bluuebunny/crossref_metadata_2025 huggingface dataset:
An Overview of Mechanical Properties of Diamond-like Phases under Tension
Authors: Baimova Julia A.
Published: January 2024

Diamond-like phases are materials with crystal lattices very similar to diamond. Recent results suggest that diamond-like phases are superhard and superstrong materials that can be used for tribological applications or as protective coatings. In this work, 14 stable diamond-like phases based on fullerenes, carbon nanotubes, and graphene layers are studied via molecular dynamics simulation. The compliance constants, Young’s modulus, and Poisson’s ratio were calculated. Deformation behavior under tension is analyzed based on two deformation modes—bond rotation and bond elongation. The results show that some of the considered phases possess very high Young’s modulus (E≥1) TPa, even higher than that of diamond. Both Young’s modulus and Poisson’s ratio exhibit mechanical anisotropy. Half of the studied phases are partial auxetics possessing negative Poisson’s ratio with a minimum value of −0.8. The obtained critical values of applied tensile strain confirmed that diamond-like phases are high-strength structures with a promising application prospect. Interestingly, the critical limit is not a fracture but a phase transformation to the short-ordered crystal lattice. Overall, our results suggest that diamond-like phases have extraordinary mechanical properties, making them good materials for protective coatings.
Research on Fabrication of Phononic Crystal Soft-Supported Graphene Resonator
Authors: Zheng Xiande , Liu Ying , Zhen Jiapeng , Qiu Jing , Liu Guanjun
Published: January 2024

In aviation, aerospace, and other fields, nanomechanical resonators could offer excellent sensing performance. Among these, graphene resonators, as a new sensitive unit, are expected to offer very high mass and force sensitivity due to their extremely thin thickness. However, at present, the quality factor of graphene resonators at room temperature is generally low, which limits the performance improvement and further application of graphene resonators. Enhancing the quality factor of graphene resonators has emerged as a pressing research concern. In a previous study, we have proposed a new mechanism to reduce the energy dissipation of graphene resonators by utilizing phononic crystal soft-supported structures. We verified its feasibility through theoretical analysis and simulations. This article focuses on the fabrication of a phononic crystal soft-supported graphene resonator. In order to address the issues of easy fracture, deformation, and low success rate in the fabrication of phononic crystal soft-supported graphene resonators, we have studied key processes for graphene suspension release and focused ion beam etching. Through parameter optimization, finally, we have obtained phononic crystal soft-supported graphene resonators with varying cycles and pore sizes. Finally, we designed an optical excitation and detection platform based on Fabry–Pérot interference principle and explored the impact of laser power and spot size on phononic crystal soft-supported graphene resonators.
Sex and Gender Differences on the Impact of Metabolism-Disrupting Chemicals on Obesity: A Systematic Review
Authors: D’Archivio Massimo , Coppola Lucia , Masella Roberta , Tammaro Alessia , La Rocca Cinzia
Published: January 2024

Obesity represents an important public health concern, being one of the leading causes of death worldwide. It is a multifactorial disease with many underlying intertwined causes, including genetic, environmental and behavioral factors. Notably, metabolism-disrupting chemicals (MDCs) can alter the set point control of metabolism, affecting the development and function of the adipose tissue. Epidemiological studies have reported associations between human exposure to MDCs and several altered metabolic endpoints. It is also noteworthy that sex and gender represent important risk factors in the development of obesity. Different sex-related biological and physiological characteristics influence individual susceptibility, whereas gender represents a critical component in determining the different exposure scenarios. Although some advancements in the treatment of obesity have been achieved in preclinical and clinical studies, the obesity pandemic continues to increase worldwide. The present study performed a systematic review of recent studies considering the effects of MDCs on obesity, with a specific focus on sex- and gender-related responses. This review highlighted that MDCs could differently affect men and women at different stages of life even though the number of studies evaluating the association between obesity and MDC exposure in relation to sex and gender is still limited. This evidence should urge researchers to carry out studies considering sex and gender differences. This is essential for developing sex-/gender-tailored prevention strategies to improve public health policies and reduce exposure.
Are the Dietary–Nutritional Recommendations Met? Analysis of Intake in Endurance Competitions
Authors: Jiménez-Alfageme Rubén , Álvarez Javier , Garbisu-Hualde Arkaitz , Romero-García David , Giménez-Monzó Daniel , Sospedra Isabel , Ausó Eva , Martínez-Sanz José Miguel
Published: January 2024

