nature of morality. The discussion presents a Caribbean refinement of Bandura’s eight mechanisms of moral disengagement, which are amplified and distilled into culturally relevant attitudes captured in the everyday language or speech events of the Jamaican people. Roman Catholic ethics have not treated with the concept of moral disengagement in any meaningful fashion. The amplification of the mechanisms of moral disengagement points to and reinforces the inadequacy of models of moral agency that disregard unconscious dimensions while centring rationality and free will in the face of human fallibility and social contexts that are distorting and deforming. It points to storytelling, an important part of the Caribbean culture, as one way to improve our moral agency by expanding the moral imagination to better form our moral vision.
Perfect Fools: Sanctity, Madness, and the Theory of Ambiguous Performance
Authors: Madigan Todd
Published: February 2025

The theory of cultural pragmatics describes the process through which social actors guide their symbolic performances. However, this theory has not paid sufficient attention to the audience’s role in constructing the meaning of these performances. Instead of positing that the intended meanings performed by actors are internal states to be either correctly or incorrectly perceived by an audience, my contention is that the purported intentions of actors are in fact audience constructions that are ascribed to actors. I support this claim through the development of what I call ambiguous performances. These are performances that combine elements of incompatible social roles. This combination creates a multistable performance, one that allows for one of several mutually exclusive interpretations. Because of this, ambiguous performances compel each audience member to choose between multiple conflicting interpretations, and in order to choose an interpretation, audience members must look beyond the performance to aspects of their own identity. I illustrate this process through an analysis of holy fools, historical figures who have appeared periodically throughout Christendom over the course of many centuries. These individuals were sometimes canonized as saints, but more often simply considered insane, an interpretive dichotomy that reveals the ambiguous nature of their performances.
The Southern Celestial Star Deities and the Refining and Transcendence Doctrine of the Ancient Lingbao Scriptures
Authors: Shi Qinsheng
Published: February 2025

In Daoist cosmology, the southern celestial star deities are represented by terms such as the Southern Dipper, the Southern Palace, the Southern Chang, and the South Pole, each with specific cosmological significance. These concepts are closely intertwined with the themes of longevity and fire-refining, yet they often blend together to such an extent that clear distinctions become difficult. Through an exploration of this series of concepts, this paper reveals that during the Six Dynasties, the ancient Lingbao scriptures inherited the mythological tradition of earlier religions, in which “fire” symbolized the alchemical refining process. These scriptures extol “fire” as a powerful force for purification and regeneration, and it is said that the Primordial Heavenly Lord once employed flames to refine the “true scriptures”, which represented the cosmic primordial essence. Such fiery transformation finds vivid expression in the legend of A-Qiuzeng. By bathing in sacred flames, this female ascetic underwent bodily transformation into a male form, exemplifying fire’s role as a catalyst for the transformation of existence. Crucially, the Lingbao scriptures utilized the Five Elements doctrine (with fire corresponding to the south) to synthesize the disparate cults of southern celestial star deities into a coherent system. This synthesis highlights the sacred religious function of the southern celestial star deities in “refining death and transcending life” through fire. Moreover, it distills their philosophical significance in mediating the transitions between life and death. Since their compilation during the Six Dynasties, this paradigm has continuously influenced the development of Daoist doctrines of refining and transcendence, as well as their corresponding ritual frameworks.
Law as a “Mystic Order”: A Critical Approach to Legal Hermeneutics from the Perspective of Mysticism
Authors: Pérez-Lasserre Diego
Published: February 2025

This paper aims to challenge interpretivism as an inadequate approach to legal hermeneutics, offering a novel response to the issue through the lens of mysticism. We will follow the premise, developed by thinkers from the traditional philosophical canon such as Derrida, Schmitt and Gadamer (among others), that every act of legal interpretation contains an apophatic dimension. However, we will delve deeper into this notion from the perspective of mysticism itself (rather than deconstructionism or hermeneutics). That is, we will approach interpretivism from a perspective different from the one usually found in the legal-philosophical canon—namely, mysticism—with the aim of understanding and illuminating some of its dimensions. Specifically, we will explore the mystical idea that, while recognizing the dynamic and vital nature of existence is essential, it is even more fundamental to orient existential vitality toward truth through an act of concentration and will. This will allow us to justify that the concept of “mystic order”, which we formulate and develop in this paper, is useful in addressing the challenges of juridical hermeneutics, as it combines conceptual rigor in approaching law (hence “order”) with the vitality, immeasurability, ineffability, and dynamism inherent in concrete reality (the “mystical” aspect).
Religiosity of Evangelical Patients in the Perception of Clinical and Hospital Psychologists in Brazil
Authors: Ruas Evelyn , Freitas Marta Helena de
Published: February 2025

