bluuebunny/crossref_metadata_2025 huggingface dataset:
The Optimization of Microwave Field Characteristics for ODMR Measurement of Nitrogen-Vacancy Centers in Diamond
Authors: Fan Zhenxian , Xing Li , Wu Feixiang , Feng Xiaojuan , Zhang Jintao
Published: May 2024

A typical solid-state quantum sensor can be developed based on negatively charged nitrogen-vacancy (NV−) centers in diamond. The electron spin state of NV− can be controlled and read at room temperature. Through optical detection magnetic resonance (ODMR) technology, temperature measurement can be achieved at the nanoscale. The key to ODMR technology is to apply microwave resonance to manipulate the electron spin state of the NV−. Therefore, the microwave field characteristics formed near the NV− have a crucial impact on the sensitivity of ODMR measurement. This article mainly focuses on the temperature situation in cellular applications and simulates the influence of structural parameters of double open loop resonant (DOLR) microwave antennas and broadband large-area (BLA) microwave antennas on the microwave field’s resonance frequency, quality factor Q, magnetic field strength, uniformity, etc. The parameters are optimized to have sufficient bandwidth, high signal-to-noise ratio, low power loss, and high magnetic field strength in the temperature range of 36 °C to 42.5 °C. Finally, the ODMR spectra are used for effect comparison, and the signal-to-noise ratio and Q values of the ODMR spectra are compared when using different antennas. We have provided an optimization method for the design of microwave antennas and it is concluded that the DOLR microwave antenna is more suitable for living cell temperature measurement in the future.
Conversion and Active Control between BIC and Absorber in Terahertz Metasurface
Authors: Xi Zhou , Chen Zhencheng
Published: May 2024

A multifunctional switchable metamaterial device based on graphene, a gold layer, polyimide, vanadiµm dioxide (VO2), and the sapphire substrate is designed in this paper. The top layer consists of a gold wire, graphene, and two split-ring resonators with the same parameters. By adjusting the Fermi level of graphene, the regulation of BIC and quasi-BIC is realized, and the conversion between BIC and absorber is realized by adjusting the conductivity of VO2. When the device is converted into a wave-absorbing device with single-band absorption characteristics, the Fermi level of graphene at this time is 0.001 eV, the absorption peak at 0.820 THz is higher than 99.5%, and when the Fermi level of regulated graphene is 1 eV, the absorption peak at 0.667 THz is also higher than 99.5%. The peak frequency of the device is 0.640 THz when it converts to quasi-BIC. To the best of our knowledge, this is the first time that the conversion and regulation of BIC and absorber have been achieved using these two phase change materials. Moreover, by adjusting the parameters of the metamaterial structure, the working efficiency and frequency of BIC and absorber can be dynamically adjusted. The electric field distribution and surface current of metamaterials are further studied, and the physical mechanism of effective absorption and BIC is discussed. These results show that the metamaterials proposed in this paper have many advantages, such as terahertz absorption, BIC, and active device control, and are of great significance for developing terahertz multifunctional devices.
Alterations in the Anatomy and Ultrastructure of Leaf Blade in Norway Maple (Acer platanoides L.) Growing on Mining Sludge: Prospects of Using This Tree Species for Phytoremediation
Authors: Krzesłowska Magdalena , Mleczek Mirosław , Luboński Aleksander , Weręża Karolina , Woźny Adam , Goliński Piotr , Samardakiewicz Sławomir
Published: May 2024