Background: In recent decades the number of endurance events has increased, as well as the number of athletes participating in them. Adequate nutritional and water planning is essential to maintain optimal sports performance and to reduce the incidence of gastrointestinal problems. The main objective of this study is to determine the dietary intake and compliance with nutritional recommendations of athletes in two endurance competitions, as well as to assess the incidence of gastrointestinal complaints. Methods: An observational and cross-sectional study was carried out on the consumption of liquids, food, and supplements in 42 triathletes and mountain runners (MRs) participating in a Vi-Half-Gasteiz triathlon and the Ultra Sierra de Cazorla trail run. At the completion of the trials, participants completed a validated questionnaire (NIQEC). Results: The mean caloric intake during the test of the participants in this study was 192.17 kcal/h, while the mean carbohydrate intake was 43.67 g/h, the mean sodium intake was 267.43 mg/h, and the mean caffeine intake was 15.53 mg/h, with no significant differences between the two sports. The amount of liquids consumed by the participants was 421.21 mL/h, with no significant differences between the triathletes and MRs. As for gastrointestinal problems, it was observed that the participants presented gastrointestinal discomfort in 61.9% of the cases. Conclusions: The intakes of energy, carbohydrates, water, sodium, and caffeine were lower than the current recommendations. There were no differences in the energy, carbohydrate, water, sodium, and caffeine intakes between the triathletes and mountain runners. Gastrointestinal problems showed a high prevalence in these athletes.
Preterm Birth and Maternal Mood States: What Is the Impact on Bonding?
Authors: Ionio Chiara , Ciuffo Giulia , Colombo Caterina , Melani Olivia , Figlino Maria Francesca , Landoni Marta , Castoldi Francesca , Cavigioli Francesco , Lista Gianluca
Published: January 2024

Preterm birth is a significant global health issue affecting millions of infants each year, with potential implications for their developmental outcomes. This study investigated the impact of preterm birth on maternal mood states during the early postpartum period and its subsequent effects on mother–infant bonding. Mothers of 90 preterm infants were involved in the assessment of maternal mood states, examined with the Profile of Mood States (POMS) questionnaire and the evaluation of mother–infant bonding, carried out through the Postpartum Bonding Questionnaire (PBQ). Contrary to expectations, there was no significant correlation between preterm birth characteristics and maternal mood states. On the other hand, significant correlations emerged between specific maternal mood states and the quality of mother–child bonding. More specifically, regression analyses showed that feelings of tension, anger, and confusion experienced by the mother tend to negatively affect the quality of her bond with her child. These findings emphasize the crucial role of maternal mental well-being in shaping the mother–infant relationship in the early postpartum period. The study highlights the importance of identifying and addressing maternal mood disorders to promote positive mother–infant bonding and child development, further underlining the need for comprehensive support and interventions for mothers of preterm infants.
Customizable Three-Dimensional Printed Earring Tap for Treating Affections Caused by Aesthetic Perforations
Authors: Pinho Ludmila A. G. , Lima Ana Luiza , Chen Yong , Sa-Barreto Livia L. , Marreto Ricardo N. , Gelfuso Guilherme M. , Gratieri Tais , Cunha-Filho Marcilio
Published: January 2024

This work aimed to develop a three-dimensional (3D) wearable drug-loaded earring tap to treat affections caused by aesthetic perforations. The initial phase involved a combination of polymers to prepare filaments for fused deposition modeling (FDM) 3D printing using a centroid mixture design. Optimized filament compositions were used in the second phase to produce 3D printed earring taps containing the anti-inflammatory naringenin. Next, samples were assessed via physicochemical assays followed by in vitro skin permeation studies with porcine ear skin. Two filament compositions were selected for the study’s second phase: one to accelerate drug release and another with slow drug dissolution. Both filaments demonstrated chemical compatibility and amorphous behavior. The use of the polymer blend to enhance printability has been confirmed by rheological analysis. The 3D devices facilitated naringenin skin penetration, improving drug recovery from the skin’s most superficial layer (3D device A) or inner layers (3D device B). Furthermore, the devices significantly decreased transdermal drug delivery compared to the control containing the free drug. Thus, the resulting systems are promising for producing 3D printed earring taps with topical drug delivery and reinforcing the feasibility of patient-centered drug administration through wearable devices.
The Late Palaeolithic and Mesolithic Settlement of Northern Italy: Problems and Perspectives
Authors: Biagi Paolo
Published: January 2024

