bluuebunny/crossref_metadata_2025 huggingface dataset:
Developing Models to Predict BRAFV600E and RAS Mutational Status in Papillary Thyroid Carcinoma Using Clinicopathological Features and pERK1/2 Immunohistochemistry Expression
Authors: Harahap Agnes Stephanie , Subekti Imam , Panigoro Sonar Soni , Asmarinah  , Lisnawati  , Werdhani Retno Asti , Agustina Hasrayati , Khoirunnisa Dina , Mutmainnah Mutiah , Gultom Fajar Lamhot , Assadyk Abdillah Hasbi , Ham Maria Francisca
Published: October 2023

The Cancer Genome Atlas (TCGA) has classified papillary thyroid carcinoma (PTC) into indolent RAS-like and aggressive BRAF-like based on its distinct driver gene mutations. This retrospective study aimed to assess clinicopathology and pERK1/2 expression variations between BRAF-like and RAS-like PTCs and establish predictive models for BRAFV600E and RAS-mutated PTCs. A total of 222 PTCs underwent immunohistochemistry staining to assess pERK1/2 expression and Sanger sequencing to analyze the BRAF and RAS genes. Multivariate logistic regression was employed to develop prediction models. Independent predictors of the BRAFV600E mutation include a nuclear score of 3, the absence of capsules, an aggressive histology subtype, and pERK1/2 levels exceeding 10% (X2 = 0.128, p > 0.05, AUC = 0.734, p < 0.001). The RAS mutation predictive model includes follicular histology subtype and pERK1/2 expression > 10% (X2 = 0.174, p > 0.05, AUC = 0.8, p < 0.001). We propose using the prediction model concurrently with four potential combination group outcomes. PTC cases included in a combination of the low-BRAFV600E-scoring group and high-RAS-scoring group are categorized as RAS-like (adjOR = 4.857, p = 0.01, 95% CI = 1.470–16.049). PTCs included in a combination of the high-BRAFV600E-scoring group and low-RAS-scoring group are categorized as BRAF-like PTCs (adjOR = 3.091, p = 0.001, 95% CI = 1.594–5.995). The different prediction models indicate variations in biological behavior between BRAF-like and RAS-like PTCs.
Surface-Enhanced Raman Scattering Active Core-Shell Ag NPs@Carbon Dots with Enzyme-Mimicking Activities for Label-Free Measurement Cholesterol
Authors: Ju Jian , Li Lin , Li Bei , Regmi Sagar , Wang Tingting , Xu Jiao , Li Chaojie , Tang Shixing
Published: October 2023

Serological-sensitive testing of cholesterol holds significant value in the fields of healthcare and clinical diagnosis. This study reports on the preparation of peroxidase-mimicking nanozymes through the wrapping of N, S-doped carbon dots (DCDs) on the surface of silver nanoparticles (Ag NPs@DCD). The shell–core structure of Ag NPs@DCD displays peroxidase-mimicking capability, with the potential to catalyze inactive Raman probe molecules into the Raman reporters. Furthermore, a “shell-isolated nanoparticles-enhanced Raman spectroscopy” structure exhibited an enhanced Raman signal of reporter molecules. Ag NPs@DCD were utilized to create a label-free SERS sensing system for high-performance detection of cholesterol in serum samples. These results demonstrate the potential of the novel nanozyme-based SERS approach for clinical diagnosis.
Smart and Multifunctional Nanomaterials and Applications for Food Safety
Authors: Wu Long
Published: October 2023

Due to growing concerns about food safety and public health, the contaminants or residues of various harmful substances in food have received much attention in recent years [...]
Sex Differences in Complex Posttraumatic Stress Disorder Network among Chinese Young Adults
Authors: Liang Yiming , Yang Luxi
Published: October 2023

Evidence suggests that sex differences commonly occur in trauma-related disorders. The current study aims to explore sex differences in complex posttraumatic stress disorder (CPTSD) symptom networks among Chinese young adults with childhood trauma. The current study utilized a representative sample of college students in Beijing and included 1416 participants (409 men and 907 women) who had childhood trauma experience. CPTSD symptoms were evaluated using the International Trauma Questionnaire. Regularized partial correlation network analysis and Bayesian network analysis were used to estimate the network structure and possible causality of CPTSD symptoms for both sexes. Male and female CPTSD symptom networks had differences in strength centrality and bridge centrality. Nightmares and feelings of failure had the highest strength centrality, and long-term upset and nightmares had the highest bridge centrality for men. Hypervigilance and feelings of failure had the highest strength centrality, and long-term upset and exaggerated startle response had the highest bridge centrality for women. The current study provides the first evidence of sex differences in the CPTSD symptom network among Chinese young adults with childhood trauma. Young men and women differed in highly central symptoms, which may speak to sex specificity in the main manifestations of CPTSD symptoms.
Nickel Glycerolate Overcoming a High-Entropy Configuration for High-Performance Oxygen Evolution Reaction
Authors: Lima Irlan S. , Pereira Rafael S. , Ritter Timothy G. , Shahbazian-Yassar Reza , Gonçalves Josué M. , Angnes Lúcio
Published: October 2023