Alterations in leaf architecture can be used as an indicator of the substrate toxicity level as well as the potential of a given plant species in the phytoremediation of polluted areas, e.g., mining sludge. In this work, we demonstrated, for the first time, the nature and scale of alterations in leaf architecture at the tissue and cellular levels occurring in Norway maple growing on mining sludge originating from a copper mine in Lubin (Poland). The substrate differs from other mine wastes, e.g., calamine or serpentine soils, due to an extremely high level of arsenic (As). Alterations in leaf anatomy predominantly included the following: (1) a significant increase in upper epidermis thickness; (2) a significant decrease in palisade parenchyma width; (3) more compact leaf tissue organization; (4) the occurrence of two to three cell layers in palisade parenchyma in contrast to one in the control; (5) a significantly smaller size of cells building palisade parenchyma. At the cellular level, the alterations included mainly the occurrence of local cell wall thickenings—predominantly in the upper and lower epidermis—and the symptoms of accelerated leaf senescence. Nevertheless, many chloroplasts showed almost intact chloroplast ultrastructure. Modifications in leaf anatomy could be a symptom of alterations in morphogenesis but may also be related to plant adaptation to water deficit stress. The occurrence of local cell wall thickenings can be considered as a symptom of a defence strategy involved in the enlargement of apoplast volume for toxic elements (TE) sequestration and the alleviation of oxidative stress. Importantly, the ultrastructure of leaf cells was not markedly disturbed. The results suggested that Norway maple may have good phytoremediation potential. However, the general shape of the plant, the significantly smaller size of leaves, and accelerated senescence indicated the high toxicity of the mining sludge used in this experiment. Hence, the phytoremediation of such a substrate, specifically including use of Norway maple, should be preceded by some amendments—which are highly recommended.
Synthesis of Thermoresponsive Chitosan-graft-Poly(N-isopropylacrylamide) Hybrid Copolymer and Its Complexation with DNA
Authors: Zaharia Marius-Mihai , Bucatariu Florin , Karayianni Maria , Lotos Elena-Daniela , Mihai Marcela , Pispas Stergios
Published: May 2024

A hybrid synthetic-natural, thermoresponsive graft copolymer composed of poly(N-isopropyl acrylamide) (PNIPAM) side chains, prepared via RAFT polymerization, and a chitosan (Chit) polysaccharide backbone, was synthesized via radical addition-fragmentation reactions using the “grafting to” technique, in aqueous solution. ATR-FTIR, TGA, polyelectrolyte titrations and 1H NMR spectroscopy were employed in order to validate the Chit-g-PNIPAM copolymer chemical structure. Additionally, 1H NMR spectra and back conductometric titration were utilized to quantify the content of grafted PNIPAM side chains. The resulting graft copolymer contains dual functionality, namely both pH responsive free amino groups, with electrostatic complexation/coordination properties, and thermoresponsive PNIPAM side chains. Particle size measurements via dynamic light scattering (DLS) were used to study the thermoresponsive behavior of the Chit-g-PNIPAM copolymer. Thermal properties examined by TGA showed that, by the grafting modification with PNIPAM, the Chit structure became more thermally stable. The lower critical solution temperature (LCST) of the copolymer solution was determined by DLS measurements at 25–45 °C. Furthermore, dynamic and electrophoretic light scattering measurements demonstrated that the Chit-g-PNIPAM thermoresponsive copolymer is suitable of binding DNA molecules and forms nanosized polyplexes at different amino to phosphate groups ratios, with potential application as gene delivery systems.
An Injectable, Self-Healing, Adhesive Multifunctional Hydrogel Promotes Bacteria-Infected Wound Healing
Authors: Zhang Ling , Wang Yan , Yang Mingrui , Yu Wen , Zhao Zheng , Liu Yichao
Published: May 2024

Bacterial infections have a serious impact on public health. It is urgent to develop antibacterial hydrogels with good biocompatibility to reduce the use of antibiotics. In this study, poly(lipoic acid-co-sodium lipoate)–phytic acid (P(LA-SL)-PA) hydrogels are prepared by a simple mixture of the natural small molecules lipoic acid (LA) and phytic acid (PA) in a mild and green reaction environment. The crosslinking network is constructed through the connection of covalent disulfide bonds as well as the hydrogen bonds, which endow the injectable and self-healing properties. The P(LA-SL)-PA hydrogels exhibit an adjustable compression modulus and adhesion. The in vitro agar plates assay indicates that the antibacterial rate of hydrogels against Escherichia coli and Staphylococcus aureus is close to 95%. In the rat-infected wound model, the P(LA-SL)-PA hydrogels adhere closely to the tissue and promote epithelialization and collagen deposition with a significant effect on wound healing. These results prove that the P(LA-SL)-PA hydrogels could act as effective wound dressings for promoting the healing of infected wounds.
Modification of the Dielectric and Thermal Properties of Organic Frameworks Based on Nonterminal Epoxy Liquid Crystal with Silicon Dioxide and Titanium Dioxide
Authors: Okrasa Lidia , Włodarska Magdalena , Kisiel Maciej , Mossety-Leszczak Beata
Published: May 2024

