bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Gamma-Phase Aluminum Oxide Nanopowder and Polyester–Glass Recyclate Filler on the Destruction Process of Composite Materials Reinforced by Glass Fiber
Authors: Panasiuk Katarzyna , Dudzik Krzysztof , Hajdukiewicz Grzegorz , Abramczyk Norbert
Published: August 2024

Recycling of composite materials is an important global issue due to the wide use of these materials in many industries. Waste management options are being explored. Mechanical recycling is one of the methods that allows obtaining polyester–glass recyclate in powder form as a result of appropriate crushing and grinding of waste. Due to the fact that the properties of composites can be easily modified by adding various types of fillers and nanofillers, this is one of the ways to improve the properties of such complex composite materials. This article presents the strength parameters of composites with the addition of fillers in the form of polyester–glass recyclate and a nanofiller in the form of gamma-phase aluminum nanopowder. To analyze the obtained results, Kolmogorov-Sinai (K-S) metric entropy was used to determine the transition from the elastic to the viscoelastic state in materials without and with the addition of nanoaluminum, during a static tensile test. The tests included samples with the addition of fillers and nanofillers, as well as a base sample without any additives. The article presents the strength parameters obtained from a testing machine during a static tensile test. Additionally, the acoustic emission method was used during the research. Thanks to which, graphs of the effective value of the electrical signal (RMS) were prepared as a function of time, the parameters were previously identified as extremely useful for analyzing the destruction process of composite materials. The values obtained from the K-S metric entropy method and the acoustic emission method were plotted on sample stretching graphs. The influence of the nanofiller and filler on these parameters was also analyzed. The presented results showed that the aluminum nanoadditive did not increase the strength parameters of the composite with recyclate as a result of the addition of aluminum nanofiller; however, its addition influenced the operational parameters, which is reflected in a 5% increase in the UTS value (from 55% to 60%).
Fabricating High Strength Bio-Based Dynamic Networks from Epoxidized Soybean Oil and Poly(Butylene Adipate-co-Terephthalate)
Authors: Xu Bin , Xia Zhong-Ming , Zhan Rui , Yang Ke-Ke
Published: August 2024

Amid the rapid development of modern society, the widespread use of plastic products has led to significant environmental issues, including the accumulation of non-degradable waste and extensive consumption of non-renewable resources. Developing healable, recyclable, bio-based materials from abundant renewable resources using diverse dynamic interactions attracts increasing global attention. However, achieving a good balance between the self-healing capacity and mechanical performance, such as strength and toughness, remains challenging. In our study, we address this challenge by developing a new type of dynamic network from epoxidized soybean oil (ESO) and poly(butylene adipate-co-terephthalate) (PBAT) with good strength and toughness. For the synthetic strategy, a thiol–epoxy click reaction was conducted to functionalize ESO with thiol and hydroxyl groups. Subsequently, a curing reaction with isocyanates generated dynamic thiourethane and urethane bonds with different bonding energies in the dynamic networks to reach a trade-off between dynamic features and mechanical properties; amongst these, the thiourethane bonds with a lower bonding energy provide good dynamic features, while the urethane bonds with a higher bonding energy ensure good mechanical properties. The incorporation of flexible PBAT segments to form the rational multi-phase structure with crystalline domains further enhanced the products. A typical sample, OTSO100-PBAT100, exhibited a tensile strength of 33.2 MPa and an elongation at break of 1238%, demonstrating good healing capacity and desirable mechanical performance. This study provides a promising solution to contemporary environmental and energy challenges by developing materials that combine mechanical and repair properties. It addresses the specific gap of achieving a trade-off between tensile strength and elongation at break in bio-based self-healing materials, promising a wide range of applications.
Effects of Macro Fibers on Crack Opening Reduction in Fiber Reinforced Concrete Overlays
Authors: Cho Sanghwan , Bordelon Amanda C. , Kim Min Ook
Published: August 2024