The research reported in this paper aimed to investigate how Brazilian psychologists describe their experiences and dealings with the evangelical religiosity (ER) of patients/users in clinical settings. The descriptive empirical–phenomenological method proposed by Amedeo Giorgi was used to analyse the recorded and transcribed interviews with fifteen Brazilian psychologists, seven of them working in a Psychosocial Attention Centrepsychosocial attention centre (CAPS) and eight working in a hospital setting. According to both groups of participants, ER is always present in both clinical contexts, manifested by users of CAPS and hospital inpatient services, as well as by their families and the health professionals themselves. The psychologists shared the implications of ER, whether dynamic or damaging, for patients’ mental health and for their treatment. Even with the predominant sense that it gives meaning to peoples’ lives and re-adnsignifies their suffering, ER is also seen as an obstacle to the promotion of health when it is characterized by strict and oppressive doctrines and dogmas. Clinical psychologists were unanimous in affirming their tendency to respect and accept such religious manifestations, but they also shared their difficulties in dealing with them, mainly due to their failure to approach the topic during their professional training.
Data-Driven Cooperative Localization Algorithm for Deep-Sea Landing Vehicles Under Track Slippage
Authors: Wei Zhenzhuo , Guo Wei , Lan Yanjun , Liu Ben , Sun Yu , Gao Sen
Published: February 2025

The deep-sea landing vehicle (DSLV) swarm exploration system is a novel platform for the detection of marine mineral resources. A high-precision cooperative localization system with Ultra-Short Baseline (USBL), Doppler Velocity Log (DVL), and electronic compass (EC) plays a vital role in the DSLV swarm exploration system. However, DVL measurements can be seriously interrupted due to the complex operational underwater environment, leading to unstable localization performance. The accuracy of the cooperative localization system could be further degraded by the persistent rubber track slippage during the vehicle’s movement over the soft seabed. In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. First, a velocity prediction model for DVL measurements is constructed using multi-output least squares support vector regression (MLSSVR), and a genetic algorithm (GA) is further employed to optimize the model’s hyperparameters in order to enhance the robustness of the framework. Furthermore, the outputs of MLSSVR are fed into a DSLV position estimation framework based on the Unscented Kalman Filter (UKF) to improve localization accuracy in the presence of DVL failures. To validate the proposed method, the RecurDyn multibody dynamics simulation platform is applied for data synthesis, accounting for both the impact of the soft seabed and real-world motion simulation. The experimental results indicate that during DVL failure, the proposed algorithm can effectively compensate for the cooperative localization errors caused by track slippage, thereby significantly improving the accuracy and reliability of the DSLV cooperative localization system.
Quantifying Tree Structural Change in an African Savanna by Utilizing Multi-Temporal TLS Data
Authors: Muumbe Tasiyiwa Priscilla , Baade Jussi , Raumonen Pasi , Coetsee Corli , Singh Jenia , Schmullius Christiane
Published: February 2025