A nonterminal liquid crystal epoxy monomer is used to create an epoxy–amine network with a typical diamine 4,4′diaminodiphenylmethane. The plain matrix is compared to matrices modified with inorganic fillers: TiO2 or SiO2. Conditions of the curing reaction and glass transition temperatures in the cured products are determined through differential scanning calorimetry and broadband dielectric spectroscopy. The curing process is also followed through optical and electrical observations. The dielectric response of all investigated networks reveals a segmental α-process related to structural reorientation (connected to the glass transition). In all products, a similar process associated with molecular motions of polar groups also appears. The matrix modified with TiO2 exhibits two secondary relaxation processes (β and γ). Similar processes were observed in the pure monomer. An advantage of the network with the TiO2 filler is a shorter time or lower temperature required for optimal curing conditions. The physical properties of cured matrices depend on the presence of a nematic phase in the monomer and nonterminal functional groups in the aliphatic chains. In effect, such cured matrices can have more flexibility and internal order than classical resins. Additional modifiers used in this work shift the glass transition above room temperature and influence the fragility index in both cases.
Preparation of Single-Helical Curdlan Hydrogel and Its Activation with Coagulation Factor G
Authors: Ru Geying , Yan Xiaoshuang , Wang Huijuan , Feng Jiwen
Published: May 2024

β-1,3-glucans are a kind of natural polysaccharide with immunomodulatory, antitumor, and anti-inflammatory properties. Curdlan, as the simplest linear β-1,3-glucan, possesses a variety of biological activities and thermogelation properties. However, due to the complexity and variability of the conformations of curdlan, the exact structure–activity relationship remains unclear. We prepare a chemically crosslinked curdlan hydrogel with the unique single-helical skeleton (named S gel) in 0.4 wt% NaOH at 40 °C, confirmed by diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). X-ray diffractometry (XRD) data show that S gel maintains the single-helical crystal structure, and the degree of crystallinity of the S gel is ~24%, which is slightly lower than that of the raw powder (~31%). Scanning electron microscopy (SEM) reveals that S gel has a continuous network structure, with large pores measuring 50–200 μm, which is consistent with its high swelling property. Using the 13C high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) method, we determine that most of the single-helical skeleton carbon signals in the swollen S gel are visible, suggesting that the single-helical skeleton of S gel exhibits fascinating mobility at room temperature. Finally, we reveal that the binding of S gel to coagulation Factor G from tachypleus amebocyte lysate increases and saturates at 20 μL tachypleus amebocyte lysate per mg of S gel. Our prepared S gel can avoid the transformation of curdlan conformations and retain the bioactivity of binding to coagulation Factor G, making it a valuable material for use in the food industry and the pharmaceutical field. This work deepens the understanding of the relationship between the single-helical structure and the activity of curdlan, promoting the development and application of β-1,3-glucans.
Preparation of a Molecularly Imprinted Polymer on Polyethylene Terephthalate Platform Using Reversible Addition-Fragmentation Chain Transfer Polymerization for Tartrazine Analysis via Smartphone
Authors: Hernández Christian Jacinto , Medina Raúl , Maza Mejía Ily , Hurtado Mario , Khan Sabir , Picasso Gino , López Rosario , Sotomayor María D. P. T.
Published: May 2024