Macro fibers have been extensively used in the construction of various concrete structures, including bridges, dams, tunnels, industrial floors, and pavements. However, their effectiveness in reducing crack opening widths in concrete pavements has not been fully explored. This study aims to delineate the role of fibers by identifying the optimal types and volumes for effectively controlling cracks in concrete pavement structures, particularly in thin overlays. The research investigates how different fiber types, such as synthetic and steel, and their respective volumes can mitigate crack propagation in concrete overlays. Additionally, it evaluates the performance of fiber-reinforced concrete overlays compared to conventional dowel bar systems in terms of crack width reduction and overall pavement durability. The findings aim to provide specific design criteria for incorporating macro fibers in concrete overlays to enhance structural integrity and longevity.
Surface Development of Polyethylene Terephthalate Films Using Low-Pressure, High-Frequency Argon + Oxygen Plasma on Zinc Powder for Dye-Sensitized Solar Cells
Authors: Poonthong Wittawat , Mungkung Narong , Tunlasakun Khanchai , Thungsuk Nuttee , Kasayapanand Nat , Arunrungrusmi Somchai , Tanitteerapan Tanes , Maneepen Threerapong , Songruk Apidat , Yuji Toshifumi
Published: August 2024

This research has developed a process for producing ZnO thin film from DEZn deposited onto a PET substrate with low-pressure, high-frequency Ar + O2 plasma using a chemical vapor deposition technique. The aim is to study the film production conditions that affect electrical properties, optical properties, and thin film surfaces. This work highlights the use of plasma energy produced from a mixture of gases between Ar + O2. Plasma production is stimulated by an RF power supply to deliver high chemical energy and push ZnO atoms from the cathode inside the reactor onto the substrate through surface chemical reactions. The results showed that increasing the RF power in plasma production affected the chemical reactions on the substrate surface of film formations. Film preparation at an RF power of 300 W will result in the thickest films. The film has a continuous columnar formation, and the surface has a granular structure. This results in the lowest electrical resistivity of 1.8 × 10−4 Ω. In addition, when fabricated into a DSSC device, the device tested the PCE value and showed the highest value at 5.68%. The reason is due to the very rough surface nature of the ZnO film, which increases the scattering and storage of sunlight, making cells more efficient. Therefore, the benefit of this research is that it will be a highly efficient prototype of thin film production technology using a chemical process that reduces production costs and can be used in the industrial development of solar cells.
Encapsulation of Pink Pepper Essential Oil (Schinus terebinthifolius Raddi) in Albumin and Low-Methoxyl Amidated Pectin Cryogels
Authors: Chaux-Gutiérrez Ana María , Pérez-Monterroza Ezequiel José , Cattelan Marília Gonçalves , Nicoletti Vânia Regina , Moura Márcia Regina de
Published: August 2024

This study evaluated cryogels from albumin (ALB) and albumin–pectin (ALB:PEC) as carriers for pink pepper (Schinus terebinthifolius Raddi) essential oil. Cryogels were evaluated through infrared spectrophotometry, X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, and differential scanning calorimetry. The bioactivity of the cryogels was analyzed by measuring their encapsulation efficiency (EE%), the antimicrobial activity of the encapsulated oil against S. aureus, E. coli, and B. cereus using the agar diffusion method; total phenolic content and antioxidant activity were analyzed by UV-vis spectrophotometry. The EE% varied between 59.61% and 77.41%. The cryogel with only ALB had the highest total phenolic content with 2.802 mg GAE/g, while the cryogel with the 30:70 ratio (ALB:PEC) presented a value of 0.822 mg GAE/g. A higher proportion of PEC resulted in a more significant inhibitory activity against S. aureus, reaching an inhibition zone of 18.67 mm. The cryogels with ALB and 70:30 ratio (ALB:PEC) presented fusion endotherms at 137.16 °C and 134.15 °C, respectively, and semicrystalline structures. The interaction between ALB and PEC increased with their concentration, as evidenced by the decreased intensity of the O-H stretching peak, leading to lower encapsulation efficiency. The cryogels obtained can be considered a suitable matrix for encapsulating pink pepper oil.
Mechanical Characteristics and Miniaturization Design of the Electromagnetic Valve Used in Drilling Robots
Authors: Zhao Jianguo , Luo Lin , Zhuo Yun , Wang Minghua , He Chao , Zhang Chunliang , Xie Gang
Published: August 2024