This paper considers some problems of the Late Palaeolithic and the Mesolithic periods in Northern Italy. More precisely, it deals with chronology, settlement pattern, techno-typological characteristics of knapped stone assemblages, and climatic changes that have taken place in the region from the discovery of the first sites in the 1960s and the excavations that soon followed to the present state of research. The Italian Alps, the Piedmont, and the valleys that descend from the high massifs have yielded important traces of Late Palaeolithic (Final Epigravettian) and Mesolithic (Sauveterrian and Castelnovian) sites and findspots, some of which are rock shelters that were settled throughout several millennia. This paper describes and discusses the evidence available mainly from two regions of the western and eastern Alpine arc, which are characterised by very different landscapes and yielded a great variety of archaeological features.
Multi-Timescale Characteristics of Southwestern Australia Nearshore Surface Current and Its Response to ENSO Revealed by High-Frequency Radar
Authors: Gu Hongfei , Mao Yadan
Published: January 2024

The surface currents in coastal areas are closely related to the ecological environment and human activities, and are influenced by both local and remote factors of different timescales, resulting in complex genesis and multi-timescale characteristics. In this research, 9-year-long, hourly high-frequency radar (HFR) surface current observations are utilized together with satellite remote sensing reanalysis products and mooring data, and based on the Empirical Orthogonal Function (EOF) and correlation analysis, we revealed the multi-timescale characteristics of the surface currents in Fremantle Sea (32°S), Southwestern Australia, and explored the corresponding driving factors as well as the impact of El Niño-Southern Oscillation (ENSO) on the nearshore currents. Results show that the currents on the slope are dominated by the southward Leeuwin Current (LC), and the currents within the shelf are dominated by winds, which are subject to obvious diurnal and seasonal variations. The strong bathymetry variation there, from a wide shelf in the north to a narrow shelf in this study region, also plays an important role, resulting in the frequent occurrence of nearshore eddies. In addition, the near-zonal winds south of 30°S in winter contribute to the interannual variability of the Leeuwin Current at Fremantle, especially in 2011, when the onshore shelf circulation is particularly strong because of the climatic factors, together with the wind-driven offshore circulation, which results in significant and long-lasting eddies. The southward Leeuwin Current along Southwestern Australia shows a strong response to interannual climatic variability. During La Niña years, the equatorial thermal anomalies generate the westward anomalies in winds and equatorial currents, which in turn strengthen the Leeuwin Current and trigger the cross-shelf current as well as downwelling within the shelf at Fremantle, whereas during El Niño years, the climate anomalies and the response of coastal currents are opposite. This paper provides insights into the multi-timescale nature of coastal surface currents and the relative importance of different driving mechanisms. It also demonstrates the potential of HFR to reveal the response of nearshore currents to climate anomalies when combined with other multivariate data. Meanwhile, the methodology adopted in this research is applicable to other coastal regions with long-term available HFR observations.
Radiation-Variation Insensitive Coarse-to-Fine Image Registration for Infrared and Visible Remote Sensing Based on Zero-Shot Learning
Authors: Li Jiaqi , Bi Guoling , Wang Xiaozhen , Nie Ting , Huang Liang
Published: January 2024

Infrared and visible remote sensing image registration is significant for utilizing remote sensing images to obtain scene information. However, it is difficult to establish a large number of correct matches due to the difficulty in obtaining similarity metrics due to the presence of radiation variation between heterogeneous sensors, which is caused by different imaging principles. In addition, the existence of sparse textures in infrared images as well as in some scenes and the small number of relevant trainable datasets also hinder the development of this field. Therefore, we combined data-driven and knowledge-driven methods to propose a Radiation-variation Insensitive, Zero-shot learning-based Registration (RIZER). First, RIZER, as a whole, adopts a detector-free coarse-to-fine registration framework, and the data-driven methods use a Transformer based on zero-shot learning. Next, the knowledge-driven methods are embodied in the coarse-level matches, where we adopt the strategy of seeking reliability by introducing the HNSW algorithm and employing a priori knowledge of local geometric soft constraints. Then, we simulate the matching strategy of the human eye to transform the matching problem into a model-fitting problem and employ a multi-constrained incremental matching approach. Finally, after fine-level coordinate fine tuning, we propose an outlier culling algorithm that only requires very few iterations. Meanwhile, we propose a multi-scene infrared and visible remote sensing image registration dataset. After testing, RIZER achieved a correct matching rate of 99.55% with an RMSE of 1.36 and had an advantage in the number of correct matches, as well as a good generalization ability for other multimodal images, achieving the best results when compared to some traditional and state-of-the-art multimodal registration algorithms.
MT-GN: Multi-Task-Learning-Based Graph Residual Network for Tropical Cyclone Intensity Estimation
Authors: Zhao Zhitao , Zhang Zheng , Tang Ping , Wang Xiaofeng , Cui Linli
Published: January 2024