This work describes the preparation of a molecularly imprinted polymer (MIP) platform on polyethylene terephthalate (MIP-PET) via RAFT polymerization for analyzing tartrazine using a smartphone. The MIP-PET platform was characterized using Fourier transform infrared (FTIR) techniques, Raman Spectroscopy, X-ray photoelectron spectroscopy (XPS), and confocal microscopy. The optimal pH and adsorption time conditions were determined. The adsorption capacity of the MIP-PET plates with RAFT treatment (0.057 mg cm−2) was higher than that of the untreated plates (0.028 mg cm−2). The kinetic study revealed a pseudo-first-order model with intraparticle diffusion, while the isotherm study indicated a fit for the Freundlich model. Additionally, the MIP-PET demonstrated durability by maintaining its adsorption capacity over five cycles of reuse without significant loss. To quantify tartrazine, images were captured using a smartphone, and the RGB values were obtained using the ImageJ® free program. A partial least squares regression (PLS) was performed, obtaining a linear range of 0 to 7 mg L−1 of tartrazine. The accuracy of the method was 99.4% (4.97 ± 0.74 mg L−1) for 10 samples of 5 mg L−1. The concentration of tartrazine was determined in two local soft drinks (14.1 mg L−1 and 16.5 mg L−1), with results comparable to the UV–visible spectrophotometric method.
Exploring the Therapeutic Potential of Virtual Reality: A Review on the Simulation of Psychedelic Effects for Treating Psychological Disorders
Authors: Casu Mirko , Farrauto Chiara , Farruggio Giorgia , Bellissima Salvatore , Battiato Sebastiano , Caponnetto Pasquale
Published: May 2024

Psychedelic therapy is increasingly acknowledged as a transformative approach to mental health care, much like how Virtual Reality (VR) technology has emerged as a potent tool in the realm of mental health. Hence, there is potential for integrating the benefits derived from both. This review aims to assess the current state of the art concerning the utilization of VR and psychedelic simulations for treating psychological disorders. The findings clarify the potential of an emerging treatment: the simulation of psychedelic states through Virtual Reality. This treatment has been shown to improve cognitive flexibility and executive functions and, as a result, could be used to prevent conditions such as mild cognitive impairment and dementia. Furthermore, this treatment facilitates the activation of other constructs in the subject, such as creativity, joy, pleasure, and relaxation, which can act as mediators in the treatment of various psychopathological disorders. This review attempts to broaden knowledge regarding the simulation of psychedelic states through Virtual Reality, exposing the results in a clinical setting and highlighting the need for further studies.
Fostering Open Data Practices in Research-Performing Organizations
Authors: Jean-Quartier Claire , Kleinberger-Pierer Harald , Zach Barbara , Hasani-Mavriqi Ilire , Pešec Lea , Schmikl-Reiter Clara
Published: May 2024

Open data provide the scientific community and other stakeholders with unrestricted access to data. Open data serve as a foundation for reproducing research findings, while also facilitating collaboration and enabling novel discoveries. However, open data practices are still not commonly applied. To contribute to the implementation of open data strategy in academia in Austria and beyond, a collection of local strategies from regional universities and higher education institutions in the Austrian provinces of Styria and Carinthia was compiled through workshop-based discussions between participants from research support service units at research-performing organizations. The collection was further organized into categories based on application time scenarios, target groups, and involved parties, as well as corresponding thematic focus. A strategic guide consisting of various measures has been developed to encourage the adoption of open data practices from an organizational standpoint. Designed for adaptability, it aims to be applicable and modifiable by all interested research and higher education institutions, regardless of their priorities and resources. Our guideline aids research organizations in crafting a tailored strategy to enhance their data dissemination practices, thereby increasing their research visibility and impact.
Preliminary Derived DInSAR Coseismic Displacements of the 2022 Mw 5.7 Stolac Earthquake
Authors: Banko Antonio , Mihelin Fran , Banković Tedi , Pavasović Marko
Published: May 2024

On 22 April 2022, a Mw 5.7 earthquake was generated near Stolac (Bosnia and Herzegovina). The mainshock was succeeded by several aftershocks, three of which were significant. Two Mb 4.3 earthquakes occurred on 23 April 2022, and a Mw 4.8 earthquake was generated on 24 April 2022. Available data from fault mechanism solutions revealed that the mainshock activated a reverse fault, while the aftershock generated a normal fault with a right-lateral component. The Balkan Peninsula stands as one of the most active geodynamic areas in Central and Eastern Europe due to its location within the collision zone between Eurasian and African tectonic plates and the Anatolian microplate. Recorded earthquakes in Bosnia and Herzegovina are related to the energy generated by the subduction of the African tectonic plate under Eurasia. Furthermore, the seismicity of Bosnia and Herzegovina, particularly its southern part, is profoundly influenced by the subduction of the Adriatic microplate under the Dinarides. The Dinarides are a mainly fold and thrust belt that extends from the Southern Alps in the northwest to the Hellenides in the southeast and make dominant the tectonic system of Bosnia and Herzegovina. In this study, two pairs of SAR images obtained from the Sentinel-1 satellite mission were utilized to generate satellite LOS surface displacements using the DInSAR method. Moreover, LOS displacements were decomposed into vertical and east–west horizontal components by combining ascending and descending satellite orbits. Ultimately, the InSAR results were analyzed and compared with the data obtained from the CROPOS CORS GNSS station in Metković (MET3).
Precision Irrigation Soil Moisture Mapper: A Thermal Inertia Approach to Estimating Volumetric Soil Water Content Using Unmanned Aerial Vehicles and Multispectral Imagery
Authors: Wienhold Kevin J. , Li Dongfeng , Fang Zheng N.
Published: May 2024

