bluuebunny/crossref_metadata_2025 huggingface dataset:
Time–Temperature Superposition of the Dissolution of Wool Yarns in the Ionic Liquid 1-Ethyl-3-methylimidazolium Acetate
Authors: Alghamdi Amjad Safar , Hine Peter John , Ries Michael Edward
Published: January 2024

The dissolution of wool yarns in the ionic liquid 1-ethyl-3-methyl-imidazolium acetate [C2mim][OAc] has been investigated. Wool yarns were submerged into [C2mim][OAc] and dissolved for various times and temperatures before coagulating with water. Optical microscopy was used to track the yarn’s cross-sectional area. We propose that there are two competing dissolution processes, one rate-limited by disulfide bonds at low temperatures (LTs), and a second by hydrogen bonds at high temperatures (HTs), with a crossover point between the two regimes at 70 ℃. The corresponding activation energies were ELT = 127 ± 9 kJ/mol and EHT = 34 ± 1 kJ/mol. The remaining area of the dissolved wool yarn could be shifted via time–temperature superposition to plot a single master curve of area against time for both regions. Finally, the dissolution could be modelled by a diffusion process, giving self-diffusion coefficients for the [C2mim][OAc] ions (0.64–15.31 × 10−13 m2/s).
Origin of Redbeds in the Neoproterozoic Socheong Formation and Their Relation to the Dashigou Large Igneous Province
Authors: Yun Hawon , Lee Seung Hwan , Seo Inah
Published: January 2024

During the latest Mesoproterozoic–Early Neoproterozoic era, extensional regimes generated a number of sedimentary basins in various regions in the Sino-Korean Craton. Mantle-plume emplacements are widely recognized in the sedimentary strata as mafic dikes and sills of the Dashigou Large Igneous Province (LIP). The occurrence of Fe-rich redbeds is first reported in the Neoproterozoic Socheong Formation of the Sangwon Supergroup in the Pyeongnam Basin. Their geochemical and mineralogical characteristics indicate basin-wide Fe enrichment due to hydrothermal fluid input. The episodic yet repetitive hydrothermal injection into the basin generated short-lived anoxia, recorded as greenish-gray coloration in the ferruginous beds. This hydrothermal fluid was likely sourced from the mafic igneous activities involved in the Dashigou LIP. The redbeds can be utilized as key beds for intra- or inter-basinal stratigraphic correlation and to study the negative carbon isotope excursions that occurred in the genetically related basins in the region (e.g., the Sangwon, Xu-Huai, and Dalian basins).
Dietary Macronutrient Intake and Cardiovascular Disease Risk and Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies
Authors: Ma Yibin , Zheng Zekun , Zhuang Litao , Wang Huiting , Li Anni , Chen Liangkai , Liu Liegang
Published: January 2024

Many epidemiological studies have evaluated the intake of macronutrients and the risk of mortality and cardiovascular disease (CVD). However, current evidence is conflicting and warrants further investigation. Therefore, we carried out an umbrella review to examine and quantify the potential dose-response association of dietary macronutrient intake with CVD morbidity and mortality. Prospective cohort studies from PubMed, Embase, and CENTRAL were reviewed, which reported associations of macronutrients (protein, fat, and carbohydrate) with all-cause, CVD, cancer mortality, or CVD events. Multivariable relative risks (RR) were pooled, and heterogeneity was assessed. The results of 124 prospective cohort studies were included in the systematic review and 101 in the meta-analysis. During the follow-up period from 2.2 to 30 years, 506,086 deaths and 79,585 CVD events occurred among 5,107,821 participants. High total protein intake was associated with low CVD morbidity (RR 0.88, 95% confidence interval 0.82–0.94), while high total carbohydrate intake was associated with high CVD morbidity (1.08, 1.02–1.13). For fats, a high intake of total fat was associated with a decreased all-cause mortality risk (0.92, 0.85–0.99). Saturated fatty acid intake was only associated with cancer mortality (1.10, 1.06–1.14); Both monounsaturated fatty acid (MUFA) and polyunsaturated fatty acids (PUFA) intake was associated with all-cause mortality (MUFA: 0.92, 0.86–0.98; PUFA: 0.91, 0.86–0.96). This meta-analysis supports that protein intake is associated with a decreased risk of CVD morbidity, while carbohydrate intake is associated with an increased risk of CVD morbidity. High total fat intake is associated with a low risk of all-cause mortality, and this effect was different in an analysis stratified by the type of fat.
Yohimbine Ingestion Mitigates Morning-Associated Decrements in High-Intensity Exercise Performance
Authors: Ballmann Christopher G. , Rogers Rebecca R. , Barnes Megan E. , Cowan Camryn R. , Elwell Carson C. , Luiken Kailey A. , Lehman Grace Y. , Kaylor Julia C. , Simpson Ella G. , Westbrooks Spencer B. , Miller Maria J. , Benjamin Courteney L. , Williams Tyler D.
Published: January 2024

