bluuebunny/crossref_metadata_2025 huggingface dataset:
Health Claims for Sports Drinks—Analytical Assessment
according to European Food Safety Authority’s Scientific Opinion
Authors: Rodríguez-Hernández María Dolores , Gil-Izquierdo Ángel , García Carlos Javier , Gabaldón José Antonio , Ferreres Federico , Giménez-Monzó Daniel , Martínez-Sanz José Miguel
Published: June 2024

In Europe, sports food supplements (SSFs) are subject to specific laws and regulations. Up to 70% of athletes are highly influenced by the information on the label or the advertisement of the SSF, which often does not correspond to the scientific evidence, such as health claims. The aim is to analyze such claims relating to sports drinks (SDs) in commercial messages. To this end, an observational and cross-sectional study was conducted based on the analysis of the content and degree of adequacy of the health claims indicated on the labelling or technical data sheet of the SDs with those established by the European legislation in force according to the European Food Safety Authority (EFSA). The SSFs were searched for via Amazon and Google Shopping. A total of 114 health claims were evaluated. No claim fully conformed with the recommendations. A total of 14 claims (n = 13 products) almost conformed to the recommendations; they were “Maintain endurance level in exercises requiring prolonged endurance”, “Improve water absorption during physical exercise”, and “Improved physical performance during high intensity, high duration physical exercise in trained adults”, representing 12.3% of the total (n = 114). The vast majority of the claims identified indicated an unproven cause–effect and should be modified or eliminated, which amounts to food fraud towards the consumer.
The Role of School Health Promotion in Students’ Dietary Intake during School Hours: A Qualitative Comparative Analysis
Authors: Vonk Lisanne , van Empelen Pepijn , Huijts Tim , Eekhout Iris , Jansen Maria
Published: June 2024

Many children in the Netherlands do not adhere to dietary guidelines. Therefore, the Healthy School (HS) program stimulates healthier dietary intake of students through schools. However, evaluating the effectiveness of school health promotion in improving dietary intake is challenging due to the influence of contextual factors. Qualitative Comparative Analysis (QCA) considers these contextual factors. Therefore, we performed a QCA to examine which (combinations of) contextual factors contribute to the healthier dietary intake of students during school hours in primary schools (approximate age range children 4–12 years) and secondary schools (age range 12–18 years) when implementing the HS program for nutrition. Data were collected mainly through interviewing school staff and a school-level questionnaire in fifteen primary schools and twelve secondary schools. We included five factors for primary schools: implementation of the HS program for nutrition, degree of implementation, socioeconomic status, parental support, and student support. For secondary schools, we included school environment instead of parental and student support. For primary schools, the best results were obtained if the HS program for nutrition was implemented in high socioeconomic status schools with a combination of high implementation, parental support, and student support. Findings indicate that if secondary schools have an impeding environment and low socioeconomic status, implementation of the HS program for nutrition can result in healthier dietary intake.
No Evidence for Cross-Sectional or Longitudinal Associations between Cognitive Flexibility Performances and Nutritional Status in a Cohort of Inpatients with Anorexia Nervosa
Authors: Castaño Lutzi , Fatseas Melina , Cuzacq Maylis , Mattar Lama , Godart Nathalie , Berthoz Sylvie
Published: June 2024

Background: According to the Cognitive–Interpersonal model of anorexia nervosa (AN), the combined influence of cognitive and socio-emotional difficulties would constitute vulnerability and maintaining factors. Poor cognitive flexibility is one of the endophenotypic candidates (i.e., a trait marker) of the disorder, but few studies have examined its association with illness symptom variations, notably weight status. The study aimed to evaluate the relationships between cognitive flexibility performances and nutritional status indices (BMI; body composition) at different times of the disorder. Methods: Cross-sectional and longitudinal associations between cognitive flexibility (TAP 2.1) and nutritional status indices, along with anxious and depressive (HAD) and eating disorder (EDE-Q) symptomatology were investigated using univariate and multivariate analyses in a cohort of AN inpatients evaluated at hospital admission (N = 167) and discharge (N = 94). Results: We found no or negligible associations between nutritional status and HAD or EDE-Q scores or cognitive flexibility performances, either cross-sectionally or longitudinally. Cognitive performances did not significantly differ between the AN subtypes. Conclusions: In agreement with the Cognitive–Interpersonal model of AN, cognitive flexibility is independent of nutritional status, as well as the AN subtype. It is also independent of the levels of anxious, depressive, or ED symptomatology. A new therapeutic approach targeting cognitive flexibility and intolerance to change could benefit severely emaciated people with AN, regardless of disease subtype and level of dysphoria.
Correction: Ferdaus et al. Taro Roots: An Underexploited Root Crop. Nutrients 2023, 15, 3337
Authors: Ferdaus Md. Jannatul , Chukwu-Munsen Ezzine , Foguel Aline , da Silva Roberta Claro
Published: June 2024