In response to the requirement for alternative energy conversion and storage methods, metal-glycerolates (MG) and their analogs are considered promising classes of electrode material that can be synthesized in various designs. Recently, the concept of high-entropy configuration and multimetallic systems has gained attention in the field of electrocatalysis. In fact, the presence of five or more metals in a single-phase material can produce unique and unexpected properties. Thus, it becomes crucial to explore different metal combinations and evaluate their synergistic interaction as a result of these combinations. Therefore, in this work, a scalable solvothermal method was used to synthesize a high-entropy glycerolate (HEG) containing Ni, Zn, Mn, Mg, and Co ions (HEG) and their respective sub-systems such as NiG, NiMnG, and NiMnZnG. The SEM-EDS images showed the excellent distribution of the metal cations in the obtained microspheres. Surprisingly, our experiments demonstrated that even in reaching a single-phase HEG, the oxygen evolution reaction (OER) performance measured in 1 M KOH electrolyte did not surpass the benefit effect observed in the NiG-based carbon paste with an overpotential of 310 mV (@10 mA cm–2), against 341 mV (@10 mA cm–2) of HEG. Moreover, the NiG shows good stability toward OER even after 24 h, which is attributed to the NiOOH active phase generated during the electrochemical cycling.
The G Protein-Coupled Estrogen Receptor (GPER): A Critical Therapeutic Target for Cancer
Authors: Hall Keith A. , Filardo Edward J.
Published: October 2023

Estrogens have been implicated in the pathogenesis of various cancers, with increasing concern regarding the overall rising incidence of disease and exposure to environmental estrogens. Estrogens, both endogenous and environmental, manifest their actions through intracellular and plasma membrane receptors, named ERα, ERβ, and GPER. Collectively, they act to promote a broad transcriptional response that is mediated through multiple regulatory enhancers, including estrogen response elements (EREs), serum response elements (SREs), and cyclic AMP response elements (CREs). Yet, the design and rational assignment of antiestrogen therapy for breast cancer has strictly relied upon an endogenous estrogen–ER binary rubric that does not account for environmental estrogens or GPER. New endocrine therapies have focused on the development of drugs that degrade ER via ER complex destabilization or direct enzymatic ubiquitination. However, these new approaches do not broadly treat all cancer-involved receptors, including GPER. The latter is concerning since GPER is directly associated with tumor size, distant metastases, cancer stem cell activity, and endocrine resistance, indicating the importance of targeting this receptor to achieve a more complete therapeutic response. This review focuses on the critical importance and value of GPER-targeted therapeutics as part of a more holistic approach to the treatment of estrogen-driven malignancies.
Characteristics of Unilaterally Impacted Maxillary Canines and Effect on Environmental Tissues: A CBCT Study
Authors: Kucukkaraca Ebru
Published: October 2023

Background: The prognosis of orthodontic treatment for a patient with impacted canine teeth can be affected by many factors and understanding some of the characteristics of impacted teeth can increase the effectiveness and reduce the duration of treatment. This study aims to explore the effects of positions and dentoalveolar morphological characteristics of impacted maxillary canines on the prognosis of orthodontic treatment. Methods: In this study, forty-six adolescent individuals who applied for treatment of impacted teeth were divided into three groups: CC (no impacted maxillary canine group), BC (unilateral buccally positioned impacted maxillary canine group), and PC (unilateral palatally positioned impacted maxillary canine group). The impacted canine and adjacent teeth were evaluated in terms of position, angulation, inclination, mesiodistal and buccolingual widths, root length, dilaceration angle, and root resorption. Results: The mean values of U3 Ang (angulation of the maxillary impacted canine) and U3/U2 angles (maxillary impacted canine and lateral incisor angle) were statistically significantly higher in the BC and PC groups (p < 0.001). The mean values of U2 RL (lateral incisor root length) and U3 RD (canine root dilaceration angle) were statistically significantly lower in the BC and PC groups. Conclusions: The results of this study suggest that shorter root lengths and increased angulation values may lead to the ectopic eruption of canines. The higher canine angulations in the PC group increase the degree of root resorption.
Application of Next-Generation Sequencing to Enterobacter Hormaechei Subspecies Analysis during a Neonatal Intensive Care Unit Outbreak
Authors: Morhart Patrick , Gerlach Roman G. , Kunz Caroline , Held Jürgen , Valenza Giuseppe , Wölfle Joachim , Reutter Heiko , Hanslik Gregor J. , Fahlbusch Fabian B.
Published: October 2023