Exercise performance tends to suffer during the morning compared to the evening, which may decrease potential training adaptations. Currently, it is unclear how nutritional interventions may affect this phenomenon and whether supplementation may allow for the attainment of optimal performance regardless of the time of day. The purpose of this study was to investigate the effects of acute yohimbine ingestion on morning-associated decrements in performance and psychophysiological responses to exercise. Physically active females (n = 16) were recruited to participate in three total visits, each with a different treatment: (1) placebo-morning (PL-AM), (2) yohimbine-morning (YHM-AM; oral 2.5 mg), and (3) placebo-afternoon (PM). The morning and afternoon visits occurred between 7:00–8:00 h and 16:00–17:00 h, respectively. The experimental treatments in the morning were ingested 20 min prior to capillary blood collection, which was completed pre- and post-exercise. Following a warm-up, participants completed a 2000 m time trial on a rowing ergometer. Power output, heart rate (HR), and rating of perceived exertion (RPE) were recorded every minute. Time to competition (TTC) and subjective energy, focus, and alertness were documented post-exercise. Pre- and post-exercise blood lactate (La) and plasma hypoxanthine (HX) levels were also assessed. The trials were separated by a 48 h washout period. The results showed that power output (p = 0.010) was lower and TTC (p = 0.003) was significantly slower with PL-AM compared to PM. Furthermore, YHM-AM resulted in higher power output (p = 0.035) and faster TTC (p = 0.007) compared to PL-AM, with no differences compared to PM (p > 0.05). Post-exercise La was significantly lower with YHM-AM compared to PL-AM (p = 0.046) and PM (p = 0.001). Pre-exercise plasma HX, as measured via conversion to xanthine, was significantly higher with PM (p = 0.039), while the levels trended higher with YHM-AM (p = 0.060) compared to PL-AM. Subjective energy was higher with YHM-AM (p = 0.045) and PM (p = 0.009) compared to PL-AM, while alertness was only higher for YHM-AM compared to PL-AM (p = 0.045). No statistical differences between the treatments were found for RPE or HR (p > 0.05). These findings indicate that YHM ingestion attenuates performance decrements in the morning. Improvements in performance may be underpinned by improved feelings of energy and alterations in metabolism. Practically, YHM may represent an effective ergogenic aid to combat a lack of energy and low performance during the morning.
Immune Activation by a Nutraceutical Blend: Rapid Increase in Immune-Modulating Cytokines, Followed by Induction of Anti-Inflammatory and Restorative Biomarkers
Authors: McGarry Sage V. , Yu Liu , Cruickshank Dina , Iloba Ifeanyi , Jensen Gitte S.
Published: January 2024

Immune cells express Pattern Recognition Receptors (PRRs) to recognize potentially pathogenic microbial forms. Nutraceutical compounds can induce immune cell activation through PRRs. The nutraceutical immune blend (IB), QuickStart™, contains botanical and yeast-derived ligands for PRRs, along with vitamin C and zinc. We evaluated immune-activating effects of the IB and its ingredients in vitro. Human peripheral blood mononuclear cells were treated with either the IB or single ingredients: elderberry extract, the proprietary Saccharomyces cerevisiae fermentate EpiCor™ (Sacc), the plant-based hemicellulose preparation Natramune (PDS-2865)™ (Hemi), vitamin C (VitC), or zinc gluconate (Zinc). The IB triggered sequential waves of immune activation. Initial cytokine induction by the IB at 2 h involved the immune-activating cytokines IL-6, IL-8, MIP-1α, and TNF-α, and the stem cell-mobilizing growth factor G-CSF, as did Sacc and Hemi. The 24 h immune-activation by the IB included increases in IL-1β, IL-17A, IP-10, GM-CSF, Basis FGF, PDGF-BB, and the anti-inflammatory cytokine IL-10. Increased CD69 expression by the IB was also seen for VitC and Sacc. Increased CD25 expression by the IB on monocytes was also seen for Sacc. The IB triggered rapid immune activating events of higher magnitude than the single ingredients, involving immune-activating cytokines and restorative growth factors. Clinical research is warranted to evaluate rapid immune-modulating events upon consumption.
GNSS Carrier-Phase Multipath Modeling and Correction: A Review and Prospect of Data Processing Methods
Authors: Zhang Qiuzhao , Zhang Longqiang , Sun Ao , Meng Xiaolin , Zhao Dongsheng , Hancock Craig
Published: January 2024