Structural changes in savanna trees vary spatially and temporally because of both biotic and abiotic drivers, as well as the complex interactions between them. Given this complexity, it is essential to monitor and quantify woody structural changes in savannas efficiently. We implemented a non-destructive approach based on Terrestrial Laser Scanning (TLS) and Quantitative Structure Models (QSMs) that offers the unique advantage of investigating changes in complex tree parameters, such as volume and branch length parameters that have not been previously reported for savanna trees. Leaf-off multi-scan TLS point clouds were acquired during the dry season, using a Riegl VZ1000 TLS, in September 2015 and October 2019 at the Skukuza flux tower in Kruger National Park, South Africa. These three-dimensional (3D) data covered an area of 15.2 ha with an average point density of 4270 points/m2 (0.015°) and 1600 points/m2 (0.025°) for the 2015 and 2019 clouds, respectively. Individual tree segmentation was applied on the two clouds using the comparative shortest-path algorithm in LiDAR 360(v5.4) software. We reconstructed optimized QSMs and assessed tree structural parameters such as Diameter at Breast Height (DBH), tree height, crown area, volume, and branch length at individual tree level. The DBH, tree height, crown area, and trunk volume showed significant positive correlations (R2 > 0.80) between scanning periods regardless of the difference in the number of points of the matched trees. The opposite was observed for total and branch volume, total number of branches, and 1st-order branch length. As the difference in the point densities increased, the difference in the computed parameters also increased (R2 < 0.63) for a high relative difference. A total of 45% of the trees present in 2015 were identified in 2019 as damaged/felled (75 trees), and the volume lost was estimated to be 83.4 m3. The results of our study showed that volume reconstruction algorithms such as TreeQSMs and high-resolution TLS datasets can be used successfully to quantify changes in the structure of savanna trees. The results of this study are key in understanding savanna ecology given its complex and dynamic nature and accurately quantifying the gains and losses that could arise from fire, drought, herbivory, and other abiotic and biotic disturbances.
Terrestrial and Martian Paleo-Hydrologic Environment Systematic Comparison with ASI PRISMA and NASA CRISM Hyperspectral Instruments
Authors: Zinzi Angelo , Manzari Paola , Camplone Veronica , Ammannito Eleonora , Sindoni Giuseppe , Zucca Francesco , Polenta Gianluca
Published: February 2025

The comparative analysis of hyperspectral data from different instruments can provide detailed information on the composition and geology of similar environments on different planets. This study aims to compare data acquired from the PRISMA satellite, used for Earth observation, with those collected by the CRISM spectrometer onboard the Mars Reconnaissance Orbiter, orbiting Mars, in order to analyze the geological and mineralogical differences between the morphologies present on the two planets of interest. The comparison of these data will allow us to examine the mineralogical composition, highlighting the similarities and differences between the terrestrial and Martian environments. In particular, in this study, we present a method to refine the interpretation of spectral features of minerals commonly found in paleo-hydrological environments on Mars and identified also by field analysis of similar terrestrial sites, thus allowing us to improve the Martian sites’ characterization. Thanks to this approach, we have been able to find spectral similarities (e.g., band positions, band ratios) among specific Earth and Mars sites, thus demonstrating that it could be further expanded, by systematically using Earth-observation orbiting instruments to better characterize and constrain Martian spectral data.
Contrastive Feature Disentanglement via Physical Priors for Underwater Image Enhancement
Authors: Li Fei , Wan Li , Zheng Jiangbin , Wang Lu , Xi Yue
Published: February 2025

Underwater image enhancement (UIE) serves as a fundamental preprocessing step in ocean remote sensing applications, encompassing marine life detection, archaeological surveying, and subsea resource exploration. However, UIE encounters substantial technical challenges due to the intricate physics of underwater light propagation and the inherent homogeneity of aquatic environments. Images captured underwater are significantly degraded through wavelength-dependent absorption and scattering processes, resulting in color distortion, contrast degradation, and illumination irregularities. To address these challenges, we propose a contrastive feature disentanglement network (CFD-Net) that systematically addresses underwater image degradation. Our framework employs a multi-stream decomposition architecture with three specialized decoders to disentangle the latent feature space into components associated with degradation and those representing high-quality features. We incorporate hierarchical contrastive learning mechanisms to establish clear relationships between standard and degraded feature spaces, emphasizing intra-layer similarity and inter-layer exclusivity. Through the synergistic utilization of internal feature consistency and cross-component distinctiveness, our framework achieves robust feature extraction without explicit supervision. Compared to existing methods, our approach achieves a 12% higher UIQM score on the EUVP dataset and outperforms other state-of-the-art techniques on various evaluation metrics such as UCIQE, MUSIQ, and NIQE, both quantitatively and qualitatively.
The Impact of MERRA-2 and CAMS Aerosol Reanalysis Data on FengYun-4B Geostationary Interferometric Infrared Sounder Simulations
Authors: Peng Weiyi , Weng Fuzhong , Ye Chengzhi
Published: February 2025