Introduction: The Enterobacter cloacae complex (ECC) species are potential neonatal pathogens, and ECC strains are among the most commonly encountered Enterobacter spp. associated with nosocomial bloodstream infections. Outbreaks caused by ECC can lead to significant morbidity and mortality in susceptible neonates. At the molecular level, ECC exhibits genomic heterogeneity, with six closely related species and subspecies. Genetic variability poses a challenge in accurately identifying outbreaks by determining the clonality of ECC isolates. This difficulty is further compounded by the limitations of the commonly used molecular typing methods, such as pulsed field gel electrophoresis, which do not provide reliable accuracy in distinguishing between ECC strains and can lead to incorrect conclusions. Next-generation sequencing (NGS) offers superior resolution in determining strain relatedness. Therefore, we investigated the clinical pertinence of incorporating NGS into existing bundle measures to enhance patient management during an outbreak of ECC in a level-3 neonatal intensive care unit (NICU) in Germany. Methods: As the standard of care, all neonates on the NICU received weekly microbiological swabs (nasopharyngeal and rectal) and analysis of endotracheal secretion, where feasible. During the 2.5-month outbreak, colonisation with ECC was detected in n = 10 neonates. The phylogenetic relationship and potential antimicrobial resistance genes as well as mobile genetic elements were identified via bacterial whole-genome sequencing (WGS) using Illumina MiSeq followed by in silico data analysis. Results: Although all ECC isolates exhibited almost identical antimicrobial susceptibility patterns, the WGS data revealed the involvement of four different ECC clones. The isolates could be characterised as Enterobacter hormaechei subspecies steigerwaltii (n = 6, clonal), subsp. hoffmannii (n = 3, two clones) and subsp. oharae (n = 1). Despite the collection of environmental samples, no source of this diffuse outbreak could be identified. A new standardised operating procedure was implemented to enhance the management of neonates colonised with MRGN. This collaborative approach involved both parents and medical professionals and successfully prevented further transmission of ECC. Conclusions: Initially, it was believed that the NICU outbreak was caused by a single ECC clone due to the similarity in antibiotic resistance. However, our findings show that antibiotic susceptibility patterns can be misleading in investigating outbreaks of multi-drug-resistant ECC. In contrast, bacterial WGS accurately identified ECC at the clonal level, which significantly helped to delineate the nature of the observed outbreak.
Stiffness Moduli Modelling and Prediction in Four-Point Bending of Asphalt Mixtures: A Machine Learning-Based Framework
Authors: Baldo Nicola , Rondinella Fabio , Daneluz Fabiola , Vacková Pavla , Valentin Jan , Gajewski Marcin D. , Król Jan B.
Published: October 2023

Stiffness modulus represents one of the most important parameters for the mechanical characterization of asphalt mixtures (AMs). At the same time, it is a crucial input parameter in the process of designing flexible pavements. In the present study, two selected mixtures were thoroughly investigated in an experimental trial carried out by means of a four-point bending test (4PBT) apparatus. The mixtures were prepared using spilite aggregate, a conventional 50/70 penetration grade bitumen, and limestone filler. Their stiffness moduli (SM) were determined while samples were exposed to 11 loading frequencies (from 0.1 to 50 Hz) and 4 testing temperatures (from 0 to 30 °C). The SM values ranged from 1222 to 24,133 MPa. Observations were recorded and used to develop a machine learning (ML) model. The main scope was the prediction of the stiffness moduli based on the volumetric properties and testing conditions of the corresponding mixtures, which would provide the advantage of reducing the laboratory efforts required to determine them. Two of the main soft computing techniques were investigated to accomplish this task, namely decision trees with the Categorical Boosting algorithm and artificial neural networks. The outcomes suggest that both ML methodologies achieved very good results, with Categorical Boosting showing better performance (MAPE = 3.41% and R2 = 0.9968) and resulting in more accurate and reliable predictions in terms of the six goodness-of-fit metrics that were implemented.
Study of Cu Content Reference Materials Used in AlCu Films for X-ray Energy/Wavelength-Dispersive Spectrometer Calibration
Authors: Li Xiao , Zhang Ran , Tian Donghui , Cui Lei , Zhang Yi , Li Xu , Lai Yuming , Wang Yalei
Published: October 2023

Calibration is one of the most important factors affecting the measurement accuracy of X-ray energy/wavelength-dispersive spectrometers (EDS/WDSs). In this work, three Cu content reference materials used in AlCu films for X-ray EDS/WDS calibration are prepared using magnetron sputtering deposition. The Cu content standard values of the three reference materials are determined using an inductively coupled plasma optical emission spectrometer (ICP-OES) and the uncertainties of the reference materials are evaluated. The results show that the homogeneity and stability of the three Cu content reference materials used in AlCu films meet the requirements. The Cu content standard values of the three reference materials are 2.56%, 14.64%, and 49.46% (mass fraction), respectively. Their extended uncertainties are 0.14%, 0.42%, and 0.98% (k = 2), respectively. Using quantitative analysis, it was determined that the three developed reference materials meet the calibration requirements of X-ray EDS/WDS, thus guaranteeing the accuracy and consistency of the measurement results.
Density Testing Method for Undercooling Solidification of High-Temperature Metal Melts
Authors: Niu Tongzhuang , Xu Junfeng , Yao Zhirui , Jian Zengyun , Galenko Peter K.
Published: October 2023