The maximum radial size of the conventional three-bit four-way electromagnetic cartridge valve is greater than the largest containable size of the fluid control valve used for downhole robots. This paper proposes two kinds of solutions to reduce the radial dimension of the three-bit four-way electromagnetic insertion valve: one is to reduce the radial arrangement of the coil and increase the axial arrangement of the coil, and the other is to reduce the diameter of the moving iron core to reduce the winding radius of the coil. Using the theoretical model established in the following text, a simulation experiment was conducted. The results show that the movement of the valve spool is basically completed within 30 ms. Then, a matching experiment on the electromagnetic insertion valve was designed and conducted. The experimental results show that the opening time of the solenoid valve on the left coil is about 52 ms, and the opening time of the solenoid valve on the right coil is about 44 ms. The reaction time of the valve spool is suitable for the practical application of the solenoid valve. The significance of this paper is the reduced radial size of the three-bit four-way electromagnetic insertion valve. These improvements have reduced the size of downhole drilling robots, which facilitates the application of downhole drilling robots in narrower environments.
Mathematical Modeling for Predicting Growth and Yield of Halophyte Hedysarum scoparium in Arid Regions under Variable Irrigation and Soil Amendment Conditions
Authors: Azeem Ahmad , Mai Wenxuan
Published: August 2024

Growing degree days (GDDs) and leaf area index (LAI) greatly influence the growth and yield of many crops grown in arid regions. Therefore, variation in LAI due to GDD can provide a theoretical basis for predicting crop growth, water consumption, plant development, and yield in arid agriculture via the development of mathematical growth models. This study described the relationship between plant biomass production and variation in LAI due to GDD in arid regions under different types of irrigation (fresh water and saline water) and soils amended with different substances (manure+sandy soil, compost+sandy soil, clay+sandy soil, and sandy soil). Mathematical models for LAI were established for GDDs. In addition, different water quality irrigation techniques were used as independent variables to calculate the LAI of halophytic plants (Hedysarum scoparium) in arid regions under different soil amendment treatments. Furthermore, mathematical models for plant biomass production were developed by using the LAI and GDDs. For this purpose, Logistic, Gaussian, modified Gaussian, and Cubic polynomial models were used. Modified Gaussian and Cubic polynomial models are the best among all developed models, but Cubic polynomial models are more suitable among all developed models because of their simple quadratic equations that can be solved by using the first derivative. It was observed that with increased salt concentration in the irrigation water, the growth of per plant production decreased. However, soil amendments like manure and compost enhance salt tolerance against salt stress and enable plants to sustain their growth. Furthermore, Hedysarum scoparium attains maximum LAI when its GDD is about 1117.5 °C under both irrigation regimes and in all soil amendment treatments. It was concluded that these predicted mathematical models can provide crucial insights for enhancing production in arid regions by using eco-friendly soil amendments to improve water use efficiency across diverse types of water irrigation.
Long-Term Sea Level Periodicities over the European Seas from Altimetry and Tide Gauge Data
Authors: Zid Ferdous , Vigo Maria Isabel , Vargas-Alemañy Juan A. , García-García David
Published: August 2024

This study investigates the long-term temporal patterns of sea level changes by analyzing monthly tide gauge data from 1950 to 2022 (42 to 72 years) along the European coastline and monthly altimetry data from 1992 to 2024 in the surrounding European seas. The primary focus is on signals with periods longer than 5 years. The application of wavelet-based approaches and multiresolution analysis has enabled the isolation of signals with periods of approximately 8 and 16 years. However, the latter has only been observed in tide gauge data, as the altimetry time series is not sufficiently long. The same analysis was applied to the North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) indices, which enabled the detection of the same signals. The reported multiyear signals of sea level are correlated with NAO and AMO indices, particularly during the period spanning from 1975 to 2010.
Pansharpening Based on Multimodal Texture Correction and Adaptive Edge Detail Fusion
Authors: Liu Danfeng , Wang Enyuan , Wang Liguo , Benediktsson Jón Atli , Wang Jianyu , Deng Lei
Published: August 2024