Aerosols significantly impact the brightness temperature (BT) in thermal infrared (IR) channels, and ignoring their effects can lead to relatively large observation-minus-background (OMB) bias in radiance calculations. The accuracy of aerosol datasets is essential for BT simulations and bias reduction. This study incorporated aerosol reanalysis datasets from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS) into the Advanced Radiative Transfer Modeling System (ARMS) to compare their impacts on BT simulations from the Geostationary Interferometric Infrared Sounder (GIIRS) and their effectiveness in reducing OMB biases. The results showed that, for a sandstorm event on 10 April 2023, incorporating total aerosol data from the MERRA-2 improved the BT simulations by 0.56 K on average, surpassing CAMS’s 0.11 K improvement. Dust aerosols notably impacted the BT, with the MERRA-2 showing a 0.17 K improvement versus CAMS’s 0.06 K due to variations in the peak aerosol level, thickness, and column mass density. Improvements for sea salt and carbonaceous aerosols were concentrated in the South China Sea and Bay of Bengal, where the MERRA-2 outperformed CAMS. For sulfate aerosols, the MERRA-2 excelled in the Bohai Sea and southern Bay of Bengal, while CAMS was better in the northern Bay of Bengal. These findings provide guidance for aerosol assimilation and retrieval, emphasizing the importance of quality control and bias correction in data assimilation systems.
Fine-Grained Aircraft Recognition Based on Dynamic Feature Synthesis and Contrastive Learning
Authors: Wan Huiyao , Nurmamat Pazlat , Chen Jie , Cao Yice , Wang Shuai , Zhang Yan , Huang Zhixiang
Published: February 2025

With the rapid development of deep learning, significant progress has been made in remote sensing image target detection. However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead to frequency deviations and loss of local detail information, affecting fine-grained object recognition; (2) class imbalance and long-tail distributions further degrade the performance of minority categories; (3) large intra-class variations and small inter-class differences make it difficult for traditional deep learning methods to effectively extract fine-grained discriminative features. To address these issues, we propose a novel remote sensing aircraft recognition method. First, to mitigate the loss of local detail information, we introduce a learnable Gabor filter-based texture feature extractor, which enhances the discriminative feature representation of aircraft categories by capturing detailed texture information. Second, to tackle the long-tail distribution problem, we design a dynamic feature hallucination module that synthesizes diverse hallucinated samples, thereby improving the feature diversity of tail categories. Finally, to handle the challenge of large intra-class variations and small inter-class differences, we propose a contrastive learning module to enhance the spatial discriminative features of the targets. Extensive experiments on the large-scale fine-grained datasets FAIR1M and MAR20 demonstrate the effectiveness of our method, achieving detection accuracies of 53.56% and 89.72%, respectively, and surpassing state-of-the-art performance. The experimental results validate that our approach effectively addresses the key challenges in remote sensing aircraft recognition.
A Geometric Calibration Method for Spaceborne Single-Photon Lasers That Integrates Laser Detectors and Corner Cube Retroreflectors
Authors: Liu Ren , Xie Junfeng , Mo Fan , Yang Xiaomeng , Jiang Zhiyu , Hong Liang
Published: February 2025

Geometric calibration, as a crucial method for ensuring the precision of spaceborne single-photon laser point cloud data, has garnered significant attention. Nonetheless, prevailing geometric calibration methods are generally limited by inadequate precision or are unable to accommodate spaceborne lasers equipped with multiple payloads on a single platform. To overcome these limitations, a novel geometric calibration method for spaceborne single-photon lasers that integrates laser detectors with corner cube retroreflectors (CCRs) is introduced in this study. The core concept of this method involves the use of triggered detectors to identify the laser footprint centerline (LFC). The geometric relationships between the triggered CCRs and the LFC are subsequently analyzed, and CCR data are incorporated to determine the coordinates of the nearest laser footprint centroids. These laser footprint centroids are then utilized as ground control points to perform the geometric calibration of the spaceborne single-photon laser. Finally, ATLAS observational data are used to simulate the geometric calibration process with detectors and CCRs, followed by conducting geometric calibration experiments with the gt2l and gt2r beams. The results demonstrate that the accuracy of the calibrated laser pointing angle is approximately 1 arcsec, and the ranging precision is better than 2.1 cm, which verifies the superiority and reliability of the proposed method. Furthermore, deployment strategies for detectors and CCRs are explored to provide feasible implementation plans for practical calibration. Notably, as this method only requires the positioning of laser footprint centroids using ground equipment for calibration, it provides exceptional calibration accuracy and is applicable to single-photon lasers across various satellite platforms.
Structured Bayesian Super-Resolution Forward-Looking Imaging for Maneuvering Platforms Based on Enhanced Sparsity Model
Authors: Guo Yiheng , Liang Yujie , Liang Yi , Sun Xiangwei
Published: February 2025