There are numerous methods used for measuring the coefficient of thermal expansion of alloys and density change at low temperatures, but it is difficult to accurately measure the volume and density of high-temperature melts, particularly during the process of rapid volume change during material phase transformation. This article proposes a method for measuring and analysing the volume and density changes in high-temperature alloy melts using high-speed photography and computer MATLAB program image analysis technology, which includes the ordinary image threshold segmentation method, the elliptical fitting method, and the local dynamic threshold segmentation method. The ordinary image threshold segmentation method is best suited to samples with clear boundaries; the elliptical fitting method is the simplest and can be used to analyse samples with unclear boundaries; and the local dynamic threshold segmentation method is the most accurate and best suited to samples with unclear boundaries. These techniques will aid in understanding the variations in the volume and density of high-temperature melt samples during the phase transition process.
Analysis of Dairy Cow Behavior during Milking Associated with Lameness
Authors: Schönberger Diana , Berthel Roxanne Magali , Savary Pascal , Bodmer Michèle
Published: October 2023

The detection of lame cows is a challenging and time-consuming issue for dairy farmers. Many farmers use the milking time to monitor the condition of their animals. Because lame cows often show increased stepping when standing to relieve pressure on aching claws, we investigated whether lame cows showed increased activity in the milking parlor. On 20 Swiss dairy farms, 647 cows were scored on lameness with a five-point locomotion score and categorized as clinical lame and non-lame cows in order to see if there are differences in behavior between these two groups (non-lame = scores 1 and 2; lame = scores 3, 4, and 5). During one evening milking, the behavior of the cows was analyzed. A three-dimensional accelerometer, attached to the milking cluster, detected the hind leg activity indirectly via the movements of the milking unit. Additionally, head movements, as well as weight shifting and the number of steps with the front legs, were analyzed from video recordings. Owing to a high percentage of false positive hind leg activities in some milkings measured by the sensor, only 60% of the collected data were evaluated for behavior (356 cows/milkings on 17 farms). Twenty-seven percent of the investigated cows were classified as lame. The lameness prevalence was increasing with increasing parity. Lame cows showed a higher hind leg activity during milking as well as a higher frequency of front steps and weight shifting events during their stay in the milking parlor than non-lame cows. No relation between the status of lameness and the number of head movements could be seen. Observation of increased stepping and weight shifting of individual animals during milking by the farmer could be used as an additional indicator to detect lame cows, but further investigations are required.
Assessing the Computational Thinking of Pre-Service Teachers: A Gender and Robotics Programming Experience Analysis
Authors: Villalustre Lourdes , Cueli Marisol
Published: October 2023

Computational thinking has provided a new logical approach to teaching programming. However, certain variables, such as gender and previous experience in robotic programming, may influence its development. The present study was aimed to (1) characterise 164 infant and primary education pre-service teachers in terms of their level of computational thinking, (2) analyse whether there are statistically significant differences according to gender and previous experience in robotics programming, and (3) identify profiles that would allow us to analyse the differences between them and the dimensions of computational thinking. For this purpose, the Computational Thinking Test (CTT) was used. The results of the t-tests revealed that men obtained better results than women and that previous experience in robotics programming is a determining factor in the level of development of the CTT. Furthermore, by means of a two-stage cluster analysis, three profiles were identified among the prospective teachers. The statistical analysis revealed that women who had previous experience and had used programming languages at some point had the best results in the CTT. These findings underline the importance of integrating computational thinking into the training of prospective teachers and adapting teaching strategies according to the specific needs of each group of students.
Conceptualising the Integration of Action Research into the Practice of Teacher Education Universities in Kazakhstan
Authors: Berikkhanova Aiman , Sapargaliyeva Bayan , Ibraimova Zhanar , Sarsenbayeva Lyaziza , Assilbayeva Fatima , Baidildinova Dana , Wilson Elaine
Published: October 2023

This study provides a critical review and assessment of the problems of enhancing the research activity at a Kazakhstani pedagogical university that prepares future teachers. The aim of this study is to provide a rationale for the relevance of scientific and methodological support for increasing the research activity of teachers at pedagogical universities through the systematic integration of practitioner action research into professional practice. The value of action research is related to the change in and improvement of the professional practice of teachers based on the convergence of practice-oriented science and science-oriented practice. This work is in response to the gap in the professional preparation of future teachers, particularly in terms of developing research competencies and equipping future teachers with knowledge about action research. This is in marked contrast to in-service teachers who have been exposed to action research through the whole country’s ongoing reform of teacher education. Using a large survey of academic staff, the paper identifies the key factors influencing the research activity of university teachers. The findings from the survey and the narrative review suggest a willingness and openness to new ways of working among staff, which would make it possible to increase the research activity of university teachers themselves by integrating non-formal forms of research such as action research into their practice.
Hybrid Game Trading Mechanism for Virtual Power Plant Based on Main-Side Consortium Blockchains
Authors: Yu Zhiwen , Qiu Zhaoming , Cai Ying , Tao Weijian , Ai Qian , Wang Di
Published: October 2023