Pansharpening refers to the process of fusing multispectral (MS) images with panchromatic (PAN) images to obtain high-resolution multispectral (HRMS) images. However, due to the low correlation and similarity between MS and PAN images, as well as inaccuracies in spatial information injection, HRMS images often suffer from significant spectral and spatial distortions. To address these issues, a pansharpening method based on multimodal texture correction and adaptive edge detail fusion is proposed in this paper. To obtain a texture-corrected (TC) image that is highly correlated and similar to the MS image, the target-adaptive CNN-based pansharpening (A-PNN) method is introduced. By constructing a multimodal texture correction model, intensity, gradient, and A-PNN-based deep plug-and-play correction constraints are established between the TC and source images. Additionally, an adaptive degradation filter algorithm is proposed to ensure the accuracy of these constraints. Since the TC image obtained can effectively replace the PAN image and considering that the MS image contains valuable spatial information, an adaptive edge detail fusion algorithm is also proposed. This algorithm adaptively extracts detailed information from the TC and MS images to apply edge protection. Given the limited spatial information in the MS image, its spatial information is proportionally enhanced before the adaptive fusion. The fused spatial information is then injected into the upsampled multispectral (UPMS) image to produce the final HRMS image. Extensive experimental results demonstrated that compared with other methods, the proposed algorithm achieved superior results in terms of both subjective visual effects and objective evaluation metrics.
Application of Getis-Ord Correlation Index (Gi) for Burned Area Detection Improvement in Mediterranean Ecosystems (Southern Italy and Sardinia) Using Sentinel-2 Data
Authors: Lanorte Antonio , Nolè Gabriele , Cillis Giuseppe
Published: August 2024

This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities.
Modeling of Explosive Pingo-like Structures and Fluid-Dynamic Processes in the Arctic Permafrost: Workflow Based on Integrated Geophysical, Geocryological, and Analytical Data
Authors: Buddo Igor , Misyurkeeva Natalya , Shelokhov Ivan , Shein Alexandr , Sankov Vladimir , Rybchenko Artem , Dobrynina Anna , Nezhdanov Alexey , Parfeevets Anna , Lebedeva Marina , Kadetova Alena , Smirnov Alexander , Gutareva Oxana , Chernikh Alexey , Shashkeeva Lyubov , Kraev Gleb
Published: August 2024

Understanding the mechanisms responsible for the origin, evolution, and failure of pingos with explosive gas emissions and the formation of craters in the Arctic permafrost requires comprehensive studies in the context of fluid dynamic processes. Properly choosing modeling methods for the joint interpretation of geophysical results and analytical data on core samples from suitable sites are prerequisites for predicting pending pingo failure hazards. We suggest an optimal theoretically grounded workflow for such studies, in a site where pingo collapse induced gas blowout and crater formation in the Yamal Peninsula. The site was chosen with reference to the classification of periglacial landforms and their relation to the local deformation pattern, according to deciphered satellite images and reconnaissance geophysical surveys. The deciphered satellite images and combined geophysical data from the site reveal a pattern of periglacial landforms matching the structural framework with uplifted stable permafrost blocks (polygons) bounded by eroded fractured zones (lineaments). Greater percentages of landforms associated with permafrost degradation fall within the lineaments. Resistivity anomalies beneath pingo-like mounds presumably trace deeply rooted fluid conduits. This distribution can be explained in terms of fluid dynamics. N–E and W–E faults, and especially their junctions with N–W structures, are potentially the most widely open conduits for gas and water which migrate into shallow sediments in the modern stress field of N–S (or rather NEN) extension and cause a warming effect on permafrost. The results obtained with a new workflow and joint interpretation of remote sensing, geophysical, and analytical data from the site of explosive gas emission in the Yamal Peninsula confirm the advantages of the suggested approach and its applicability for future integrated fluid dynamics research.
Radiation Anomaly Detection of Sub-Band Optical Remote Sensing Images Based on Multiscale Deep Dynamic Fusion and Adaptive Optimization
Authors: Ci Jinlong , Tan Hai , Zhai Haoran , Tang Xinming
Published: August 2024

Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models.
Limited Sample Radar HRRP Recognition Using FWA-GAN
Authors: Song Yiheng , Zhang Liang , Wang Yanhua
Published: August 2024

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets.
Design of a Gaze-Controlled Interactive Art System for the Elderly to Enjoy Life
Authors: Wang Chao-Ming , Hsu Wei-Chih
Published: August 2024