A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation.
Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands
Authors: Wang Jingxue , Zang Dongdong , Yu Jinzheng , Xie Xiao
Published: January 2024

Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.
Star Image Registration Modeling and Parameter Calibration for a 3CMOS Star Sensor
Authors: Niu Yanzhao , Wei Xinguo , Li Jian
Published: January 2024

This paper presents an image registration method specifically designed for a star sensor equipped with three complementary metal oxide semiconductor (CMOS) detectors. Its purpose is to register the red-, green-, and blue-channel star images acquired from three CMOS detectors, assuring the precision of star image fusion and centroid extraction in subsequent stages. This study starts with a theoretical analysis aimed at investigating the effect of inconsistent three-channel imaging parameters on the position of feature points. Based on this analysis, this paper establishes a registration model for transforming the red- and blue-channel star images into the green channel’s coordinate system. Subsequently, the method estimates model parameters by finding a nonlinear least-squares solution. The experimental results demonstrate the correctness of the theoretical analysis and the proposed registration method. This method can achieve subpixel alignment accuracy in both the x and y directions, thus effectively ensuring the performance of subsequent operation steps in the 3CMOS star sensor.
Improvement in Multi-Angle Plane Wave Image Quality Using Minimum Variance Beamforming with Adaptive Signal Coherence
Authors: Shen Che-Chou , Huang Chun-Lin
Published: January 2024

For ultrasound multi-angle plane wave (PW) imaging, the coherent PW compounding (CPWC) method provides limited image quality because of its conventional delay-and-sum beamforming. The delay-multiply-and-sum (DMAS) method is a coherence-based algorithm that improves image quality by introducing signal coherence among either receiving channels or PW transmit angles into the image output. The degree of signal coherence in DMAS is conventionally a global value for the entire image and thus the image resolution and contrast in the target region improves at the cost of speckle quality in the background region. In this study, the adaptive DMAS (ADMAS) is proposed such that the degree of signal coherence relies on the local characteristics of the image region to maintain the background speckle quality and the corresponding contrast-to-noise ratio (CNR). Subsequently, the ADMAS algorithm is further combined with minimum variance (MV) beamforming to increase the image resolution. The optimal MV estimation is determined to be in the direction of the PW transmit angle (Tx) for multi-angle PW imaging. Our results show that, using the PICMUS dataset, TxMV-ADMAS beamforming significantly improves the image quality compared with CPWC. When the p value is globally fixed to 2 as in conventional DMAS, though the main-lobe width and the image contrast in the experiments improve from 0.57 mm and 27.0 dB in CPWC, respectively, to 0.24 mm and 38.0 dB, the corresponding CNR decreases from 12.8 to 11.3 due to the degraded speckle quality. With the proposed ADMAS algorithm, however, the adaptive p value in DMAS beamforming helps to restore the CNR value to the same level of CPWC while the improvement in image resolution and contrast remains evident.
Time Convolutional Network-Based Maneuvering Target Tracking with Azimuth–Doppler Measurement
Authors: Huang Jianjun , Hu Haoqiang , Kang Li
Published: January 2024

In the field of maneuvering target tracking, the combined observations of azimuth and Doppler may cause weak observation or non-observation in the application of traditional target-tracking algorithms. Additionally, traditional target tracking algorithms require pre-defined multiple mathematical models to accurately capture the complex motion states of targets, while model mismatch and unavoidable measurement noise lead to significant errors in target state prediction. To address those above challenges, in recent years, the target tracking algorithms based on neural networks, such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer architectures, have been widely used for their unique advantages to achieve accurate predictions. To better model the nonlinear relationship between the observation time series and the target state time series, as well as the contextual relationship among time series points, we present a deep learning algorithm called recursive downsample–convolve–interact neural network (RDCINN) based on convolutional neural network (CNN) that downsamples time series into subsequences and extracts multi-resolution features to enable the modeling of complex relationships between time series, which overcomes the shortcomings of traditional target tracking algorithms in using observation information inefficiently due to weak observation or non-observation. The experimental results show that our algorithm outperforms other existing algorithms in the scenario of strong maneuvering target tracking with the combined observations of azimuth and Doppler.
PVS-GEN: Systematic Approach for Universal Synthetic Data Generation Involving Parameterization, Verification, and Segmentation
Authors: Kim Kyung-Min , Kwak Jong Wook
Published: January 2024