With the rapid development and technological innovation in the energy market, peer-to-peer (P2P) energy trading, as a decentralised and efficient trading model, has been widely studied and practically applied. However, in P2P energy transactions involving multiple prosumers, there are challenges such as information asymmetry, trust issues, and transaction transparency. To address these challenges, blockchain technology, as a distributed ledger technology, provides solutions. In this paper, we propose a blockchain technology-based prosumer–virtual power plant (VPP) two-tier interactive energy management framework to assist P2P energy transactions between multiple prosumers. In this framework, the virtual power plant acts as a leader and sets differentiated tariffs for different prosumers to equal the distribution of social welfare. The various prosumers act as followers and respond to the leader’s decisions in a cooperative manner. Blockchain’s immutability and transparency enable prosumers to participate in P2P energy trading with greater trust, share idle energy, and share revenues based on contribution. In addition, given the uncertainty of renewable energy, this paper employs a stochastic planning approach with conditional value at risk (CVaR) to describe the expected loss of VPP. Ultimately, as verified by the arithmetic simulation, the blockchain co-governance transaction model effectively supports energy coordination and optimization of complementarities while ensuring the utility of each transaction node. This model promotes the application of renewable energy in local consumption, while facilitating the innovation and sustainable development of the energy market.
YOLO-GCRS: A Remote Sensing Image Object Detection Algorithm Incorporating a Global Contextual Attention Mechanism
Authors: Liao Huan , Zhu Wenqiu
Published: October 2023

With the significant advancements in deep learning technology, the domain of remote sensing image processing has witnessed a surge in attention, particularly in the field of object detection. The detection of targets in remotely sensed images is a challenging task, primarily due to the abundance of small-sized targets and their multi-scale distribution. These challenges often result in inaccurate object detection, leading to both missed detections and false positives. To overcome these issues, this paper presents a novel algorithm called YOLO-GCRS. This algorithm builds upon the original YOLOv5s algorithm by enhancing the feature capture capability of the backbone network. This enhancement is achieved by integrating a new module, the Global Context Block (GC-C3), with the C3 backbone network. Additionally, the algorithm incorporates a convoluted block known as CBM (Convolution + BatchNormalization + Mish) to enhance the network model’s capability of extracting depth features. Moreover, a detection head, ECAHead, is proposed, which integrates an efficient attention channel (ECA) for extracting high-dimensional features from images. It achieves higher precision, recall, and mAP@0.5 values (98.3%, 94.7%, and 97.7%, respectively) on the publicly available RSOD dataset compared to the original YOLOv5s algorithm (improving by 5.3%, 0.8%, and 2.7%, respectively). Furthermore, when compared to mainstream detection algorithms like YOLOv7-tiny and YOLOv8s, the proposed algorithm exhibits improvements of 2.0% and 7.5%, respectively, in mAP@0.5. These results provide validation for the effectiveness of our YOLO-GCRS algorithm in addressing the challenges of missed and false detections in remote sensing object detection.
Lightweight Three-Dimensional Pose and Joint Center Estimation Model for Rehabilitation Therapy
Authors: Kim Yeonggwang , Ku Giwon , Yang Chulseung , Lee Jeonggi , Kim Jinsul
Published: October 2023

In this study, we proposed a novel transformer-based model with independent tokens for estimating three-dimensional (3D) human pose and shape from monocular videos, specifically focusing on its application in rehabilitation therapy. The main objective is to recover pixel-aligned rehabilitation-customized 3D human poses and body shapes directly from monocular images or videos, which is a challenging task owing to inherent ambiguity. Existing human pose estimation methods heavily rely on the initialized mean pose and shape as prior estimates and employ parameter regression with iterative error feedback. However, video-based approaches face difficulties capturing joint-level rotational motion and ensuring local temporal consistency despite enhancing single-frame features by modeling the overall changes in the image-level features. To address these limitations, we introduce two types of characterization tokens specifically designed for rehabilitation therapy: joint rotation and camera tokens. These tokens progressively interact with the image features through the transformer layers and encode prior knowledge of human 3D joint rotations (i.e., position information derived from large-scale data). By updating these tokens, we can estimate the SMPL parameters for a given image. Furthermore, we incorporate a temporal model that effectively captures the rotational temporal information of each joint, thereby reducing jitters in local parts. The performance of our method is comparable with those of the current best-performing models. In addition, we present the structural differences among the models to create a pose classification model for rehabilitation. We leveraged ResNet-50 and transformer architectures to achieve a remarkable PA-MPJPE of 49.0 mm for the 3DPW dataset.
A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis
Authors: Niu Yijie , Deng Wu , Zhang Xuesong , Wang Yuchun , Wang Guoqing , Wang Yanjuan , Zhi Pengpeng
Published: October 2023