A tropical cyclone (TC) is a type of severe weather system that damages human property. Understanding TC mechanics is crucial for disaster management. In this study, we propose a multi-task learning framework named Multi-Task Graph Residual Network (MT-GN) to classify and estimate the intensity of TCs from FY-4A geostationary meteorological satellite images. And we construct a new benchmark dataset collected from the FY-4A satellite for both TC classification and intensity estimation tasks. Four different methodologies to classify TCs and estimate the intensity of TCs are fairly compared in our dataset. We discover that accurate classification and estimation of TCs, which are usually achieved separately, requires co-related knowledge from each process. Thus, we train a convolution feature extractor in a multi-task way. Furthermore, we build a task-dependency embedding module using a Graph Convolution Network (GCN) that further drives our model to reach better performance. Finally, to overcome the influence of the unbalanced distribution of TC category samples, we introduce class-balanced loss to our model. Experimental results on the dataset show that the classification and estimation performance are improved. With an overall root mean square error (RMSE) of 9.50 knots and F1-score of 0.64, our MT-GN model achieves satisfactory performance. The results demonstrate the potential of applying multi-task learning for the study of TCs.
Cloud Overlap Features from Multi-Year Cloud Radar Observations at the SACOL Site and Comparison with Satellites
Authors: Yang Xuan , Li Qinghao , Ge Jinming , Wang Bo , Peng Nan , Su Jing , Zhang Chi , Du Jiajing
Published: January 2024

Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.
Self-Supervised Deep Multi-Level Representation Learning Fusion-Based Maximum Entropy Subspace Clustering for Hyperspectral Band Selection
Authors: Wang Yulei , Ma Haipeng , Yang Yuchao , Zhao Enyu , Song Meiping , Yu Chunyan
Published: January 2024

As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of attention with good effect. However, many of them lack the self-supervision of representations and ignore the multi-level spectral–spatial information of HSI and the connectivity of subspaces. To this end, this paper proposes a novel self-supervised multi-level representation learning fusion-based maximum entropy subspace clustering (MLRLFMESC) method for hyperspectral band selection. Firstly, to learn multi-level spectral–spatial information, self-representation subspace clustering is embedded between the encoder layers of the deep-stacked convolutional autoencoder and its corresponding decoder layers, respectively, as multiple fully connected layers to achieve multi-level representation learning (MLRL). A new auxiliary task is constructed for multi-level representation learning and multi-level self-supervised training to improve its capability of representation. Then, a fusion model is designed to fuse the multi-level spectral–spatial information to obtain a more distinctive coefficient matrix for self-expression, where the maximum entropy regularization (MER) method is employed to promote connectivity and the uniform dense distribution of band elements in each subspace. Finally, subspace clustering is conducted to obtain the final band subset. Experiments have been conducted on three hyperspectral datasets, and the corresponding results show that the proposed MLRLFMESC algorithm significantly outperforms several other band selection methods in classification performance.
The Aerial Guide Dog: A Low-Cognitive-Load Indoor Electronic Travel Aid for Visually Impaired Individuals
Authors: Zhang Xiaochen , Pan Ziyi , Song Ziyang , Zhang Yang , Li Wujing , Ding Shiyao
Published: January 2024

Most navigation aids for visually impaired individuals require users to pay close attention and actively understand the instructions or feedback of guidance, which impose considerable cognitive loads in long-term usage. To tackle the issue, this study proposes a cognitive burden-free electronic travel aid for individuals with visual impairments. Utilizing human instinctive compliance in response to external force, we introduce the “Aerial Guide Dog”, a helium balloon aerostat drone designed for indoor guidance, which leverages gentle tugs in real time for directional guidance, ensuring a seamless and intuitive guiding experience. The introduced Aerial Guide Dog has been evaluated in terms of directional guidance and path following in the pilot study, focusing on assessing its accuracy in orientation and the overall performance in navigation. Preliminary results show that the Aerial Guide Dog, utilizing Ultra-Wideband (UWB) spatial positioning and Measurement Unit (IMU) angle sensors, consistently maintained minimal deviation from the targeting direction and designated path, while imposing negligible cognitive burdens on users while completing the guidance tasks.
Monitoring of Composite Structures for Re-Usable Space Applications Using FBGs: The Influence of Low Earth Orbit Conditions
Authors: Juwet Thibault , Luyckx Geert , Lamberti Alfredo , Creemers Frank , Voet Eli , Missinne Jeroen
Published: January 2024