Sparse reconstruction-based imaging techniques can be utilized to solve forward-looking imaging problems with limited azimuth resolution. However, these methods perform well only under the traditional model for the platform with low speed, and the performance deteriorates for the maneuvering trajectory. In this paper, a structured Bayesian super-resolution forward-looking imaging algorithm for maneuvering platforms under an enhanced sparsity model is proposed. An enhanced sparsity model for maneuvering platforms is established to address the reconstruction problem, and a hierarchical Student-t (ST) prior is designed to model the distribution characteristics of the sparse imaging scene. To further leverage prior information about structural characteristics of the scatterings, coupled patterns among neighboring pixels are incorporated to construct a structured sparse prior. Finally, forward-looking imaging parameters are estimated using the expectation/maximization-based variational Bayesian inference. Numerical simulations validate the effectiveness of the proposed algorithm and the superiority over conventional methods based on pixel sparse assumptions in forward-looking scenes for maneuvering platforms.
A Big Data Approach for the Regional-Scale Spatial Pattern Analysis of Amazonian Palm Locations
Authors: Drouillard Matthew J. , Cummings Anthony R.
Published: February 2025

Arecaceae (palms) are an important resource for indigenous communities as well as fauna populations across Amazonia. Understanding the spatial patterns and the environmental factors that determine the habitats of palms is of considerable interest to rainforest ecologists. Here, we utilize remotely sensed imagery in conjunction with topography and soil attribute data and employ a generalized cluster identification algorithm, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), to study the underlying patterns of palms in two areas of Guyana, South America. The results of the HDBSCAN assessment were cross-validated with several point pattern analysis methods commonly used by ecologists (the quadrat test for complete spatial randomness, Morista Index, Ripley’s L-function, and the pair correlation function). A spatial logistic regression model was generated to understand the multivariate environmental influences driving the placement of cluster and outlier palms. Our results showed that palms are strongly clustered in the areas of interest and that the HDBSCAN’s clustering output correlates well with traditional analytical methods. The environmental factors influencing palm clusters or outliers, as determined by logistic regression, exhibit qualitative similarities to those identified in conventional ground-based palm surveys. These findings are promising for prospective research aiming to integrate remote flora identification techniques with traditional data collection studies.
Progress in Atmospheric Density Inversion Based on LEO Satellites and Preliminary Experiments for SWARM-A
Authors: Bian Xiaoyu , Xiao Cunying , Song Shuli , Wu Mengjun
Published: February 2025

The vigorous development of Low Earth Orbit (LEO) satellite constellation programs imposes higher requirements for the accuracy of satellite orbit determination. Significant variations in atmospheric density within the operational region of LEO satellites are primary factors influencing their orbital decay and operational lifespan. This article first summarizes the research advancements in atmospheric density inversion utilizing LEO satellites, comparing and analyzing the principles of various algorithms, factors affecting accuracy, as well as the advantages and disadvantages associated with different acquisition methods. Subsequently, we introduce recent progress in enhancing atmospheric density inversion algorithms and data analysis applications based on LEO satellites. The SWARM-A satellite, equipped with a high-precision GPS receiver and accelerometer, was employed to invert atmospheric density using both semi-long axis attenuation and accelerometer methodologies. The inversion results were compared against empirical models to validate their reliability; specifically, the correlation coefficient between the semi-long axis attenuation method and nrlmsise00 reached 0.9158, while that between the accelerometer method and nrlmsise00 attained 0.9204. Notably, the inversion accuracy achieved by the accelerometer slightly surpasses that of the semi-long axis attenuation method. These findings provide valuable support for predicting large air tightness based on LEO satellite orbit data inversions and for adjusting operational orbits to ensure successful execution of satellite missions.
Monitoring Abiotic Stressors in Rainfed Vineyards Involves Combining UAV and Field Monitoring Techniques to Enhance Precision Management
Authors: Moresi Federico Valerio , Cirigliano Pasquale , Rengo Andrea , Brunori Elena , Biasi Rita , Scarascia Mugnozza Giuseppe , Maesano Mauro
Published: February 2025