Sparsity-based fault diagnosis methods have achieved great success. However, fault classification is still challenging because of neglected potential knowledge. This paper proposes a combined sparse representation deep learning (SR-DEEP) method for rolling bearing fault diagnosis. Firstly, the SR-DEEP method utilizes prior domain knowledge to establish a sparsity-based fault model. Then, based on this model, the corresponding regularization parameter regression networks are trained for different running states, whose core is to explore the latent relationship between the regularization parameters and running states. Subsequently, the performance of the fault classification is improved by embedding the trained regularization parameter regression networks into the sparse representation classification method. This strategy improves the adaptability of the sparse regularization parameter, further improving the performance of the fault classification method. Finally, the applicability of the SR-DEEP method for rolling bearing fault diagnosis is validated with the CWRU platform and QPZZ-II platform, demonstrating that SR-DEEP yields superior accuracies of 100% and 99.20% for diagnosing four and five running states, respectively. Comparative studies show that the SR-DEEP method outperforms four sparse representation methods and seven classical deep learning classification methods in terms of the classification performance.
Design of High-Gain and Low-Mutual-Coupling Multiple-Input–Multiple-Output Antennas Based on PRS for 28 GHz Applications
Authors: Jung Jinkyu , Awan Wahaj Abbas , Choi Domin , Lee Jaemin , Hussain Niamat , Kim Nam
Published: October 2023

In this paper, a high-gain and low-mutual-coupling four-port Multiple Input Multiple Output (MIMO) antenna based on a Partially Reflective Surface (PRS) for 28 GHz applications is proposed. The antenna radiator is a circular-shaped patch with a circular slot and a pair of vias to secure a wide bandwidth ranging from 24.29 GHz to 28.45 GHz (15.77%). The targeted band has been allocated for several countries such as Korea, Europe, the United States, China, and Japan. The optimized antenna offers a peak gain of 8.77 dBi at 24.29 GHz with a gain of 6.78 dBi. A novel PRS is designed and loaded on the antenna for broadband and high-gain characteristics. With the PRS, the antenna offers a wide bandwidth from 23.67 GHz to 29 GHz (21%), and the gain is improved up to 11.4 dBi, showing an overall increase of about 3 dBi. A 2 × 2 MIMO system is designed using the single-element antenna, which offers a bandwidth of 23.5 to 29 GHz (20%), and a maximum gain of 11.4 dBi. The MIMO antenna also exhibits a low mutual coupling of −35 dB along with a low Envelope Correlation Coefficient and Channel Capacity Loss, making it a suitable candidate for future compact-sized mmWave MIMO systems.
A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction
Authors: E Guangxun , Gao He , Lu Youfu , Zheng Xuehan , Ding Xiaoying , Yang Yuanhao
Published: October 2023

Traditional transmission line fault diagnosis approaches ignore local structure feature information during feature extraction and cannot concentrate more attention on fault samples, which are difficult to diagnose. To figure out these issues, an enhanced feature extraction-based attention temporal convolutional network (EATCN) is developed to diagnose transmission line faults. The proposed EATCN suggests a new comprehensive feature-preserving (CFP) technique to maintain the global and local structure features of original process data during dimension reduction, where the local structure-preserving technique is incorporated into the principal component analysis model. Furthermore, to diagnose transmission line faults more effectively, a CFP-based attention TCN scheme is constructed to classify the global and local structure features of a fault snapshot dataset. To be specific, to cope with the gradient disappearance problem and improve learning capability, a skip connection attention (SCA) network is developed by incorporating a skip connection structure and two fully connected layers into the existing attention mechanism. By combining the developed SCA network with the conventional TCN’s residual blocks, an EATCN-based diagnosis model is then constructed to dynamically pay attention to various imported global and local structure features. Detailed experiments on the datasets of the simulated power system are performed to test the effectiveness of the developed EATCN-based fault diagnosis scheme.
Comparison of Three Linear Digital Filters Applied to Improve the Quality of the Measured Acoustic Field
Authors: Rodrigues Estevão Patricio , Santos Djalma Simões dos , Buiochi Flávio
Published: October 2023