The Editorial Office was made aware that the original publication [...]
Comparative Evaluation of the Sensory Qualities of Refined and Wholegrain Rice as Ingredients within Mixed Dishes
Authors: Da Graça Andreia , Teinye-Boyle Foyeke , Brownlee Iain
Published: June 2024

Low wholegrain food consumption is a leading dietary risk for avoidable morbidity and mortality globally, with limited sensory acceptability suggested to be a challenge for changing behaviour. This study aimed to evaluate the sensory acceptability of both wholegrain (brown) and refined (white) rice in common preparations. Four brown- and white-rice-containing dishes (Garlic Rice, Rice and Beans, Jollof Rice, and Rice Pudding) were tested. Quantitative (five-point scales) and qualitative (open question responses) sensory information were collected for dish appearance, aroma, taste, and texture. All four characteristics were scored equally acceptable in Rice and Beans and Rice Pudding (p > 0.05) between paired comparisons for brown and white rice. Scores were significantly lower for all characteristics for Jollof Rice (p ≤ 0.002), and lower for Aroma (median (lower quartile–upper quartile)) for brown (3.5 (3–4)) vs. white rice (4 (4–5)), p = 0.006). Appearance (brown (3 (3–4)) vs. white rice (4 (3.25–5)), p = 0.012), and Texture (brown 3 (2.25–4) vs. white rice (4 (4–5)), p < 0.001) for Garlic Rice. Familiarity and appealingness were qualitative themes aligned with the higher acceptability of white-rice-containing dishes. Certain dishes appear to mask key negative sensory attributes of wholegrain foods, possibly representing a means to increase wholegrain ingredient acceptability, thereby potentially improving individual/population-level intake.
The Impact of Chronic Oral Beta-Blocker Intake on Intravenous Bolus Landiolol Response in Hospitalized Intensive Care Patients with Sudden-Onset Supraventricular Tachycardia—A Post Hoc Analysis of a Cross-Sectional Trial
Authors: Eibensteiner Felix , Mosor Emmilie , Tihanyi Daniel , Anders Sonja , Kornfehl Andrea , Neymayer Marco , Oppenauer Julia , Veigl Christoph , Al Jalali Valentin , Domanovits Hans , Sulzgruber Patrick , Schnaubelt Sebastian
Published: June 2024

Background: Landiolol, a highly cardioselective agent with a short half-life (2.4–4 min), is commonly used as a perfusor or bolus application to treat tachycardic arrhythmia. Some small studies suggest that prior oral β-blocker use results in a less effective response to intravenous β-blockers. Methods: This study investigated whether prior chronic oral β-blocker (Lβ) or no prior chronic oral β-blocker (L–) intake influences the response to intravenous push-dose Landiolol in intensive care patients with acute tachycardic arrhythmia. Results: The effects in 30 patients (67 [55–72] years) were analyzed, 10 (33.3%) with and 20 (66.7%) without prior oral β-blocker therapy. Arrhythmias were diagnosed as tachycardic atrial fibrillation in 14 patients and regular, non-fluid-dependent, supraventricular tachycardia in 16 cases. Successful heart rate control (Lβ 4 vs. L– 7, p = 1.00) and rhythm control (Lβ 3 vs. L– 6, p = 1.00) did not significantly differ between the two groups. Both groups showed a significant decrease in heart rate when comparing before and after the bolus administration, without significant differences between the two groups (Lβ −26/min vs. L– −33/min, p = 0.528). Oral β-blocker therapy also did not influence the change in mean arterial blood pressure after Landiolol bolus administration (Lβ −5 mmHg vs. L– −4 mmHg, p = 0.761). Conclusions: A prior chronic intake of β-blockers neither affected the effectiveness of push-dose Landiolol in heart rate or rhythm control nor impacted the difference in heart rate or mean arterial blood pressure before and after the Landiolol boli.
Bioprospecting, Synergistic Antifungal and Toxicological Aspects of the Hydroxychalcones and Their Association with Azole Derivates against Candida spp. for Treating Vulvovaginal Candidiasis
Authors: Fernandes Lígia de Souza , Ogasawara Letícia Sayuri , Medina-Alarcón Kaila Petronila , dos Santos Kelvin Sousa , de Matos Silva Samanta , de Assis Letícia Ribeiro , Regasini Luís Octavio , de Oliveira Anselmo Gomes , Mendes Giannini Maria José Soares , Scarpa Maria Virginia , Fusco Almeida Ana Marisa
Published: June 2024