Synthetic data generation addresses the challenges of obtaining extensive empirical datasets, offering benefits such as cost-effectiveness, time efficiency, and robust model development. Nonetheless, synthetic data-generation methodologies still encounter significant difficulties, including a lack of standardized metrics for modeling different data types and comparing generated results. This study introduces PVS-GEN, an automated, general-purpose process for synthetic data generation and verification. The PVS-GEN method parameterizes time-series data with minimal human intervention and verifies model construction using a specific metric derived from extracted parameters. For complex data, the process iteratively segments the empirical dataset until an extracted parameter can reproduce synthetic data that reflects the empirical characteristics, irrespective of the sensor data type. Moreover, we introduce the PoR metric to quantify the quality of the generated data by evaluating its time-series characteristics. Consequently, the proposed method can automatically generate diverse time-series data that covers a wide range of sensor types. We compared PVS-GEN with existing synthetic data-generation methodologies, and PVS-GEN demonstrated a superior performance. It generated data with a similarity of up to 37.1% across multiple data types and by 19.6% on average using the proposed metric, irrespective of the data type.
Susceptibility of the Different Oxygen-Sensing Probes to Interferences in Respirometric Bacterial Assays with Complex Media
Authors: Zanetti Chiara , Li Liang , Gaspar Rafael Di Lazaro , Santovito Elisa , Elisseeva Sophia , Collins Stuart G. , Maguire Anita R. , Papkovsky Dmitri B.
Published: January 2024

Respirometric microbial assays are gaining popularity, but their uptake is limited by the availability of optimal O2 sensing materials and the challenge of validating assays with complex real samples. We conducted a comparative evaluation of four different O2-sensing probes based on Pt-porphyrin phosphors in respirometric bacterial assays performed on standard time-resolved fluorescence reader. The macromolecular MitoXpress, nanoparticle NanO2 and small molecule PtGlc4 and PtPEG4 probes were assessed with E. coli cells in five growth media: nutrient broth (NB), McConkey (MC), Rapid Coliform ChromoSelect (RCC), M-Lauryl lauryl sulfate (MLS), and Minerals-Modified Glutamate (MMG) media. Respiration profiles of the cells were recorded and analyzed, along with densitometry profiles and quenching studies of individual media components. This revealed several limiting factors and interferences impacting assay performance, which include probe quenched lifetime, instrument temporal resolution, inner filter effects (mainly by indicator dyes), probe binding to lipophilic components, and dynamic and static quenching by media components. The study allowed for the ranking of the probes based on their ruggedness, resilience to interferences and overall performance in respirometric bacterial assays. The ‘shielded’ probe NanO2 outperformed the established MitoXpress probe and the small molecule probes PtGlc4 and PtPEG4.
A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images
Authors: Nan Guojun , Li Haorui , Du Haibo , Liu Zhuo , Wang Min , Xu Shuiqing
Published: January 2024

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet.
Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s
Authors: Lee Ji-Yong , Kwon Kihyeon , Kim Changgyun , Youm Sekyoung
Published: January 2024

This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body type diagnosis was developed using two full-body pictures of the front and side taken with a mobile phone. For data training, 400 3D body datasets (male: 200, female: 200) provided by Size Korea were used, and four models, i.e., 3D recurrent reconstruction neural network, point cloud generative adversarial network, skinned multi-person linear model, and pixel-aligned impact function for high-resolution 3D human digitization, were used. The models proposed in this study were analyzed and compared. A total of 10 men and women were analyzed, and their corresponding 3D models were verified by comparing 3D body data derived from 2D image inputs with those obtained using a body scanner. The model was verified through the difference between 3D data derived from the 2D image and those derived using an actual body scanner. Unlike the 3D generation models that could not be used to derive the body values in this study, the proposed model was successfully used to derive various body values, indicating that this model can be implemented to identify various body types and monitor obesity in the future.
A Flexible and Stretchable MXene/Waterborne Polyurethane Composite-Coated Fiber Strain Sensor for Wearable Motion and Healthcare Monitoring
Authors: Cao Junming , Jiang Yuanqing , Li Xiaoming , Yuan Xueguang , Zhang Jinnan , He Qi , Ye Fei , Luo Geng , Guo Shaohua , Zhang Yangan , Wang Qi
Published: January 2024