The measurement of acoustic fields generated by ultrasonic transducers is important for determining the focal length, lateral resolution, and amplitudes of the lateral and grating lobes. The acoustic field is commonly characterized by a set of scans using a needle hydrophone. The output of the hydrophone can be connected to an analog filter to enhance the signal. However, the analog filter might not be sufficient to avoid the noises that distort the signals. Alternatively, linear digital filters can be advantageous to improving the acoustic-field characterization. In this work, three filters were investigated: moving average (MA), band-pass Hamming window (HW), and band-pass Blackman window (BW). The filters were implemented and evaluated in terms of the root-mean-square error (RMSE) of the measured sound field, which was filtered, in relation to the simulated acoustic field (gold standard). As a compromise between effective filtering and signal non-distortion, a method to model the MA kernel length was proposed. All the filters reduced the noise of the measured acoustic field. The HW and the BW filters were more effective (RMSE = 4.01%) than the MA filter (RMSE = 4.28%). In spite of the small quantitative difference, acoustic field comparisons showed qualitative improvements.
Exploration of Microbial Community Diversity and Bioactive Substances during Fermentation of Mulberry Jiaosu, an Edible Naturally Fermented Mulberry Product
Authors: Zhang Jing , Zhao Minhui , Yi Yuan , Huang Yifen , Yin Qianqian , Zuo Yong
Published: October 2023

Mulberry Jiaosu, derived from natural fermentation using fresh mulberry fruit as a raw material, refers to an edible product containing specific bioactive substances. However, the dynamic changes in the bioactive substances of organic acids, amino acids and polyphenols as well as the species and function of microorganisms in mulberry Jiaosu are still not clear. Herein, the whole fermentation process of mulberry Jiaosu was comprehensively researched by analyzing the microbial community structure and bioactive substances. The results showed that the change in physicochemical parameters mainly happened within 30 days of fermentation. The total organic acids and total polyphenols presented upward trends. Total amino acids were partly consumed during the fermentation. A total of 173 fungal genera and 295 bacterial genera were detected in mulberry Jiaosu, mainly including Torulaspora, Zygosaccharomyces and Lactobacillus, whose abundance can be influenced by changes in the fermentation environment. During the fermentation of mulberry Jiaosu, 8 organic acids, 17 amino acids and 9 polyphenols were observed, which could be regulated by the metabolism of microorganisms. Zygosaccharomyces exhibited positive correlations with the majority of the organic acids, amino acids and polyphenols, presenting a great influence on the formation of bioactive substances. Compared with fungi, bacteria contributed more to the synthesis of organic acids, free amino acids and polyphenols. This study revealed the bioactive substances and microbial diversity during the fermentation of mulberry Jiaosu, which are findings that will contribute to the precise regulation of the fermentation process and improvement of the product quality.
Complete Mitogenome and Phylogenetic Analysis of a Marine Ray-Finned Fish, Alcichthys elongatus (Perciformes: Cottidae)
Authors: Patil Maheshkumar Prakash , Kim Jong-Oh , Yoo Seung Hyun , Seo Yong Bae , Lee Yu-Jin , Kim Jin-Koo , Kitamura Shin-Ichi , Kim Gun-Do
Published: October 2023

Alcichthys elongatus is the only species in the genus, and this work is the first to provide a comprehensive mitogenome analysis of this species. The A. elongatus mitogenome was 16,712 bp long, with biased A + T content (52.33%), and featured thirteen protein-coding genes (PCGs), twenty-two tRNAs, two rRNAs, and the control region (D-loop). The H strand encoded twenty-eight genes (twelve PCGs, fourteen tRNA, and two rRNA) and the control region, whereas the L strand encoded the remaining nine genes (ND6 and eight tRNA). Except for COXI, which started with GTG, all PCG sequences started with ATG and ended with TAA (ND4L, ND5, COXI, ATP8) or TAG (ND1, ND6) termination codons, with some (ND2, ND3, ND4, COXII, COXIII, ATP6, Cytb) having an incomplete termination codon. Except for tRNA-serine-1 (trnS), the majority of the tRNAs exhibited characteristic cloverleaf secondary structures. Based on 13 PCGs, phylogenetic analysis placed A. elongatus in the same clade as Icelus spatula. This genomic data will be useful for species identification, phylogenetic analysis, and population genetics.
A Decade of Evidence of Sorghum Potential in the Development of Novel Food Products: Insights from a Bibliometric Analysis
Authors: Aguiar Etiene Valéria , Santos Fernanda Garcia , Queiroz Valéria Aparecida Vieira , Capriles Vanessa Dias
Published: October 2023