To address the issue of estimating soil moisture at a hyper-resolution scale, a methodology referred to as Precision Irrigation Soil Moisture Mapper (PrISMM), that includes three key components, is developed: high-resolution remotely sensed optical and thermal data, surface energy balance modeling, and site-specific soil analysis. An Unmanned Aerial Vehicle/System (UAV or UAS) collects high-resolution multispectral imagery in the Dallas–Fort Worth metropolitan study area. Orthomosaics are converted to thermal inertia estimates in a spatially distributed format using the remotely sensed data combined with a set of surface energy balance modeling equations. Using thermal and physical properties of soil gained from site-specific soil analysis, thermal inertia estimates were further converted from thermal inertia to daily volumetric soil water content (VSWC) with a horizonal resolution of 8.6 cm. A ground truthing dataset of measured VSWC values taken from a Time Domain Reflectometer was compared with model results, producing a reasonable correlation with an average coefficient of determination of (R2) = 0.79, an average root mean square error (RMSE) = 0.0408, and mean absolute error (MAE) = 0.0308. This study highlights a practical approach of estimating VSWC for irrigation purposes while providing superior spatio-temporal coverage over in situ methods. The authors envision that PrISMM can be implemented in water usage management by relating VSWC with weather forecasts and evapotranspiration rates to develop time-based spatially distributed irrigation management plans.
Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection
Authors: Li Yan , Weng Liguo , Xia Min , Hu Kai , Lin Haifeng
Published: May 2024

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.
Real-Time Estimation of BDS-3 Satellite Clock Offset with Ambiguity Resolution Using B1C/B2a Signals
Authors: Xie Wei , Wang Kan , Fu Wenju , Xie Shichao , Cui Bobin , Li Mengyuan
Published: May 2024

The third generation of the BeiDou navigation satellite system (BDS-3) can transmit five-frequency signals. The real-time satellite clock offset of BDS-3 is typically generated utilizing the B1I/B3I combination with the ambiguity-float solutions. By conducting the ambiguity resolution (AR), the reliability of the satellite clock offset can be improved. However, the performance of BDS-3 ambiguity-fixed real-time satellite clock offset with B1C/B2a signals remains unknown and unrevealed. In this contribution, the performance of the BDS-3 ambiguity-fixed satellite clock offset with the new B1C/B2a signals is investigated. One week of observation data from 85 stations was used to perform ambiguity-fixed satellite clock offset estimation. For B1I/B3I and B1C/B2a signals, the wide-lane (WL) uncalibrated phase delay (UPD) on the satellite end is fairly stable for one day, while the narrow-lane (NL) UPD standard deviation (STD) amounts to 0.122 and 0.081 cycles, respectively. The mean ambiguity fixing rate is 80.7% and 78.0% for these two signal combinations, and the time to first fix (TTFF) for the B1C/B2a signals is remarkably shorter than that of the B1I/B3I signals. The STDs of the ambiguity-float and -fixed satellite clock offsets are 0.033 and 0.026 ns, respectively, for the B1I/B3I combination, and it is reduced to 0.024 and 0.023 ns for B1C/B2a signals, respectively. Using the estimated UPD and clock offset products, the positioning performance of the kinematic Precise Point Positioning (PPP)-AR results amounts to 1.56, 1.23, and 4.46 cm in the east, north, and up directions for B1I/B3I signals, respectively. It is improved to 1.36, 1.16, and 4.25 cm using the products estimated with the B1C/B2a signals, with improvements of 12.8%, 5.7%, and 4.7% in three directions, respectively. The experiments showed that the performances of the ambiguity-fixed satellite clock offsets and the PPP-AR results using B1C/B2a signals are better than those of B1I/B3I.
Multi-Target Feeding-Behavior Recognition Method for Cows Based on Improved RefineMask
Authors: Li Xuwen , Gao Ronghua , Li Qifeng , Wang Rong , Liu Shanghao , Huang Weiwei , Yang Liuyiyi , Zhuo Zhenyuan
Published: May 2024