Fiber-based flexible sensors have promising application potential in human motion and healthcare monitoring, owing to their merits of being lightweight, flexible, and easy to process. Now, high-performance elastic fiber-based strain sensors with high sensitivity, a large working range, and excellent durability are in great demand. Herein, we have easily and quickly prepared a highly sensitive and durable fiber-based strain sensor by dip coating a highly stretchable polyurethane (PU) elastic fiber in an MXene/waterborne polyurethane (WPU) dispersion solution. Benefiting from the electrostatic repulsion force between the negatively charged WPU and MXene sheets in the mixed solution, very homogeneous and stable MXene/WPU dispersion was successfully obtained, and the interconnected conducting networks were correspondingly formed in a coated MXene/WPU shell layer, which makes the as-prepared strain sensor exhibit a gauge factor of over 960, a large sensing range of over 90%, and a detection limit as low as 0.5% strain. As elastic fiber and mixed solution have the same polymer constitute, and tight bonding of the MXene/WPU conductive composite on PU fibers was achieved, enabling the as-prepared strain sensor to endure over 2500 stretching–releasing cycles and thus show good durability. Full-scale human motion detection was also performed by the strain sensor, and a body posture monitoring, analysis, and correction prototype system were developed via embedding the fiber-based strain sensors into sweaters, strongly indicating great application prospects in exercise, sports, and healthcare.
Conditional Enhanced Variational Autoencoder-Heterogeneous Graph Attention Neural Network: A Novel Fault Diagnosis Method for Electric Rudders Based on Heterogeneous Information
Authors: Cao Ximing , Yang Ruifeng , Guo Chenxia , Qin Hao
Published: January 2024

In machine fault diagnosis, despite the wealth of information multi-sensor data provide for constructing high-quality graphs, existing graph data-driven diagnostic methods face challenges posed by handling these heterogeneous multi-sensor data. To address this issue, we propose CEVAE-HGANN, an innovative model for fault diagnosis based on the electric rudder, which can process heterogeneous data efficiently. Initially, we facilitate interaction between conditional information and the original features, followed by dimensional reduction via a conditional enhanced variational autoencoder, thereby achieving a more robust state representation. Subsequently, we define two meta-paths and employ both the Euclidean distance and Pearson coefficient in crafting an effective adjacency matrix to delineate the relationships among edges within the graph, thereby effectively representing the complex interrelations among these subsystems. Ultimately, we incorporate heterogeneous graph attention neural networks for classification, which emphasizes the connections among different subsystems, moving beyond the reliance on node-level fault identification and effectively capturing the complex interactions between subsystems. The experimental outcomes substantiate the superiority of the electric rudder-based CEVAE-HGANN model fault diagnosis.
A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN
Authors: Liu Dali , Yang Hongyuan , Hou Weimin , Wang Baozhu
Published: January 2024

In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.
Effects of Domestic and International External Collaboration on New Product Development Performance in SMEs: Evidence from China
Authors: Lu Chang , Qi Yong , Yu Bo
Published: January 2024

This study attempts to examine the influences of domestic and international external collaboration on new product development (NPD) performance in small and medium-sized enterprises (SMEs) and further explores the mediating mechanism of knowledge integration capability and the moderating effects of the innovation climate and organizational legitimacy. In this study, we collected data from 236 manufacturing SMEs in China by means of questionnaires, and multiple hierarchical regression analysis was used to test hypotheses. The results reveal that both domestic and international external collaboration are positively associated with SMEs’ NPD performance. Knowledge integration capability mediates domestic and international external collaboration–SMEs’ NPD performance relationships. Innovation climate positively moderates domestic external collaboration–SMEs’ NPD performance relationship and organizational legitimacy positively moderates domestic and international external collaboration–SMEs’ NPD performance relationships. This study expands the research on the relationship between external collaboration and NPD performance by categorizing external collaboration into two dimensions, domestic and international, and examining their direct influences on SMEs’ NPD performance. Moreover, through examining the mediating effect of knowledge integration capabilities and moderating effects of innovation climate and organizational legitimacy, this paper explains how, and under what conditions, domestic and international external collaboration facilitates the improvement of NPD performance in SMEs.
Exploring Circular Economy Practices in the Healthcare Sector: A Systematic Review and Bibliometric Analysis
Authors: D’Alessandro Carlotta , Szopik-Depczyńska Katarzyna , Tarczyńska-Łuniewska Małgorzata , Silvestri Cecilia , Ioppolo Giuseppe
Published: January 2024