The impact of global population aging on older adults’ health and emotional well-being is examined in this study, emphasizing innovative technological solutions to address their diverse needs. Changes in physical and mental functions due to aging, along with emotional challenges that necessitate attention, are highlighted. Gaze estimation and interactive art are utilized to develop an interactive system tailored for elderly users, where interaction is simplified through eye movements to reduce technological barriers and provide a soothing art experience. By employing multi-sensory stimulation, the system aims to evoke positive emotions and facilitate meaningful activities, promoting active aging. Named “Natural Rhythm through Eyes”, it allows for users to interact with nature-themed environments via eye movements. User feedback via questionnaires and expert interviews was collected during public demonstrations in elderly settings to validate the system’s effectiveness in providing usability, pleasure, and interactive experience for the elderly. Key findings include the following: (1) Enhanced usability of the gaze estimation interface for elderly users. (2) Increased enjoyment and engagement through nature-themed interactive art. (3) Positive influence on active aging through the integration of gaze estimation and interactive art. These findings underscore technology’s potential to enhance well-being and quality of life for older adults navigating aging challenges.
Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis
Authors: Que Hongbo , Liu Xuyan , Jin Siqin , Huo Yaoyan , Wu Chengpan , Ding Chuancang , Zhu Zhongkui
Published: August 2024

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.
Precoder Design for Network Massive MIMO Optical Wireless Communications
Authors: Ali Zakir , Sun Chen , Jan Qasim , Furqan Muhammad , Gao Xiqi
Published: August 2024

Precoding is a technique employed to enhance transmission rates in various communication systems, including massive multiple-input multiple-output (MIMO) and optical wireless communication (OWC). In this study, we focus on network massive MIMO OWC (NM-MIMO-OWC) systems and investigate the precoder design to enhance the sum rate and improve the system performance. We present the network’s massive MIMO OWC framework. By utilizing this framework, we are able to calculate the achievable sum rate. Subsequently, we consider the precoding design for maximizing the sum rate while adhering to the total power constraint. To solve this optimization problem, we provide a necessary condition of the optimal solution based on the Karush–Kuhn–Tucker (KKT) conditions, and propose a low-complexity algorithm to further enhance the efficiency of the proposed precoding technique. The numerical results demonstrate that the proposed precoder design significantly improves the transmission rate and effectively maximizes the sum rate.
Using Flexible-Printed Piezoelectric Sensor Arrays to Measure Plantar Pressure during Walking for Sarcopenia Screening
Authors: Han Shulang , Xiao Qing , Liang Ying , Chen Yu , Yan Fei , Chen Hui , Yue Jirong , Tian Xiaobao , Xiong Yan
Published: August 2024

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly.
Novel Optical Modulator Photonic Device Based on TiN/Ti3C2 Heterojunction
Authors: Zhou Zexin , Yan Miao , Liang Hu , Yu Jie , Liu Qidong , Song Yufeng , Ji Jianhua , Wang Zhenhong , Wang Ke
Published: August 2024

Due to the ability of optical modulators to achieve rapid modulation of optical signals, meeting the demands of high-speed data transmission, modulators based on different novel nanomaterials have become one of the research hotspots over the past dacade. Recently, TiN/Ti3C2 heterojunction exhibits highly efficient thermo-optic performance and extremely strong stability. Therefore, we have demonstrated an all-optical modulator based on the principle of Michelson interference and the thermo-optic effect in this paper. The modulator employs a TiN/Ti3C2 heterojunction-coated microfiber (THM) and further demonstrates its ability to generate phase shifts through an ASE light source. The modulator, with a phase shift slope of 0.025π/mW, can also convert the phase shifts of signal light into amplitude modulation through Michelson interference. The fixed signal light wavelength is 1552.09 nm, and the modulation depth is stable at about 26.4 dB within a wavelength detuning range of −10 to 6 nm; The waveforms of signal light at modulation rates of 500 Hz, 1000 Hz, 2000 Hz, and 3000 Hz were tested, and a 3 dB modulation bandwidth of 2 kHz was measured. The all-optical modulator based on THM has the advantages of high efficiency and stability and has broad application prospects in the fields of all-optical signal processing and high-speed optical communication.
Leveraging Deep Learning for Time-Series Extrinsic Regression in Predicting the Photometric Metallicity of Fundamental-Mode RR Lyrae Stars
Authors: Monti Lorenzo , Muraveva Tatiana , Clementini Gisella , Garofalo Alessia
Published: August 2024