Vulvovaginal candidiasis (VVC) remains a prevalent fungal disease, characterized by challenges, such as increased fungal resistance, side effects of current treatments, and the rising prevalence of non-albicans Candida spp. naturally more resistant. This study aimed to propose a novel therapeutic approach by investigating the antifungal properties and toxicity of 2-hydroxychalcone (2-HC) and 3′-hydroxychalcone (3′-HC), both alone and in combination with fluconazole (FCZ) and clotrimazole (CTZ). A lipid carrier (LC) was also developed to deliver these molecules. The study evaluated in vitro anti-Candida activity against five Candida species and assessed cytotoxicity in the C33-A cell line. The safety and therapeutic efficacy of in vivo were tested using an alternative animal model, Galleria mellonella. The results showed antifungal activity of 2-HC and 3′-HC, ranging from 7.8 to 31.2 as fungistatic and 15.6 to 125.0 mg/L as fungicide effect, with cell viability above 80% from a concentration of 9.3 mg/L (2-HC). Synergistic and partially synergistic interactions of these chalcones with FCZ and CTZ demonstrated significant improvement in antifungal activity, with MIC values ranging from 0.06 to 62.5 mg/L. Some combinations reduced cytotoxicity, achieving 100% cell viability in many interactions. Additionally, two LCs with suitable properties for intravaginal application were developed. These formulations demonstrated promising therapeutic efficacy and low toxicity in Galleria mellonella assays. These results suggest the potential of this approach in developing new therapies for VVC.
Selective Capturing of the CO2 Emissions Utilizing Ecological (3-Mercaptopropyl)trimethoxysilane-Coated Porous Organic Polymers in Composite Materials
Authors: Kotp Mohammed G. , Kuo Shiao-Wei
Published: June 2024

Capturing carbon dioxide (CO2) is still a major obstacle in the fight against climate change and the reduction of greenhouse gas emissions. To address this problem, we employed a simple Friedel–Crafts alkylation to investigate the effectiveness of porous organic polymers (POPs) based on triphenylamine (TPA) and trihydroxy aryl terms derived from chloranil (CH), designated as TPA-CH POP. We then treated the TPA-CH POP with (3-mercaptopropyl)trimethoxysilane (3-MPTS), forming a TPA-CH POP-SH nanocomposite to enhance CO2 capture. Utilizing FTIR, solid-state NMR, SEM, TEM, along with XPS techniques, the molecular makeup, morphological characteristics, as well as physical features of TPA-CH POP and the TPA-CH POP-SH nanocomposite were thoroughly explored. Upon scorching to 800 °C, the TPA-CH POP-SH nanocomposite demonstrated more thermal durability over TPA-CH POP, achieving a char yield of up to 71.5 wt.%. The TPA-CH POP-SH nanocomposite displayed a 2.5-times better CO2 capture, as well as a comparable adsorption capacity of 48.07 cm3 g−1 at 273 K. Additionally, we found that the TPA-CH POP-SH nanocomposite exhibited an improved CO2/nitrogen (N2) selectivity versus the original TPA-CH POP. Typical enthalpy changes for CO2 capture were somewhat increased by the 3-MPTS coating, indicating greater binding energies between CO2 molecules and the adsorbent surface. Our outcomes demonstrate that a TPA-CH POP composite coated with MPTS is a viable candidate for effective CO2 capture uses. Our findings encourage the investigation of different functional groups and optimization strategies.
Enhanced Hydrogen Peroxide Decomposition in a Continuous-Flow Reactor over Immobilized Catalase with PAES-C
Authors: Li Yunrui , Zhang Yu , Zhang Wenyu , Wu Hao , Zhang Shaoyin
Published: June 2024