Within the current process of large-scale dairy-cattle breeding, to address the problems of low recognition-accuracy and significant recognition-error associated with existing visual methods, we propose a method for recognizing the feeding behavior of dairy cows, one based on an improved RefineMask instance-segmentation model, and using high-quality detection and segmentation results to realize the recognition of the feeding behavior of dairy cows. Firstly, the input features are better extracted by incorporating the convolutional block attention module into the residual module of the feature extraction network. Secondly, an efficient channel attention module is incorporated into the neck design to achieve efficient integration of feature extraction while avoiding the surge of parameter volume computation. Subsequently, the GIoU loss function is used to increase the area of the prediction frame to optimize the convergence speed of the loss function, thus improving the regression accuracy. Finally, the logic of using mask information to recognize foraging behavior was designed, and the accurate recognition of foraging behavior was achieved according to the segmentation results of the model. We constructed, trained, and tested a cow dataset consisting of 1000 images from 50 different individual cows at peak feeding times. The method’s effectiveness, robustness, and accuracy were verified by comparing it with example segmentation algorithms such as MSRCNN, Point_Rend, Cascade_Mask, and ConvNet_V2. The experimental results show that the accuracy of the improved RefineMask algorithm in recognizing the bounding box and accurately determining the segmentation mask is 98.3%, which is higher than that of the benchmark model by 0.7 percentage points; for this, the model parameter count size was 49.96 M, which meets the practical needs of local deployment. In addition, the technologies under study performed well in a variety of scenarios and adapted to various light environments; this research can provide technical support for the analysis of the relationship between cow feeding behavior and feed intake during peak feeding periods.
Towards a Remote Patient Monitoring Platform for Comprehensive Risk Evaluations for People with Diabetic Foot Ulcers
Authors: Cay Gozde , Finco M.G. , Garcia Jason , McNitt-Gray Jill L. , Armstrong David G. , Najafi Bijan
Published: May 2024

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0–10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.
Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review
Authors: Li Ningyang , Wang Zhaohui , Cheikh Faouzi Alaya
Published: May 2024

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.
Highway Deformation Monitoring by Multiple InSAR Technology
Authors: Zhao Dan , Yao Haonan , Gu Xingyu
Published: May 2024

Addressing the challenge of large-scale uneven deformation and the complexities of monitoring road conditions, this study focuses on a segment of the G15 Coastal Highway in Jiangsu Province. It employs PS-InSAR, SBAS-InSAR, and DS-InSAR techniques to comprehensively observe deformation. Analysis of 73 image datasets spanning 2018 to 2021 enables separate derivation of deformation data using distinct InSAR methodologies. Results are then interpreted alongside geological and geomorphological features. Findings indicate widespread deformation along the G15 Coastal Highway, notably significant settlement near Guanyun North Hub and uplift near Guhe Bridge. Maximum deformation rates exceeding 10 mm/year are observed in adjacent areas by all three techniques. To assess data consistency across techniques, identical observation points are identified, and correlation and difference analyses are conducted using statistical software. Results reveal a high correlation between the monitoring outcomes of the three techniques, with an average observation difference of less than 2 mm/year. This underscores the feasibility of employing a combination of these InSAR techniques for road deformation monitoring, offering a reliable approach for establishing real-time monitoring systems and serving as a foundation for ongoing road health assessments.
Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback
Authors: Davey Christopher P. , Shakeel Ismail , Deo Ravinesh C. , Salcedo-Sanz Sancho
Published: May 2024