The healthcare sector produces 2 gigatons of CO2. To address this impactful trend and contribute to the Sustainable Development Goals (SDGs), the adoption of circular economy (CE) practices could represent a strategic target. In this context, the present article provides a systematic and bibliometric literature review of CE practices applied in the healthcare sector by considering the collected case studies. This study aims to analyze the state of the art in CEs in the healthcare sector in order to identify CE practices in healthcare, examining how they contribute to sustainability goals and the critical issues in their implementation. A final selection of 36 articles from reputable databases, Web of Science and Scopus, was obtained and analyzed using VOSviewer. By systematically examining these papers, the study investigates the key CE practices implemented within the healthcare sector and their respective areas of application, which help the broader mission of achieving SDG 12, and also, to a lesser extent, SDG 9. Although the research criteria impose some limitations, this study offers a comprehensive review of successful circular practices adopted in the healthcare sector while shedding light on existing gaps and providing valuable insights for relevant stakeholders.
Purification of Pesticide-Contaminated Water Using Activated Carbon from Prickly Pear Seeds for Environmentally Friendly Reuse in a Circular Economy
Authors: Zgolli Amira , Souissi Marwa , Dhaouadi Hatem
Published: January 2024

This study proposes an innovative approach based on the concept of the circular economy. It involves treating deltamethrin-contaminated water using an activated carbon (AC) adsorption technique based on a highly adsorbent plant waste derived from prickly pear seeds (PPSs). Activated carbon was prepared from PPS via a simple pyrolysis process preceded by chemical impregnation with phosphoric acid. Thus, a whole range of physicochemical tests were carried out, including iodine number (QI2), methylene blue number (QMB), Bohem dosage, pHZC, Brunauer–Emmett–Teller analysis (BET), and scanning electron microscopy (SEM). The QI2 and QMB were, respectively, 963.5 (mg g−1) and 8.3 (mg g−1). The pHzc of activated carbon was 2.5, and the surface area BET was 1161.3 m2 g−1. Adsorption kinetics, isotherms, and thermodynamic studies of pesticides using activated carbon were established. The obtained results revealed that the adsorption of the pesticide by the activated carbon appeared to be chemisorption with an adsorption capacity of 1.13 mg g−1. The adsorption capacity increased with increasing temperature, which explains an endothermic adsorption interaction. These results are in agreement with the results found using the density functional theory (DFT) and showed that activated carbon has an interesting adsorption power, which makes it as efficient as commercial activated carbon and predisposes it to the depollution of aqueous solutions contaminated with pesticides.
On a Hierarchy of Vector Derivative Nonlinear Schrödinger Equations
Authors: Smirnov Aleksandr O. , Frolov Eugene A. , Dmitrieva Lada L.
Published: January 2024

We propose a new hierarchy of the vector derivative nonlinear Schrödinger equations and consider the simplest multiphase solutions of this hierarchy. The study of the simplest solutions of these equations led to the following results. First, the three-leaf spectral curves Γ={(μ,λ)} of the simplest multiphase solutions have a quite simple symmetry. They are invariant with respect to holomorphic involution τ. The type of this involution depends on the genus of the spectral curve. Or the involution has the form τ:(μ,λ)→(μ,−λ), or τ:(μ,λ)→(−μ,−λ). The presence of symmetry leads to the fact that the dynamics of the solution is determined not by the entire spectral curve Γ, but by its factor Γ/τ, which has a smaller genus. Secondly, it turned out that the dynamics of the two-component vector p=(p1,p2)t is determined, first of all, by the dynamics of its length |p|. Independent equations determine the dependence of the direction of the vector p from its length. In cases where the direction of the vector p is fixed, the corresponding spectral curve splits into separate components. In conclusion, we note that, as in the case of the Manakov system, the equation of the spectral curve is invariant with respect to the orthogonal transformation of the vector solutions. I.e., the solution can be found from the spectral curve up to the orthogonal transformation. This fact indicates that the spectral curve does not depend on the individual components of the solution, but on their symmetric functions. Thus, the spectral data of multiphase solutions have two symmetries. These symmetries make it difficult to reconstruct signals from their spectral data. The work contains examples illustrating these statements.
Zoonoses: Changing, Challenging, and Increasing Global Disease Threats
Authors: Wikel Stephen K.
Published: January 2024

The public awareness of zoonotic pathogens as well as the threats they pose to global public health have grown significantly, since the emergence of SARS-CoV-2, the causative agent of the COVID-19 pandemic [...]
Factores que inciden en el afianzamiento de emprendimientos y su impacto en la economía ecuatoriana
Authors: Toala Moreno Luis , Rengifo Herradez Karina Clarismar
Published: January 2024