Due to the specificity, high efficiency, and gentleness of enzyme catalysis, the industrial utilization of enzymes has attracted more and more attention. Immobilized enzymes can be recovered/recycled easily compared to their free forms. The primary benefit of immobilization is protection of the enzymes from harsh environmental conditions (e.g., elevated temperatures, extreme pH values, etc.). In this paper, catalase was successfully immobilized in a poly(aryl ether sulfone) carrier (PAES-C) with tunable pore structure as well as carboxylic acid side chains. Moreover, immobilization factors like temperature, time, and free-enzyme dosage were optimized to maximize the value of the carrier and enzyme. Compared with free enzyme, the immobilized-enzyme exhibited higher enzymatic activity (188.75 U g−1, at 30 °C and pH 7) and better thermal stability (at 60 °C). The adsorption capacity of enzyme protein per unit mass carrier was 4.685 mg. Hydrogen peroxide decomposition carried out in a continuous-flow reactor was selected as a model reaction to investigate the performance of immobilized catalase. Immobilized-enzymes showed a higher conversion rate (90% at 8 mL/min, 1 h and 0.2 g) compared to intermittent operation. In addition, PAES-C has been synthesized using dichlorodiphenyl sulfone and the renewable resource bisphenolic acid, which meets the requirements of green chemistry. These results suggest that PAES-C as a carrier for immobilized catalase could improve the catalytic activity and stability of catalase, simplify the separation of enzymes, and exhibit good stability and reusability.
Degradation Behaviors of Polylactic Acid, Polyglycolic Acid, and Their Copolymer Films in Simulated Marine Environments
Authors: Chen Zeyu , Zhang Xi , Fu Ye , Jin Yujuan , Weng Yunxuan , Bian Xinchao , Chen Xuesi
Published: June 2024

Poly(lactic acid) (PLA) and poly(glycolic acid) (PGA) are extensively studied biodegradable polymers. However, the degradation behavior of their copolymer, poly(lactic-co-glycolic acid) (PLGA), in marine environments has not yet been confirmed. In this study, the changes in macroscopic and microscopic morphology, thermal properties, aggregation, and chemical structure of PLA, PGA, PLGA-85, and PLGA-75 (with 85% and 75% LA content) in simulated marine environments were investigated. Results revealed that degradation occurred through hydrolysis of ester bonds, and the degradation rate of PGA was faster than that of PLA. The amorphous region degraded preferentially over the crystalline region, leading to cleavage-induced crystallization and decreased thermal stability of PLA, PLGA-85, and PLGA-75. The crystal structures of PLGAs were similar to those of PLA, and the higher GA content, the faster was the degradation rate. This study provides a deeper understanding of the seawater degradation behaviors of PLA, PGA, and their copolymers, and provides guidance for the preparation of materials with controllable degradation performance.
Predicting and Understanding the Pacific Decadal Oscillation Using Machine Learning
Authors: Yao Zhixiong , Xu Dongfeng , Wang Jun , Ren Jian , Yu Zhenlong , Yang Chenghao , Xu Mingquan , Wang Huiqun , Tan Xiaoxiao
Published: June 2024

The Pacific Decadal Oscillation (PDO), the dominant pattern of sea surface temperature anomalies in the North Pacific basin, is an important low-frequency climate phenomenon. Leveraging data spanning from 1871 to 2010, we employed machine learning models to predict the PDO based on variations in several climatic indices: the Niño3.4, North Pacific index (NPI), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio Extension (KOE) region (SSH_KOE and Ther_KOE), as well as the Arctic Oscillation (AO) and Atlantic Multi-decadal Oscillation (AMO). A comparative analysis of the temporal and spatial performance of six machine learning models was conducted, revealing that the Gated Recurrent Unit model demonstrated superior predictive capabilities compared to its counterparts, through the temporal and spatial analysis. To better understand the inner workings of the machine learning models, SHapley Additive exPlanations (SHAP) was adopted to present the drivers behind the model’s predictions and dynamics for modeling the PDO. Our findings indicated that the Niño3.4, North Pacific index, and SSH_KOE were the three most pivotal features in predicting the PDO. Furthermore, our analysis also revealed that the Niño3.4, AMO, and Ther_KOE indices were positively associated with the PDO, whereas the NPI, SSH_KOE, and AO indices exhibited negative correlations.
Characteristics of Yellow Sea Fog under the Influence of Eastern China Aerosol Plumes
Authors: Liang Jiakun , Griswold Jennifer D. Small
Published: June 2024