Future climate conditions may jeopardize the suitability of traditional grape-growing areas in the Mediterranean. However, precise vineyard management is a crucial component of adaptation strategies aimed at optimizing resource efficiency, which is essential for sustainable farming practices. A fine-scale characterization, based on the spatial variability of soil’s physical–chemical and hydrological traits combined with temporal variability of vine canopy temperature extracted from UAV thermal images has been adopted in a rainfed vineyard of central Italy, for better understanding the impact of soil and climate abiotic factors in the vineyard for planning precision adaptation strategies encouraging sustainable resource use. This study identifies significant soil heterogeneity within the tested vineyard, affecting water retention, nutrient availability, and vine water stress. We combined ground-based measurements with remote sensing-enhanced data spatialization and helped to advocate for site-specific management techniques as short- and long-term strategies (such as canopy management, deficit irrigation, and compost application) to counter climate emergencies, restore soil health, and preserve vine function and economic yields.
Utilizing IoT Sensors and Spatial Data Mining for Analysis of Urban Space Actors’ Behavior in University Campus Space Design
Authors: Koszewski Krzysztof , Olszewski Robert , Pałka Piotr , Walczak Renata , Korpas Przemysław , Dąbrowska-Żółtak Karolina , Wyszomirski Michał , Czeranowska-Panufnik Olga , Manujło Andrzej , Szczepankowska-Bednarek Urszula , Kuźmicz-Kubiś Joanna , Szalwa Anna , Ejsmont Krzysztof , Czernic Paweł
Published: February 2025

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial–temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas—the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors’ behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process of redesigning the WUT campus, but also to support the process of discussion and activation of the community in the development of deliberative democracy and participatory shaping of space in general.
Non-Invasive Biomarkers in the Era of Big Data and Machine Learning
Authors: Lazaros Konstantinos , Adam Styliani , Krokidis Marios G. , Exarchos Themis , Vlamos Panagiotis , Vrahatis Aristidis G.
Published: February 2025

Invasive diagnostic techniques, while offering critical insights into disease pathophysiology, are often limited by high costs, procedural risks, and patient discomfort. Non-invasive biomarkers represent a transformative alternative, providing diagnostic precision through accessible biological samples or physiological data, including blood, saliva, breath, and wearable health metrics. They encompass molecular and imaging approaches, revealing genetic, epigenetic, and metabolic alterations associated with disease states. Furthermore, advances in breathomics and gut microbiome profiling further expand their diagnostic scope. Even with their strengths in terms of safety, cost-effectiveness, and accessibility, non-invasive biomarkers face challenges in achieving monitoring sensitivity and specificity comparable to traditional clinical approaches. Computational advancements, particularly in artificial intelligence and machine learning, are addressing these limitations by uncovering complex patterns in multi-modal datasets, enhancing diagnostic accuracy and facilitating personalized medicine. The present review integrates recent innovations, examines their clinical applications, highlights their limitations and provides a concise overview of the evolving role of non-invasive biomarkers in precision diagnostics, positioning them as a compelling choice for large-scale healthcare applications.
Critical Suicide Notes: On Witnessing and Prefigurative Politics
Authors: Ansloos Jeffrey P. , White Jennifer H.
Published: February 2025