Astronomy is entering an unprecedented era of big-data science, driven by missions like the ESA’s Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia’s vast dataset presents a monumental challenge for traditional analysis methods. The sheer scale of this data exceeds the capabilities of manual exploration, necessitating the utilization of advanced computational techniques. In response to this challenge, we developed a novel approach leveraging deep learning to estimate the metallicity of fundamental mode (ab-type) RR Lyrae stars from their light curves in the Gaia optical G-band. Our study explores applying deep-learning techniques, particularly advanced neural-network architectures, in predicting photometric metallicity from time-series data. Our deep-learning models demonstrated notable predictive performance, with a low mean absolute error (MAE) of 0.0565, the root mean square error (RMSE) of 0.0765, and a high R2 regression performance of 0.9401, measured by cross-validation. The weighted mean absolute error (wMAE) is 0.0563, while the weighted root mean square error (wRMSE) is 0.0763. These results showcase the effectiveness of our approach in accurately estimating metallicity values. Our work underscores the importance of deep learning in astronomical research, particularly with large datasets from missions like Gaia. By harnessing the power of deep-learning methods, we can provide precision in analyzing vast datasets, contributing to more precise and comprehensive insights into complex astronomical phenomena.
Explainable Feature Engineering for Multi-Modal Tissue State Monitoring Based on Impedance Spectroscopy
Authors: Guermazi Mahdi , Kallel Ahmed Yahia , Kanoun Olfa
Published: August 2024

One of the most promising approaches to food quality assessments is the use of impedance spectroscopy combined with machine learning. Thereby, feature selection is decisive for a high classification accuracy. Physically based features have particularly significant advantages because they are able to consider prior knowledge and to concentrate the data into pertinent understandable information, building a solid basis for classification. In this study, we aim to identify physically based measurable features for muscle type and freshness classifications of bovine meat based on impedance spectroscopy measurements. We carry out a combined study where features are ranked based on their F1-score, cumulative feature selection, and t-distributed Stochastic Neighbor Embedding (t-SNE). In terms of features, we analyze the characteristic points (CPs) of the impedance spectrum and the model parameters (MPs) obtained by fitting a physical model to the measurements. The results show that either MPs or CPs alone are sufficient for detecting muscle type. Combining capacitance (C) and extracellular resistance (Rex) or the modulus of the characteristic point Z1 and the phase at the characteristic frequency of the beta dispersion (Phi2) leads to accurate separation. In contrast, the detection of freshness is more challenging. It requires more distinct features. We achieved a 90% freshness separation using the MPs describing intracellular resistance (Rin) and capacitance (C). A 95.5% freshness separation was achieved by considering the phase at the end of the beta dispersion (Phi3) and Rin. Including additional features related to muscle type improves the separability of samples; ultimately, a 99.6% separation can be achieved by selecting the appropriate features.
No-Reference Image Quality Assessment Combining Swin-Transformer and Natural Scene Statistics
Authors: Yang Yuxuan , Lei Zhichun , Li Changlu
Published: August 2024

No-reference image quality assessment aims to evaluate image quality based on human subjective perceptions. Current methods face challenges with insufficient ability to focus on global and local information simultaneously and information loss due to image resizing. To address these issues, we propose a model that combines Swin-Transformer and natural scene statistics. The model utilizes Swin-Transformer to extract multi-scale features and incorporates a feature enhancement module and deformable convolution to improve feature representation, adapting better to structural variations in images, apply dual-branch attention to focus on key areas, and align the assessment more closely with human visual perception. The Natural Scene Statistics compensates information loss caused by image resizing. Additionally, we use a normalized loss function to accelerate model convergence and enhance stability. We evaluate our model on six standard image quality assessment datasets (both synthetic and authentic), and show that our model achieves advanced results across multiple datasets. Compared to the advanced DACNN method, our model achieved Spearman rank correlation coefficients of 0.922 and 0.923 on the KADID and KonIQ datasets, respectively, representing improvements of 1.9% and 2.4% over this method. It demonstrated outstanding performance in handling both synthetic and authentic scenes.
Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals
Authors: Park Yeonjae , Park You Hyun , Jeong Hoyeon , Kim Kise , Jung Ji Ye , Kim Jin-Bae , Kang Dae Ryong
Published: August 2024

Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.
Correction: Aasan et al. The Relative Importance of Family, School, and Leisure Activities for the Mental Wellbeing of Adolescents: The Young-HUNT Study in Norway. Societies 2023, 13, 93
Authors: Aasan Bodil Elisabeth Valstad , Lillefjell Monica , Krokstad Steinar , Sylte Mari , Sund Erik Reidar
Published: August 2024

In the original publication [...]
“These Researchers Think They Come From Heaven with Analytical Superpowers When They Don’t”: A Qualitative Analysis of Research Experiences in Intersex-Related Studies
Authors: Mestre-Martínez Yessica
Published: August 2024

This article explores diverse ethical considerations related to the study of vulnerable population groups to produce meaningful project outcomes that avoid harm to the involved communities. In the specific context of the intersex community, previous analyses have encountered various challenges, including the medicalization of intersex people’s bodies, the use of pathologizing language, and misrepresentation of the population’s needs. The article explores some of the beliefs, experiences, and tools that experts in research ethics, researchers, and intersex research participants consider most important regarding research ethics in intersex-related studies. The article is based on original empirical research; semi-structured interviews were conducted with 21 participants, recruited through email and snowball sampling methods, and the data were examined using thematic analysis. It moves from issues related to the research design (positionality, researcher preparation, framing intersex within the LGBT acronym) to experiences related to the research development (ethical approval, informed consent process, language use, relationship of trust). The article describes some of the major concerns and raises discussions that could be relevant for the development of future research under human rights-based perspectives. The findings are aimed to be useful beyond the field of intersex-related studies, as they can be relevant to research about other communities that have endured violations of their human rights during research.
Research Trends on Valorisation of Agricultural Waste Discharged from Production of Distilled Beverages and Their Implications for a “Three-Level Valorisation System”
Authors: Stewart Kelly , Willoughby Nik , Zhuang Shiwen
Published: August 2024

The circular economy, driven by waste elimination, material circulation and nature regeneration, is crucial for business, people, and the environment. With the increasing demand for distilled beverages, managing agricultural waste like spent grains is paramount. While previous studies focused on individual beverages, investigating technologies across different types of beverages has been overlooked. This paper provides a systematic review of agricultural waste valorisation over the past five years, focusing on four representative distilled beverages: whisk(e)y, tequila, baijiu and shochu. Research efforts have primarily focused on bioenergy production from whisk(e)y and tequila waste, whereas extracting functional substances is common for baijiu and shochu. Through integrating different technologies, a “Three-level Valorisation System” was proposed to enhance the translation of agricultural waste into value-added products like proteins. This system is directly relevant to the distilled beverage industry globally and applicable to associated industries such as biofuel and food production.
Optimal Rule-Interposing Reinforcement Learning-Based Energy Management of Series—Parallel-Connected Hybrid Electric Vehicles
Authors: Dai Lihong , Hu Peng , Wang Tianyou , Bian Guosheng , Liu Haoye
Published: August 2024

P2–P3 series–parallel hybrid electric vehicles exhibit complex configurations with multiple power sources and operational modes, presenting a difficulty in developing efficient energy management strategies. This paper takes a P2–P3 series–parallel hybrid power system-KunTye 2DHT system as the research object and proposes a deep reinforcement learning framework based on pre-optimized energy management to improve the energy consumption performance of the hybrid electric vehicles. Firstly, a control-oriented model is established based on its system configuration and characteristics. Then, the optimal distribution of the motor energy under different operating modes is pre-optimized, which aims to reduce the energy management task’s dimensionality by equating two motors as an equivalent motor. Subsequently, based on real-time traffic information under connected conditions, deep reinforcement learning is utilized to optimize the optimal operating modes of the hybrid system and the optimal distribution between the engine and equivalent motors. Combining the pre-optimized results, the optimal energy distribution between the engine and the two motors in the system is achieved. Finally, performance comparisons are made between the predictive control and the traditional Dynamic Programming and Adaptive Equivalent Consumption Minimization Strategy, revealing the proposed optimization algorithm’s promising potential in reducing fuel consumption.
Do Agricultural Productive Services Impact the Carbon Emissions of the Planting Industry in China: Promotion or Inhibition?
Authors: Wu Beihe , Guo Yan , Chen Zhaojiu , Wang Liguo
Published: August 2024