Sea fog is a societally relevant phenomenon that occurs under the influence of specific oceanic and atmospheric conditions including aerosol conditions. The Yellow Sea region in China regularly experiences sea fog events, of varying intensity, that impact coastal regions and maritime activities. The occurrence and structure of fog are impacted by the concentration of aerosols in the air where the fog forms. Along with industrial development, air pollution has become a serious environmental problem in Northeastern China. These higher pollution levels are confirmed by various satellite remote sensing instruments including the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite that observes aerosol and cloud properties. These observations show a clear influence of aerosol loading over the Yellow Sea region, which can impact regional sea fog. In this study, high-resolution data sets from MODIS Aqua L2 are used to investigate the relationships between cloud properties and aerosol features. Using a bi-variate comparison method, we find that, for most cases, larger values of COT (cloud optical thickness) are related to both a smaller DER (droplet effective radius) and higher CTH (cloud top height). However, in the cases where fog is thinner with many zero values in CTH, the larger COT is related to both a smaller DER and CTH. For fog cases where the aerosol type is dominated by smoke (e.g., confirmed fire activities in the East China Plain), the semi-direct effect is indicated and may play a role in determining fog structure such that a smaller DER corresponds with thinner fog and smaller COT values.
High-Precision Monitoring Method for Bridge Deformation Measurement and Error Analysis Based on Terrestrial Laser Scanning
Authors: Zhou Yin , Zhu Jinyu , Zhao Lidu , Hu Guotao , Xin Jingzhou , Zhang Hong , Yang Jun
Published: June 2024

In bridge structure monitoring and evaluation, deformation data serve as a crucial basis for assessing structural conditions. Different from discrete monitoring points, spatially continuous deformation modes provide a comprehensive understanding of deformation and potential information. Terrestrial laser scanning (TLS) is a three-dimensional deformation monitoring technique that has gained wide attention in recent years, demonstrating its potential in capturing structural deformation models. In this study, a TLS-based bridge deformation mode monitoring method is proposed, and a deformation mode calculation method combining sliding windows and surface fitting is developed, which is called the SWSF method for short. On the basis of the general characteristics of bridge structures, a deformation error model is established for the SWSF method, with a detailed quantitative analysis of each error component. The analysis results show that the deformation monitoring error of the SWSF method consists of four parts, which are related to the selection of the fitting function, the density of point clouds, the noise of point clouds, and the registration accuracy of point clouds. The error caused by point cloud noise is the main error component. Under the condition that the noise level of point clouds is determined, the calculation error of the SWSF method can be significantly reduced by increasing the number of points of point clouds in the sliding window. Then, deformation testing experiments were conducted under different measurement distances, proving that the proposed SWSF method can achieve a deformation monitoring accuracy of up to 0.1 mm. Finally, the proposed deformation mode monitoring method based on TLS and SWSF was tested on a railway bridge with a span of 65 m. The test results showed that in comparison with the commonly used total station method, the proposed method does not require any preset reflective markers, thereby improving the deformation monitoring accuracy from millimeter level to submillimeter level and transforming the discrete measurement point data form into spatially continuous deformation modes. Overall, this study introduces a new method for accurate deformation monitoring of bridges, demonstrating the significant potential for its application in health monitoring and damage diagnosis of bridge structures.
Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea
Authors: Alrabayah Osama , Caus Danu , Watson Robert Alban , Schulten Hanna Z. , Weigel Tobias , Rüpke Lars , Al-Halbouni Djamil
Published: June 2024

Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data.
A Side-Lobe Denoise Process for ISAR Imaging Applications: Combined Fast Clean and Spatial Focus Technique
Authors: Xv Jia-Hua , Zhang Xiao-Kuan , Zong Bin-feng , Zheng Shu-Yu
Published: June 2024