Este artículo tiene por objetivo determinar factores que alteran el afianzamiento de emprendimientos y su impacto en la economía ecuatoriana, tomando como referente la ciudad de Portoviejo. Metodológicamente se realizó bajo un enfoque cuantitativo subordinado al paradigma positivista de profundidad descriptiva con alcance explicativo, donde se estableció relación entre variables para determinar la incidencia entre ellas. En la medición de los datos se empleó como técnica la encuesta y como instrumento un cuestionario, compuesto por 32 preguntas con respuestas ajustadas a un escalamiento Likert. La población estuvo conformada por los emprendimientos de la provincia de Manabí, de los que se desconoce el total existente, por lo que la población se considera infinita. En tal sentido, se consideró una muestra intencional para efectos del estudio representada por los emprendimientos existentes en la ciudad de Portoviejo, es decir una muestra total de 180 emprendimientos catastrados que se encuentran en la municipalidad. Los datos se analizaron utilizando la estadística descriptiva y el programa SPSS versión 25 para la correlación de Rho Spearman,  se representaron en tablas y gráficos. Los resultados muestran la existencia de correlación entre el afianzamiento de emprendimientos y la economía ecuatoriana,  por lo que se concluye que  si  el afianzamiento de los emprendimientos mejora, la economía ecuatoriana también mejorará en el mismo sentido y magnitud.
Festival Economy: The Impact of Events on Sustainable Tourism
Authors: Purwadi Purwadi , Darma Dio , Setini Made
Published: January 2024

Tourism is like a “treasure” and needs to be developed in order to attract visitors to an area. The advantage of those who have a destination with the presence of various attractions, events, and local culture considered a bonus. The case study of the tourism sector in Samarinda City has not yet developed and is in line with expectations, therefore we have started aspects of the festival economic which are expanded into three dimensions (event management, sustainable tourism, and destination branding). Evaluation to find out how big its role is, filtered by multiple regression analysis. We collected the data based on surveys, in which it conducted direct interviews with respondents who had a good understanding of the local demographic, social and cultural conditions. The survey coverage has determined based on time and place considerations, so they aimed specifically it at the two-day “The 354th Anniversary of Samarinda City” in 2022. The outcome of this study concludes that event management has a significant effect on sustainable tourism and destination branding. In line with that, we also found that sustainable tourism has a significant effect on destination branding and sustainable tourism can play a significant role in both the relationship between event management and destination branding, because the results are significant. Further, our need to study to enrich our findings so that the novelty will continue. The novelty of the theoretical or practical landscape teaches about market segmentation in the middle of a transition, especially boosting tourism commodities towards new trends.
Enhancing Product Excellence and Business Growth Approaches for Small and Medium-Sizes Pastry and Bakery Enterprises
Authors: Wawan Bagus Santoso  , Wuryaningsih Dwi Sayekti  , Dyah Aring Hepiana Lestari
Published: January 2024

TThis research aims to comprehensively assess the production facilities, production performance, operating income, the marketing mix, supporting services, and strategy development within Small and Medium-Sized Enterprises (SMEs) in the pastry and bakery industry. The study includes 41 respondents, comprising business owners, seven employees, 30 consumers, and three expert chefs in the pastry and bakery field. A mixed-method approach, combining descriptive qualitative and quantitative analyses, was employed. The research involved both qualitative exploration of respondents' perceptions and quantitative measurements of production performance and marketing effectiveness. Findings revealed satisfactory product quality, yielding profitable results from an average daily production of 100 bread loaves and 32 cakes. While the overall marketing mix was effective, the promotional aspect was identified as an area requiring enhancement. Supporting services were underutilized, suggesting opportunities for improvement, particularly in optimizing raw material utilization to heighten productivity and income while ensuring superior product quality. The study underscores the need for strategic enhancements in promotional efforts and the maximization of supporting services to align with the already satisfactory product quality. Furthermore, it emphasizes the significance of leveraging raw materials effectively to augment productivity and profitability in the SMEs pastry and bakery sector. Enhancing promotional strategies and optimizing supporting services are crucial for SMEs in the pastry and bakery industry to strengthen market positioning. Additionally, future research endeavors should delve deeper into the impact of optimized raw material utilization on productivity and income within this sector, thereby fostering sustained growth and competitiveness.
Real-Time Sign Language Fingerspelling Recognition using Convolutional Neural Network
Authors: Oguntimilehin Abiodun , Balogun Kolade
Published: January 2024