Fiber Bragg grating sensors (FBGs) are promising for structural health monitoring (SHM) of composite structures in space owing to their lightweight nature, resilience to harsh environments, and immunity to electromagnetic interference. In this paper, we investigated the influence of low Earth orbit (LEO) conditions on the integrity of composite structures with embedded optical fiber sensors, specifically FBGs. The LEO conditions were simulated by subjecting carbon fiber-reinforced polymer (CFRP) coupons to 10 cycles of thermal conditioning in a vacuum (TVac). Coupons with embedded optical fibers (OFs) or capillaries were compared with reference coupons without embedded OFs or capillaries. Embedded capillaries were necessary to create in situ temperature sensors. Tensile and compression tests were performed on these coupons, and the interlaminar shear strength was determined to assess the influence of TVac conditioning on the integrity of the composite. Additionally, a visual inspection of the cross-sections was conducted. The impact on the proper functioning of the embedded FBGs was tested by comparing the reflection spectra before and after TVac conditioning and by performing tensile tests in which the strain measured using the embedded FBGs was compared with the output of reference strain sensors applied after TVac conditioning. The measured strain of the embedded FBGs showed excellent agreement with the reference sensors, and the reflection spectra did not exhibit any significant degradation. The results of the mechanical testing and visual inspection revealed no degradation of the structural integrity when comparing TVac-conditioned coupons with non-TVac-conditioned coupons of the same type. Consequently, it was concluded that TVac conditioning does not influence the functionality of the embedded FBGs or the structural integrity of the composite itself. Although in this paper FBG sensors were tested, the results can be extrapolated to other sensing techniques based on optical fibers.
Determination of N-Acetyl-L-cysteine Ethyl Ester (NACET) by Sequential Injection Analysis
Authors: Kukoc-Modun Lea , Kraljevic Tomislav , Tsikas Dimitrios , Spassov Tony G. , Kolev Spas D.
Published: January 2024

New sequential injection analysis (SIA) methods with optical sensing for the determination of N-acetyl-L-cysteine ethyl ester (NACET) have been developed and optimized. NACET is a potential drug and antioxidant with advantageous pharmacokinetics. The methods involve the reduction of Cu(II) in its complexes with neocuproine (NCN), bicinchoninic acid (BCA), and bathocuproine disulfonic acid (BCS) to the corresponding chromophoric Cu(I) complexes by the analyte. The absorbance of the Cu(I) complexes with NCN, BCA, and BCS was measured at their maximum absorbance wavelengths of 458, 562, and 483 nm, respectively. The sensing manifold parameters and experimental conditions were optimized for each of the Cu(II) complexes used. Under optimal conditions, the corresponding linear calibration ranges, limits of detection, and sampling rates were 8.0 × 10−6–2.0 × 10−4 mol L−1, 5.5 × 10−6 mol L−1, and 60 h−1 for NCN; 6.0 × 10−6–1.0 × 10−4 mol L−1, 5.2 × 10−6 mol L−1, and 60 h−1 for BCA; and 4.0 × 10−6–1.0 × 10−4 mol L−1, 2.6 × 10−6 mol L−1, and 78 h−1 for BCS. The Cu(II)-BCS complex was found to be best performing in terms of sensitivity and sampling rate. Usual excipients in pharmaceutical preparations did not interfere with NACET analysis.
Assessment of ADHD Subtypes Using Motion Tracking Recognition Based on Stroop Color–Word Tests
Authors: Li Chao , Delgado-Gómez David , Sujar Aaron , Wang Ping , Martin-Moratinos Marina , Bella-Fernández Marcos , Masó-Besga Antonio Eduardo , Peñuelas-Calvo Inmaculada , Ardoy-Cuadros Juan , Hernández-Liebo Paula , Blasco-Fontecilla Hilario
Published: January 2024

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder known for its significant heterogeneity and varied symptom presentation. Describing the different subtypes as predominantly inattentive (ADHD–I), combined (ADHD–C), and hyperactive–impulsive (ADHD–H) relies primarily on clinical observations, which can be subjective. To address the need for more objective diagnostic methods, this pilot study implemented a Microsoft Kinect-based Stroop Color–Word Test (KSWCT) with the objective of investigating the potential differences in executive function and motor control between different subtypes in a group of children and adolescents with ADHD. A series of linear mixture modeling were used to encompass the performance accuracy, reaction times, and extraneous movements during the tests. Our findings suggested that age plays a critical role, and older subjects showed improvements in KSWCT performance; however, no significant divergence in activity level between the subtypes (ADHD–I and ADHD–H/C) was established. Patients with ADHD–H/C showed tendencies toward deficits in motor planning and executive control, exhibited by shorter reaction times for incorrect responses and more difficulty suppressing erroneous responses. This study provides preliminary evidence of unique executive characteristics among ADHD subtypes, advances our understanding of the heterogeneity of the disorder, and lays the foundation for the development of refined and objective diagnostic tools for ADHD.
Removal of Mixed Noise in Hyperspectral Images Based on Subspace Representation and Nonlocal Low-Rank Tensor Decomposition
Authors: He Chun , Wei Youhua , Guo Ke , Han Hongwei
Published: January 2024