The presence of side-lobe noise degrades the image quality and adversely affects the performance of inverse synthetic aperture radar (ISAR) image understanding applications, such as automatic target recognition (ATR), target detection, etc. However, methods reliant on data processing, such as windowing, inevitably encounter resolution reduction, and current deep learning approaches under-appreciate the sparsity inherent in ISAR images. Taking the above analysis into consideration, a convolutional neural network-based process for ISAR side-lobe noise training is proposed in this paper. The proposed processing, based on the ISAR images sparsity characteristic analysis, undergoes enhancements in three core ideas, dataset construction, prior network design, and loss function improvements. In the realm of dataset construction, we introduce a bin-by-bin fast clean algorithm and accelerate the processing speed significantly on the basis of image complete information. Subsequently, a spatial attention layer is incorporated into the prior network designed to augment the network’s focus on the crucial regions of ISAR images. In addition, a loss function featuring a weighting factor is devised to ensure the precise recovery of the strong scattering point. Simulation experiments demonstrate that the proposed process achieves significant improvements in both quantitative and qualitative results over the classical denoise methods.
Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning
Authors: Huang Jia , Chen Zhen , Liu Sheng-Zheng , Zhang Hao , Long Hai-Xia
Published: June 2024

The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model’s detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.
Optical Design of a Hyperspectral Remote-Sensing System Based on an Image-Slicer Integral Field Unit in the Short-Wave Infrared Band
Authors: Ding Yi , Liu Chunyu , Zhang Guoxiu , Hao Pengfei , Liu Shuai , Zhao Yingming , Zhang Yuxin , Liu Hongxin
Published: June 2024

Grating-type spectral imaging systems are frequently employed in scenes for high-resolution remote-sensing observations of the Earth. However, the entrance of the grating-type spectral imaging system is a slit or a pinhole. This structure relies on the push broom method, which presents a challenge in capturing spectral information of transiently changing targets. To address this issue, the IFU is used to slice the focal plane of the telescope system, thereby expanding the instantaneous field of view (IFOV) of the grating-type spectral imaging system. The aberrations introduced by the expansion of the single-slice field of view (FOV) of the IFU are corrected, and the conversion of the IFU’s FOV from arcseconds to degrees is achieved. The design of a spectral imaging system based on an image-slicer IFU for remote sensing is finally completed. The system has a wavelength range of 1400 nm to 2000 nm, and a spectral resolution of better than 3 nm. Compared with the traditional grating-type spectral imaging system, its IFOV is expanded by a factor of four. And it allows for the capture of complete spectral information of transiently changing targets through a single exposure. The simulation results demonstrate that the system has good performance at each sub-slit, thereby validating the effectiveness and advantages of the proposed system for dynamic target capture in remote sensing.
Channel-Blind Joint Source–Channel Coding for Wireless Image Transmission
Authors: Yuan Hongjie , Xu Weizhang , Wang Yuhuan , Wang Xingxing
Published: June 2024

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network’s self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model’s performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.
A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder
Authors: Song Shan , Zhao Xiaoyong , Zhang Zhengbing , Luo Mingzhang
Published: June 2024

The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.
Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework
Authors: Siddique Muhammad Farooq , Ahmad Zahoor , Ullah Niamat , Ullah Saif , Kim Jong-Myon
Published: June 2024

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.
Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm
Authors: Ma Tingchen , Kong Lingxin , Ou Yongsheng , Xu Sheng
Published: June 2024

Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 ×103 order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward–reverse–forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having O(1) time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.
An Enhanced FGI-GSRx Software-Defined Receiver for the Execution of Long Datasets
Authors: Liaquat Muwahida , Bhuiyan Mohammad Zahidul H. , Islam Saiful , Pääkkönen Into , Kaasalainen Sanna
Published: June 2024

The Global Navigation Satellite System (GNSS) software-defined receivers offer greater flexibility, cost-effectiveness, customization, and integration capabilities compared to traditional hardware-based receivers, making them essential for a wide range of applications. The continuous evolution of GNSS research and the availability of new features require these software-defined receivers to upgrade continuously to facilitate the latest requirements. The Finnish Geospatial Research Institute (FGI) has been supporting the GNSS research community with its open-source implementations, such as a MATLAB-based GNSS software-defined receiver `FGI-GSRx’ and a Python-based implementation `FGI-OSNMA’ for utilizing Galileo’s Open Service Navigation Message Authentication (OSNMA). In this context, longer datasets are crucial for GNSS software-defined receivers to support adaptation, optimization, and facilitate testing to investigate and develop future-proof receiver capabilities. In this paper, we present an updated version of FGI-GSRx, namely, FGI-GSRx-v2.0.0, which is also available as an open-source resource for the research community. FGI-GSRx-v2.0.0 offers improved performance as compared to its previous version, especially for the execution of long datasets. This is carried out by optimizing the receiver’s functionality and offering a newly added parallel processing feature to ensure faster capabilities to process the raw GNSS data. This paper also presents an analysis of some key design aspects of previous and current versions of FGI-GSRx for a better insight into the receiver’s functionalities. The results show that FGI-GSRx-v2.0.0 offers about a 40% run time execution improvement over FGI-GSRx-v1.0.0 in the case of the sequential processing mode and about a 59% improvement in the case of the parallel processing mode, with 17 GNSS satellites from GPS and Galileo. In addition, an attempt is made to execute v2.0.0 with MATLAB’s own parallel computing toolbox. A detailed performance comparison reveals an improvement of about 43% in execution time over the v2.0.0 parallel processing mode for the same GNSS scenario.
High-Magnification Object Tracking with Ultra-Fast View Adjustment and Continuous Autofocus Based on Dynamic-Range Focal Sweep
Authors: Zhang Tianyi , Shimasaki Kohei , Ishii Idaku , Namiki Akio
Published: June 2024