In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences.
Electroencephalogram Functional Connectivity Analysis and Classification of Mental Arithmetic Working Memory Task
Authors: Gangapuram Harshini , Manian Vidya
Published: May 2024

Analyzing brain activity during mental arithmetic tasks provides insight into psychological disorders such as ADHD, dyscalculia, and autism. While most research is conducted on the static functional connectivity of the brain while performing a cognitive task, the dynamic changes of the brain, which provide meaningful information for diagnosing individual differences in cognitive tasks, are often ignored. This paper aims to classify electroencephalogram (EEG) signals for rest vs. mental arithmetic task performance, using Bayesian functional connectivity features in the sensor space as inputs into a graph convolutional network. The subject-specific (intrasubject) classification performed on 36 subjects for rest vs. mental arithmetic task performance achieved the highest subject-specific classification accuracy of 98% and an average accuracy of 91% in the beta frequency band, outperforming state-of-the-art methods. In addition, statistical analysis confirms the consistency of Bayesian functional connectivity features compared to traditional functional connectivity features. Furthermore, the graph-theoretical analysis of functional connectivity networks reveals that good-performance subjects had higher global efficiency, betweenness centrality, and closeness centrality than bad-performance subjects. The ablation study on the classification of three cognitive states (subtraction, music, and memory) achieved a classification accuracy of 97%, and visual working memory (n-back task) achieved a classification accuracy of 94%, confirming the consistency and reliability of the proposed methodology.
Introducing a Novel Concept for an Integrated Demolition Waste Recycling Center and the Establishment of a Stakeholder Network: A Case Study from Germany
Authors: Zabek Magdalena , Jegen Pauline , Kreiss Lillith
Published: May 2024

Using recycled aggregates has many positive environmental impacts because of the conservation of natural resources and minimization of waste. The use of recycled aggregates in downcycling processes is already common in Germany, whereas utilizing them to produce high-quality recycled concrete is rarely applied in practice. The reasons behind this lag have been investigated based on surveys and interviews with stakeholders. Miscommunication and missing information were identified in all stakeholder groups. Therefore, establishing a robust network and facilitating knowledge transfer by specifying the demand for recycled aggregates in the case study region have been considered as prerequisites. Therefore, the paper presents a novel concept of a stakeholder network for an integrated construction and demolition waste center. The conceptualization integrates the recycling companies and construction product manufacturers in one venue with research, service, and educational divisions. The design of the facilities is based on calculations regarding future construction activities and the demand for concrete production. The proposed concept aims to supply the region in the west of Germany with high-quality recycled products while also establishing a robust network that offers benefits in terms of logistical optimization and knowledge transfer.
A Study on the Relationship between Road Network Centrality and the Spatial Distribution of Commercial Facilities—A Case of Changchun, China
Authors: Shi Xiaochi , Liu Daqian , Gan Jing
Published: May 2024

Using the Urban Network Analysis Tool, the centrality of a road network (closeness centrality, betweenness centrality, and straightness centrality) was calculated, and the POI data of the commercial facilities were reclassified. KDE estimation was used to estimate the centrality of the traffic network, and the correlation coefficient was calculated to explore the spatial relationship between road network centrality and the types of commercial facilities (catering facilities, shopping facilities, residential life facilities, and financial and insurance facilities). The results indicate the following: (1) Closeness centrality displays a discernible “Core–Periphery” pattern, and the high-value areas of betweenness centrality are mainly concentrated around the main arterial roads of the city. In contrast, straightness centrality unveils a polycentric structure. (2) The spatial distribution of commercial facilities demonstrates a notable correlation with the centrality of the road network. From the perspective of centrality, the distribution of residential life facilities is most strongly influenced by road network centrality, followed by financial and insurance facilities and then catering facilities, with the distribution of shopping facilities being the least affected. (3) The centrality of the road network plays a crucial role in shaping the arrangement of commercial facilities. Closeness centrality significantly influences the distribution of residential life facilities, catering facilities, and shopping facilities. Betweenness centrality has a noteworthy impact on the selection of locations for financial and insurance facilities, as well as residential life facilities. Furthermore, areas characterized by better straightness centrality are preferred for the distribution of residential life facilities, financial and insurance facilities, and catering facilities. (4) The centrality of the road network has a greater influence on the arrangement of various commercial facilities than the population distribution.
Numerical Simulation of the Density Effect on the Macroscopic Transport Process of Tracer in the Ruhrstahl–Heraeus (RH) Vacuum Degasser
Authors: Xu Zhibo , Ouyang Xin , Chen Chao , Li Yihong , Wang Tianyang , Ren Ruijie , Yang Mingming , Zhao Yansong , Xue Liqiang , Wang Jia
Published: May 2024