Sign Language allows mute people to communicate, problem occurs when a conversationalist fails to understand it. Despite efforts to address this problem, an effective solution is not yet found. In this work, Convolutional Neural Network was trained on two different datasets separately- Binary and Red Blue Green )RGB(, each contains 25,900 images of Nigerian Sign Language. A Deep Neural pre-trained module was used to detect hand gestures in the video feed which tackled the issue of complex backgrounds, also showed excellent detection in dimly lit areas. The accuracies of (98.95%, 76%) and (98.87%, 98.85%) were obtained respectively on the training and the validation sets. The real time system developed implemented both models as a single system which makes it a unique one.
Development of Curriculum Standards for “Safety and Quality Improvement”
Authors:   Korean Society of Nursing Research, Kim Ji Mee , Hong Sung Kyung , Lee Sun Woo , Nam Ho Hee , Nam Hye Ri
Published: January 2024

Purpose : This study aimed to develop a curriculum standard for “safety and quality improvement” based on the nursing competencies proposed by QSEN(Quality and Safety Education for Nurses). Methods : Through a systematic process involving three stages(analysis-design-development) required for curriculum development, curriculum standards were designed. The Delphi surveys were conducted twice, and through gathering expert opinions, multiple research team meetings were held to refine and crystallize the specific course design into the final standard after several iterations. Results : In this study, 13 modules focusing on the sub factor of safety and quality improvement were developed. Specifically, there are six modules dedicated to safety, two modules each for teamwork & collaboration and quality improvement and one module for each of the remaining three sub factor. Conclusion : The findings of this study represent just a starting point for curriculum development in nursing education for safety and quality improvement. For sustained improvement in safety and quality in clinical practice, nursing educational institutions and clinical facilities must collaborate and work together continuously to evolve in developing nursing education competencies geared towards enhancing safety and quality.
Luxação congênita de joelho: importância do diagnóstico e tratamento precoce
Authors: Marsal Thainá de Morais , Gaiola Priscila Clemente , Manzano Marcelo Valentim , De Abreu Marilda Aparecida Milanez Morgado , Torrieri Érico , Pereira Priscilla Aparecida Tartari
Published: January 2024

Historicamente, a luxação congênita do joelho é uma malformação rara podendo ser isolado, combinado com outros distúrbios musculoesqueléticos congênitos, ou associado a distúrbios neurológicos, até em condições sindrômicas, tendo como característica hiperextensão do joelho gerando deslocamento anterior da tíbia. A luxação congênita do joelho foi relatada  por Chanssier,  como doença predominante do sexo feminino, sendo difícil distinguir a porcentagem de acometimento entre o joelho direito e esquerdo. A partir de então, houve relatos de casos com intuito de entender melhor tal patologia e seus acometimentos sistêmicos, casos existentes, além do impacto no desenvolvimento motor do recém-nascido. Logo, o papel do pediatra em identificar tal anomalia do membro e encaminhar imediatamente ao ortopedista, promove cuidados necessários para tratamento eficaz do paciente. Destacada essa importância, este trabalho objetivou discorrer sobre luxação congênita do joelho. E contribuir para informar a população sobre a patologia e seu tratamento, além de ressaltar o tema entre os profissionais da saúde. Desta forma, conhecer as questões que englobam a luxação congênita é fundamental para realização da abordagem precoce resultando em tratamento com resultados positivos e melhora da qualidade de vida daqueles acometidos
Tratamento ortodôntico precoce para transposição dentária: relato de caso
Authors: Lopes Filho Hibernon , Cavalcante Lilian Karla Costa , De Farias Talita Pinto , Coutinho Ivy Larissa de Araújo , Lins Beatriz Lima da Silva , Pinto Priscila de Albuquerque , De Miranda Isabella Queiroga , Rodrigues Raphaela Farias
Published: January 2024

A transposição dentária baseia-se na troca de posição de dois elementos dentários em uma mesma arcada. É uma alteração rara, que acomete mais o sexo feminino e atinge com maior prevalência a maxila, com predileção pelo lado direito, sendo o canino o elemento dentário que apresenta uma maior proporção de casos. Possui etiologia multifatorial, como retenção prolongada de dente decíduo, traumas e fatores genéticos. O diagnóstico é obtido através de exame clínico e exames radiográficos, determinando se a transposição é parcial ou total. O objetivo deste estudo é discutir sobre a anomalia e apresentar um relato de caso de transposição entre incisivo lateral e canino no arco inferior do lado esquerdo, o mesmo apresenta baixa prevalência de acordo com a literatura. O caso clínico relatado ocorreu devido a traumas na infância que ocasionaram à retenção prolongada do elemento dentário 72. Realizou-se a exodontia do dente decíduo e foram executados métodos descritos na Ortodontia interceptiva para correção da alteração citada. Com o tratamento interceptivo foram obtidos os resultados esperados devido aos aspectos favoráveis e seu diagnóstico precoce, apresentando assim sucesso na abordagem utilizada.