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking.
Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model
Authors: Chen Zhen , Liu Sheng-Zheng , Huang Jia , Xiu Yu-Han , Zhang Hao , Long Hai-Xia
Published: June 2024

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.
A Low-Cost Sensing Solution for SHM, Exploiting a Dedicated Approach for Signal Recognition
Authors: Andò Bruno , Greco Danilo , Navarra Giacomo , Lo Iacono Francesco
Published: June 2024

Health assessment and preventive maintenance of structures are mandatory to predict injuries and to schedule required interventions, especially in seismic areas. Structural health monitoring aims to provide a robust and effective approach to obtaining valuable information on structural conditions of buildings and civil infrastructures, in conjunction with methodologies for the identification and, sometimes, localization of potential risks. In this paper a low-cost solution for structural health monitoring is proposed, exploiting a customized embedded system for the acquisition and storing of measurement signals. Experimental surveys for the assessment of the sensing node have also been performed. The obtained results confirmed the expected performances, especially in terms of resolution in acceleration and tilt measurement, which are 0.55 mg and 0.020°, respectively. Moreover, we used a dedicated algorithm for the classification of recorded signals in the following three classes: noise floor (being mainly related to intrinsic noise of the sensing system), exogenous sources (not correlated to the dynamic behavior of the structure), and structural responses (the response of the structure to external stimuli, such as seismic events, artificially forced and/or environmental solicitations). The latter is of main interest for the investigation of structures’ health, while other signals need to be recognized and filtered out. The algorithm, which has been tested against real data, demonstrates relevant features in performing the above-mentioned classification task.
Preparation of CNT/CNF/PDMS/TPU Nanofiber-Based Conductive Films Based on Centrifugal Spinning Method for Strain Sensors
Authors: Mei Shunqi , Xu Bin , Wan Jitao , Chen Jia
Published: June 2024

Flexible conductive films are a key component of strain sensors, and their performance directly affects the overall quality of the sensor. However, existing flexible conductive films struggle to maintain high conductivity while simultaneously ensuring excellent flexibility, hydrophobicity, and corrosion resistance, thereby limiting their use in harsh environments. In this paper, a novel method is proposed to fabricate flexible conductive films via centrifugal spinning to generate thermoplastic polyurethane (TPU) nanofiber substrates by employing carbon nanotubes (CNTs) and carbon nanofibers (CNFs) as conductive fillers. These fillers are anchored to the nanofibers through ultrasonic dispersion and impregnation techniques and subsequently modified with polydimethylsiloxane (PDMS). This study focuses on the effect of different ratios of CNTs to CNFs on the film properties. Research demonstrated that at a 1:1 ratio of CNTs to CNFs, with TPU at a 20% concentration and PDMS solution at 2 wt%, the conductive films crafted from these blended fillers exhibited outstanding performance, characterized by electrical conductivity (31.4 S/m), elongation at break (217.5%), and tensile cycling stability (800 cycles at 20% strain). Furthermore, the nanofiber-based conductive films were tested by attaching them to various human body parts. The tests demonstrated that these films effectively respond to motion changes at the wrist, elbow joints, and chest cavity, underscoring their potential as core components in strain sensors.
Experimental and Numerical Investigation of Bogie Hunting Instability for Railway Vehicles Based on Multiple Sensors
Authors: Zheng Biao , Wei Lai , Zeng Jing , Zhang Dafu
Published: June 2024

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel–rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel–rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.