Hyperspectral images (HSIs) contain abundant spectral and spatial structural information, but they are inevitably contaminated by a variety of noises during data reception and transmission, leading to image quality degradation and subsequent application hindrance. Hence, removing mixed noise from hyperspectral images is an important step in improving the performance of subsequent image processing. It is a well-established fact that the data information of hyperspectral images can be effectively represented by a global spectral low-rank subspace due to the high redundancy and correlation (RAC) in the spatial and spectral domains. Taking advantage of this property, a new algorithm based on subspace representation and nonlocal low-rank tensor decomposition is proposed to filter the mixed noise of hyperspectral images. The algorithm first obtains the subspace representation of the hyperspectral image by utilizing the spectral low-rank property and obtains the orthogonal basis and representation coefficient image (RCI). Then, the representation coefficient image is grouped and denoised using tensor decomposition and wavelet decomposition, respectively, according to the spatial nonlocal self-similarity. Afterward, the orthogonal basis and denoised representation coefficient image are optimized using the alternating direction method of multipliers (ADMM). Finally, iterative regularization is used to update the image to obtain the final denoised hyperspectral image. Experiments on both simulated and real datasets demonstrate that the algorithm proposed in this paper is superior to related mainstream methods in both quantitative metrics and intuitive vision. Because it is denoising for image subspace, the time complexity is greatly reduced and is lower than related denoising algorithms in terms of computational cost.
A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks
Authors: Sadowski Jaroslaw , Stefanski Jacek
Published: January 2024

This article proposes the use of a feedforward neural network (FNN) to select the starting point for the first iteration in well-known iterative location estimation algorithms, with the research objective of finding the minimum size of a neural network that allows iterative position estimation algorithms to converge in an example positioning network. The selected algorithms for iterative position estimation, the structure of the neural network and how the FNN is used in 2D and 3D position estimation process are presented. The most important results of the work are the parameters of various FNN network structures that resulted in a 100% probability of convergence of iterative position estimation algorithms in the exemplary TDoA positioning network, as well as the average and maximum number of iterations, which can give a general idea about the effectiveness of using neural networks to support the position estimation process. In all simulated scenarios, simple networks with a single hidden layer containing a dozen non-linear neurons turned out to be sufficient to solve the convergence problem.
Spectrum Situation Awareness for Space–Air–Ground Integrated Networks Based on Tensor Computing
Authors: Qi Bin , Zhang Wensheng , Zhang Lei
Published: January 2024

The spectrum situation awareness problem in space–air–ground integrated networks (SAGINs) is studied from a tensor-computing perspective. Tensor and tensor computing, including tensor decomposition, tensor completion and tensor eigenvalues, can satisfy the application requirements of SAGINs. Tensors can effectively handle multidimensional heterogeneous big data generated by SAGINs. Tensor computing is used to process the big data, with tensor decomposition being used for dimensionality reduction to reduce storage space, and tensor completion utilized for numeric supplementation to overcome the missing data problem. Notably, tensor eigenvalues are used to indicate the intrinsic correlations within the big data. A tensor data model is designed for space–air–ground integrated networks from multiple dimensions. Based on the multidimensional tensor data model, a novel tensor-computing-based spectrum situation awareness scheme is proposed. Two tensor eigenvalue calculation algorithms are studied to generate tensor eigenvalues. The distribution characteristics of tensor eigenvalues are used to design spectrum sensing schemes with hypothesis tests. The main advantage of this algorithm based on tensor eigenvalue distributions is that the statistics of spectrum situation awareness can be completely characterized by tensor eigenvalues. The feasibility of spectrum situation awareness based on tensor eigenvalues is evaluated by simulation results. The new application paradigm of tensor eigenvalue provides a novel direction for practical applications of tensor theory.
Analysis of Agricultural Carbon Emissions and Carbon Sinks in the Yellow River Basin Based on LMDI and Tapio Decoupling Models
Authors: Jia Luhao , Wang Mingya , Yang Shili , Zhang Fan , Wang Yidong , Li Penghao , Ma Wanqi , Sui Shaobo , Liu Tong , Wang Mingshi
Published: January 2024