This paper reimagines the study of suicide as a critical, relational practice rooted in solidarity and transformative possibilities. Moving beyond the limitations of conventional suicidology, this work emphasizes the importance of attending to the broader social, political, and structural contexts that shape experiences of suicidality. By framing this work as a collection of “notes,” this paper calls for an approach that notices, marks, and responds to both the violence and resistance inherent in these experiences. This paper introduces witnessing, dreaming, and prefiguration as key methodologies for Critical Suicide Studies. Witnessing is conceptualized as an active and relational practice that centers on the lived realities of those affected by suicide, making their stories and the systems of harm that often go unaddressed, visible. Dreaming involves imagining futures beyond survival, where care and justice guide collective responses. Prefiguration focuses on enacting these futures in the present, embedding relational and community-based approaches in the everyday practices of suicide care and research. Through these practices, this paper explores how Critical Suicide Studies can move from critique to action, creating conditions in which responses to suicide are life-affirming, relational, and grounded in mutual care. This work aspires to cultivate spaces for collective healing, dignity and transformative change.
The Comparative Effects of High-Intensity Interval Training and Traditional Resistance Training on Hormonal Responses in Young Women: A 10-Week Intervention Study
Authors: Ramadan Wael , Xirouchaki Chrysovalantou E. , El-Gilany Abdel-Hady
Published: February 2025

Background: Hormonal levels in women are influenced by exercise intensity and modality. Methods: This 10-week study compared high-intensity interval training (HIIT) and traditional resistance training (TRT) in 72 young women. Hormonal levels (estrogen, testosterone, FSH, prolactin, and LH) were measured pre- and post-intervention. Results: Both groups showed significant increases in estrogen (HIIT: 150%; TRT: 72.3%) and decreases in testosterone (HIIT: 58%; TRT: 49%), FSH (HIIT: 6%; TRT: 7.7%), and PL (HIIT: 5%; TRT: 2.1%). There are no significant changes in LH. Conclusions: HIIT and TRT effectively modulate hormonal profiles, potentially benefiting reproductive and metabolic health.
Thermodynamic Optimization of Building HVAC Systems Through Dynamic Modeling and Advanced Machine Learning
Authors: Moveh Samuel , Merchán-Cruz Emmanuel Alejandro , Ibrahim Ahmed Osman , Elhassan Zeinab Abdallah Mohammed , Ramadan Abdelhai Nada Mohamed , Abdelrazig Mona Dafalla
Published: February 2025

This study enhances thermodynamic efficiency and demand response in an office building’s HVAC system using machine learning (ML) and model predictive control (MPC). This study, conducted in a simulated EnergyPlus 8.9 environment integrated with MATLAB (R2023a, 9.14), focuses on optimizing the HVAC system of an office building in Jeddah, Kingdom of Saudi Arabia. Support vector regression (SVR) and deep reinforcement learning (DRL) were selected for their regression accuracy and adaptability in dynamic environments, with exergy destruction analysis used to assess thermodynamic efficiency. The models, integrated with MPC, aimed to reduce exergy destruction and improve demand response. Simulations evaluated room temperature prediction, HVAC energy optimization, and energy cost reduction. The DRL model showed superior prediction accuracy, reducing energy costs by 21.75% while keeping indoor temperature increase minimal at 0.12 K. This simulation-based approach demonstrates the potential of combining ML and MPC to optimize HVAC energy use and support demand response programs effectively.
Assessing Greenhouse Gas Emissions in Urban Water Management: Scenarios Analysis for Mitigation
Authors: Shim Intae , Kim Eunju , Nam Sook-Hyun , Koo Jae-Wuk , Lee Juwon , Park Jeongbeen , Kye Homin , Shin Yonghyun , Hwang Tae-Mun
Published: February 2025

Urban water systems are essential infrastructure but significantly contribute to greenhouse gas emissions through their operation. This study analyzed the greenhouse gas emissions of Incheon’s water system and proposed effective reduction strategies. In 2021, total greenhouse gas emissions from Incheon’s water system are 410,407 tCO2eq, with the sanitation sector accounting for 82.1% and water supply for 17.9%. N2O from wastewater treatment contributes 59.2% of total emissions, followed by CO2 (36.6%) and CH4 (4.2%). Sensitivity analysis using system dynamics identified per capita water consumption (LPCD) reduction as the most impactful mitigation strategy, surpassing widely adopted strategies such as renewable energy adoption. Scenario analysis showed that an aggressive policy could reduce emissions by 28.8% by 2050 compared to the baseline scenario. These findings provide a decision-making policy for carbon-neutral urban water management, emphasizing the need for integrated approaches to water management, emphasizing water demand reduction, energy efficiency, and sludge management.