Reducing carbon emissions from planting (PCE) is crucial for achieving the “dual carbon” objective within the agricultural sector. This paper aims to investigate the impact of agricultural productive services (APSs) on carbon emissions in the planting industry, providing novel insights into reducing carbon emissions in this field. Based on the panel data of 30 provinces and regions in China from 2005 to 2021, this study measured the PCE and the level of APSs. The internal relationship between the APSs and PCE is subsequently examined empirically using various statistical models, including the fixed effect model, instrumental variable model, mediating effect model, and threshold effect model. The findings suggest that the PCE experienced an initial increase, followed by a subsequent decrease between 2005 and 2021, with the peak observed in 2015. APSs has a significant inhibitory effect on PCE, which remains significant even after conducting various robustness tests. However, the carbon emission reduction effect of APSs varies across different crop-producing areas. The study also revealed that APSs can inhibit PCE by facilitating land transfer. Additionally, the inhibitory effect of APSs on PCE is influenced by the scale of land management and exhibits a U-shaped nonlinear relationship. To effectively reduce PCE and achieve sustainable agricultural development, policymakers should actively promote the integration of APSs with modern agriculture and form moderate-scale operations by facilitating land transfer to maximize APSs’ carbon emission reduction effect.
Differential Pricing Strategies for Airport Shuttles: A Study of Shanghai Based on Customized Bus Ticketing Data
Authors: Yu Siyuan , Xu Chenlong , Zhai Zhikang , Zheng Yuefeng , Shen Yu
Published: August 2024

Airport shuttle buses, as a specialized form of bus service, serve as an economical, efficient, and sustainable transportation option for air travelers. In contrast to conventional bus services, airport shuttle bus operations exhibit more pronounced market-oriented characteristics, striving to balance extensive public transport coverage with the optimization of corporate profitability. Although these services outperform regular bus transit in terms of efficiency, they incur higher operational costs. However, existing studies on enhancing profitability and optimizing resource allocation for airport shuttle buses are inadequate. This study proposes a differential pricing strategy based on historical ticketing data. Initially, we analyze the characteristics of orders, users, and reservations within the context of customized bus operations. Leveraging the differences among various groups, we employ clustering techniques to classify seat grades and segment users. Based on the clustering outcomes, we determine distinct price elasticity values for each segment. As the strategies are developed based on seat grades, booking time, and user travel patterns, the numerical experiments indicate that the proposed differentiated pricing strategy can increase the revenue of customized public transport services by at least 41%. This strategy not only enhances the efficiency of resource allocation and service accessibility but also makes the service more financially sustainable.
Strong Decays of the ϕ(2170) as a Fully Strange Tetraquark State
Authors: Jiang Yi-Wei , Tan Wei-Han , Chen Hua-Xing , Cui Er-Liang
Published: August 2024

We study the strong decays of the ϕ(2170), along with its possible partner X(2436), as two fully strange tetraquark states of JPC=1−−. These two states are assumed to contain two strange quarks and two anti-strange quarks, with the flavor symmetry 6ss⊗6¯s¯s¯. We consider seven decay channels: ϕη, ϕη′, ϕf0(980), ϕf1(1420), h1(1415)η, h1(1415)η′, and h1(1415)f1(1420). Some of these channels are kinematically possible, and we calculate their relative branching ratios through the Fierz rearrangement. Future experimental measurements on these ratios could be useful in determining the nature of the ϕ(2170) and X(2436). The ϕ(2170) has been observed in the ϕf0(980), ϕη, and ϕη′ channels, and we propose to further examine it in the h1(1415)η channel. Evidences of the X(2436) have been observed in the ϕf0(980) channel, and we propose to verify whether this structure exists or not in the ϕη, ϕη′, h1(1415)η, and h1(1415)η′ channels.