In addition to creating economic value, crops also serve important ecological functions. Especially their carbon sink function, which plays a key role in mitigating climate change. In this study, the LMDI and the Tapio model were innovatively combined to quantitatively evaluate the carbon emissions and sinks in the Yellow River basin (YRB). It shows that the average annual growth rate of the YRB was −0.1344% during 2002–2020. Carbon emissions show a negative trend due to the transformation and upgrading of agriculture from traditional to modern and the implementation of policies related to China’s agricultural benefits. Agricultural production efficiency is a major factor in inhibiting agricultural carbon emissions, reducing carbon emissions by an average of approximately 8.07 million tons per year. High-carbon emission and high-sink areas in agriculture are mainly concentrated in the southeast of the YRB, where livestock and poultry farming is the principal source of carbon emissions, with rice, wheat, and corn being the principal contributors to the carbon sink. Moreover, there are significant differences in the carbon sink capacity of crops in the YRB. Optimizing crop selection and area distribution can enhance the carbon sink capacity in different regions, contributing to more effective carbon emission control. This study combines agricultural carbon emissions with the carbon sequestration capacity of crops, providing data support and a theoretical basis for the policy formulation and planning of low-carbon agriculture in China. It is of great significance for promoting sustainable agricultural development and mitigating climate change.
Environmental Life Cycle Assessment of Silage Maize in Relation to Regenerative Agriculture
Authors: Dědina Martin , Jevič Petr , Čermák Pavel , Moudrý Jan , Mukosha Chisenga Emmanuel , Lošák Tomáš , Hrušovský Tadeáš , Watzlová Elizaveta
Published: January 2024

The demand for agricultural products is growing and is resulting in significant environmental impacts due to the overuse of fertilizers (and pesticides in some cases). There is a continued need to find sustainable methods in agricultural systems without harming the environment. Regenerative agriculture can be considered as one of the best methods of sustainable agriculture. The aim of this comparative life cycle assessment (LCA) study was to quantify the environmental impacts associated with the production of silage maize at different doses of fertilizers and pesticides under conventional agriculture and without the use of fertilizers and pesticides under regenerative agriculture. The input data were obtained from the experimental fields and supplemented by background process databases of Ecoinvent, World Food Live Cycle Assessment Database (WFLCD), and the French database AGRIBALYSE. The results of the study were related to six midpoint impact categories: global warming, marine eutrophication, freshwater eutrophication, freshwater ecotoxicity, marine ecotoxicity, and terrestrial ecotoxicity. Although the variant of growing silage maize without the use of fertilizers and pesticides according to the principle of regenerative agriculture showed the lowest burden on the environment, the yields of the cultivated silage maize were 43–55% lower than those of the fertilized variants.
Digital Twin of Microgrid for Predictive Power Control to Buildings
Authors: Jiang Hao , Tjandra Rudy , Soh Chew Beng , Cao Shuyu , Soh Donny Cheng Lock , Tan Kuan Tak , Tseng King Jet , Krishnan Sivaneasan Bala
Published: January 2024

The increased focus on sustainability in response to climate change has given rise to many new initiatives to meet the rise in building load demand. The concept of distributed energy resources (DER) and optimal control of supply to meet power demands in buildings have resulted in growing interest to adopt microgrids for a precinct or a university campus. In this paper, a model for an actual physical microgrid has been constructed in OPAL-RT for real-time simulation studies. The load demands for SIT@NYP campus and its weather data are collected to serve as input to run on the digital twin model of DERs of the microgrid. The dynamic response of the microgrid model in response to fluctuations in power generation due to intermittent solar PV generation and load demands are examined via real-time simulation studies and compared with the response of the physical assets. It is observed that the simulation results match closely to the performance of the actual physical asset. As such, the developed microgrid model offers plug-and-play capability, which will allow power providers to better plan for on-site deployment of renewable energy sources and energy storage to match the expected building energy demand.
Let’s Do It for Real: Making the Ecosystem Service Concept Operational in Regional Planning for Climate Change Adaptation
Authors: Longo Alessandra , Zardo Linda , Maragno Denis , Musco Francesco , Burkhard Benjamin
Published: January 2024