Silicon steel (electrical steel) has been used in electric motors that are important components in sustainable new energy Electrical Vehicles (EVs). The Ruhrstahl–Heraeus process is commonly used in the refining process of silicon steel. The refining effect inside the RH degasser is closely related to the flow and mixing of molten steel. In this study, a 260 t RH was used as the prototype, and the transport process of the passive scalar tracer (virtual tracer) and salt tracer (considering density effect) was studied using numerical simulation and water model research methods. The results indicate that the tracer transports from the up snorkel of the down snorkel to the bottom of the ladle, and then upwards from the bottom of the ladle to the top of the ladle. Density and gravity, respectively, play a promoting and hindering role in these two stages. In different areas of the ladle, density and gravity play a different degree of promotion and obstruction. Moreover, in different regions of the ladle, the different circulation strength leads to the different promotion degrees and obstruction degrees of the density. This results in the difference between the concentration growth rate of the salt tracer and the passive scalar in different regions of the ladle top. From the perspective of mixing time, density and gravity have no effect on the mixing time at the bottom of the ladle, and the difference between the passive scalar and NaCl solution tracer is within the range of 1–5%. For a larger dosage of tracer case, the difference range is reduced. However, at the top of the ladle, the average mixing time for the NaCl solution case is significantly longer than that of the passive scalar case, within the range of 3–14.7%. For a larger dosage of tracer case, the difference range is increased to 17.4–41.1%. It indicates that density and gravity delay the mixing of substances at the top area of the ladle, and this should be paid more attention when adding denser alloys in RH degasser.
The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning
Authors: Jia Muxin , Liu Ang , Narahara Taro
Published: May 2024

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.
Rapid Investigation of Oil Pollution in Water-Combined Induced Fluorescence and Random Sample Consensus Algorithm
Authors: Wu Hui , Wang Ziyi , Zhao Youquan
Published: May 2024

The global issue of oil spreading in water poses a significant environmental challenge, emphasizing the critical need for the accurate determination and monitoring of oil content in aquatic environments to ensure sustainable development of the environment. However, the complexity arises from challenges such as oil dispersion, clustering, and non-uniform distribution, making it difficult to obtain real-time oil concentration data. This paper introduces a sophisticated system for acquiring induced fluorescence spectra specifically designed for the quantitative analysis of oil pollutants. The paper involved measuring the fluorescence spectra across 20 concentration gradients (ranging from 0 to 1000 mg/L) for four distinct oil samples: 92# Gasoline, Mobil Motor Oil 20w-40, Shell 10w-40 engine oil, and Soybean Oil. The research focused on establishing a relationship model between relative fluorescence intensity and concentration, determined at the optimal excitation wavelength, utilizing the segmented Random Sample Consensus (RANSAC) algorithm. Evaluation metrics, including standard addition recovery, average recovery, relative error, and average relative error, were employed to assess the accuracy of the proposed model. The experimental findings suggest that the average recovery rates for the four samples ranged between 99.61% and 101.15%, with the average relative errors falling within the range of 2.04% to 3.14%. These results underscore the accuracy and efficacy of the detection methodology presented in this paper. Importantly, this accuracy extends to scenarios involving heavier oil pollution. This paper exhibits exceptional sensitivity, enabling precise detection of diverse oil spills within the concentration range of 0~1000 mg/L in water bodies, offering valuable insights for water quality monitoring and sustainable development of the environment.
Assessing the EU Climate and Energy Policy Priorities for Transport and Mobility through the Analysis of User-Generated Social Media Content Based on Text-Mining Techniques
Authors: Nikolaidou Anastasia , Kopsacheilis Aristomenis , Gavanas Nikolaos , Politis Ioannis
Published: May 2024

For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.