The application of ecosystem service (ES) knowledge to planning processes and decision-making can lead to more effective climate change adaptation. Despite the increased attention given to the ES concept, its degree of integration and use in spatial planning processes are still below the expectations of those who are promoting this concept. Barriers hindering its operationalisation cover a span of aspects ranging from theoretical to procedural and methodological issues. Overall, there is a general lack of guidance on how and at what point ES knowledge should be integrated into planning processes. This study aims to promote the inclusion of ES knowledge into spatial planning practices and decision-making processes to enhance climate change adaptation. A replicable GIS-based methodology is proposed. First, the potential supply of ESs that can support climate change adaptation (ESCCAs) is defined, mapped, and quantified. Then, a need for an ESCCA supply is identified, and territorial capacities to respond to the expected climate change impacts on natural and socio-economic sectors are assessed. The methodology is applied to the Friuli Venezia Giulia Autonomous Region (Italy) as an illustrative case study. The results reveal that areas with similar geomorphological characteristics tend to respond similarly. Forest ecosystems, inland wetlands and specifically salt marshes can potentially supply a greater variety of ESCCAs. In the case study area, about 62% of the supplied ESCCAs can contribute to reducing the impacts in more than 50% of the impacted sectors. The territory of the study site generally shows good preparedness for expected impacts in most of the analysed sectors; less prepared areas are characterised by agricultural ecosystems. This reading approach based on land cover analyses can thus assist in developing policies to enhance different territorial capacities, ultimately leading to better and more sustainable decision-making.
Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China
Authors: Pan Lizhi , Yang Chaobin , Han Jing , Yan Fengqin , Ju Anhua , Kui Tong
Published: January 2024

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R2BTH = 0.58, R2SP = 0.66, and R2CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.
Evaluating Cleaning Services in Civil Environments: Microbiological and Life Cycle Analysis Comparing Conventional and Sustainable Methods
Authors: Fontana Riccardo , Buratto Mattia , Caproni Anna , Nordi Chiara , Pappadà Mariangela , Bandera Beatrice , Vogli Luciano , Buffone Cesare , Marconi Peggy
Published: January 2024

In response to the growing global concern for environmental sustainability, a Life Cycle Assessment (LCA) study was conducted to evaluate the environmental benefits of the “Formula Servizi” GREEN protocol compared to a conventional cleaning protocol, as mandated by the updated Criteria for Environmental Sustainability (CAM—Criteri Ambientali Minimi) for cleaning services. The CAM, effective on 19 June 2021, requires companies to demonstrate the environmental advantages of their cleaning protocols over traditional methods. This study aligns with the new CAM guidelines and employs UNI EN ISO 14040–14044 technical standards for a comprehensive comparative analysis. The study highlights the significance of maintaining hygiene to ensure safety in various contexts, emphasizing the importance of environmental sampling and monitoring to prevent contamination and infection transmission. Despite the complexity and expenses associated with microbiological monitoring, this research affirms its crucial role in validating cleaning procedures, particularly in healthcare facilities, food service areas, and industrial settings. The findings reveal that both the “Traditional” and “GREEN” cleaning protocols demonstrate satisfactory effectiveness in controlling microbiological contamination according to established guidelines. Moreover, the LCA results indicate that the “GREEN” protocol, while exhibiting higher water consumption and wastewater treatment, showcases a strategic use of more sustainable cleaning and laundry detergents. Despite the increased water usage in certain phases, the significantly lower environmental impact per unit of weight demonstrates the potential for optimizing both environmental sustainability and operational efficiency in future Life Cycle Sustainability Assessment (LCSA) endeavors. The comparative LCA further reveals that the “GREEN” protocol enables an annual avoidance of 260 g of CO2-e emissions per square meter of cleaned surface. The most significant reduction in absolute terms is associated with the use of eco-labeled detergents in the laundry system, resulting in the avoidance of 654.1 kg of CO2-e per year of service (−77% compared to traditional laundry detergents).
Measuring Green Practices in Sport: Development and Validation of a Scale
Authors: Morán-Gámez Guillermo , Fernández-Martínez Antonio , Biscaia Rui , Nuviala Román
Published: January 2024

Environmental sustainability has often been associated with increased organizational profitability, efficiency, and competitiveness. A related example is green practices which have become a central component of many companies’ marketing strategies and day-to-day management. The current study aims to develop and validate a scale for use in sport organizations and activities. An item bank derived from past literature was prepared, which was then assessed by a group of experts and pilot-tested. Two waves of data collection were then collected (N = 1165 and N = 567). An exploratory factor analysis was performed with the data from wave 1, and a one-dimensional solution was presented. Then, a confirmatory factor analysis followed by a multigroup analysis was performed with the data from wave 2. The results confirmed the six-item one-dimensional scale with high levels of reliability and validity and optimal fit indices. The scale is important to green practices adopted by sport organizations regarding the customer–organization relationship.