Due to the increasing interest in sorghum for human nutrition, recent literature reviews highlight its nutrient and bioactive contents, potential health benefits and its ‘gluten-free’ feature. Moreover, a current view of research advances on sorghum-based food products is needed to help both food scientists and industry identify current trends and forward-looking approaches. Studies on homemade processing are still scarce. Thus, this review aimed to provide the latest information regarding the use of sorghum to develop ready-to-eat products or food ingredients based on studies published in the last decade (2012–2022), which then guided discussions on recent advances and prospects. The articles were identified by searching the Elsevier Scopus database. Sorghum has great potential as a functional and sustainable food that can be used in daily meals as a substitute for common cereals like wheat, rice and corn. The studies in the review show that it is possible to process sorghum in a wide variety of ways to obtain ready-to-eat products and ingredients for food products and preparations, such as popping, lamination, extrusion and wet cooking. The studies also show promising approaches to use sorghum in acceptable and nutrient-dense bakery and pasta products, highlighting their gluten-free versions. However, more efforts to make these novel food products available to consumers should be made.
Determination of Modified QuEChERS Method for Chlorothalonil Analysis in Agricultural Products Using Gas Chromatography–Mass Spectrometry (GC-MS/MS)
Authors: Yun Da-Young , Bae Ji-Yeon , Park Chan-Woong , Jang Gui-Hyun , Choe Won-Jo
Published: October 2023

Chlorothalonil is an organochlorine fungicide that blocks the respiratory process of cells and persists in agricultural products because it is used extensively to prevent fungal diseases. An analytical method of chlorothalonil using the modified QuEChERS method and gas chromatography– mass spectrometry (GC-MS/MS) was developed to analyze the residue in agricultural commodities distributed in Republic of Korea. Acetonitrile, including acetic acid and formic acid, was used to compare the extraction efficiency. The extraction and purification processes were established by comparing three versions of the QuEChERS method and various dispersive solid-phase extraction (d-SPE) combinations. Ultimately, 1% formic acid in acetonitrile with QuEChERS original salts and d-SPE (PSA, C18) were selected for the extraction and clean-up procedures for method validation and establishment. Five agricultural commodities, viz., brown rice, mandarin, soybean, pepper, and potato, were examined to validate the established method, which displayed excellent linearity, with a coefficient of determination of R2 = 0.9939–0.997 in the calibration curve range of 0.002–0.1 mg/kg. The limits of detection (LOD) and quantification (LOQ) were calculated to be 0.003 mg/kg and 0.01, respectively, for the method. The LOQ value satisfied the suitable level for the Positive List System (PLS). The mean recovery of chlorothalonil was 79.3–104.1%, and the coefficient of variation was <17.9% for intra- and inter-day precision at 0.01, 0.1, and 0.5 mg/kg. The matrix effects in the five commodities were confirmed by the ion suppression effects, except for brown rice, in which a medium enhancement effect was observed at 21.4%. Chlorothalonil was detected in eight apples, one watermelon, and one cucumber. Ultimately, chlorothalonil was detected in ten agricultural products. Thus, this analytical method could be used for the routine detection of chlorothalonil in agricultural products, and the data may be used to inform and improve current food policies.
Evaluating Gaps in Otolaryngology Training: An In-Depth Needs Assessment in Saudi Arabia
Authors: Alarfaj Abdullah A. , Al-Nasser Sami
Published: October 2023

Background: The landscape of otolaryngology training in Saudi Arabia is undergoing transformation due to the expansion of medical colleges and increased overseas medical scholarships. However, concerns persist regarding the satisfaction and adequacy of surgical education. This study aims to assess gaps in otolaryngology training through an in-depth needs assessment. Methods: A cross-sectional study was conducted among 85 otolaryngology–head and neck surgery residency graduates in Saudi Arabia between 2019 and 2021. Participants completed a validated questionnaire assessing deficiencies, importance, and competence in different subspecialty areas. Data were analyzed using descriptive statistics, median comparisons, and Kruskal–Wallis tests. Results: Participants identified deficiencies in training across domains, with significant variations in specific subspecialties among different regions. Dissatisfaction with clinical discussions, research training, access to simulation labs, and training in emerging subspecialties was evident. Conclusion: The study highlights challenges within otolaryngology training, emphasizing the need for continuous evaluation and adaptation to ensure high-quality and comprehensive training. Addressing these gaps is essential to produce well-rounded otolaryngologists capable of meeting the evolving demands of modern healthcare.
Participation and Outcomes among Disabled and Non-Disabled People in the Diabetes Pay-for-Performance Program
Authors: Kuo Wei-Yin , Tsai Wen-Chen , Kung Pei-Tseng
Published: October 2023

Objectives: This study’s objectives were to compare the participation rates of people with and without disabilities who had type 2 diabetes in a diabetes pay-for-performance (DM P4P) program, as well as their care outcomes after participation. Methods: This was a retrospective cohort study. The data came from the disability registry file, cause of death file, and national health insurance research database of Taiwan. The subjects included patients newly diagnosed with type 2 diabetes between 2001 and 2013 who were followed up with until 2014 and categorized as disabled and non-disabled patients. The propensity score matching method was used to match the disabled with the non-disabled patients at a 1:1 ratio. Conditional logistic regression analysis was used to determine the odds ratio between the disabled and non-disabled patients who joined the P4P program. The Cox hazard model was used to compare the risk of dialysis and death between the disabled and non-disabled patients participating in the P4P program. Results: There were 110,645 disabled and 110,645 non-disabled individuals after matching. After controlling for confounding factors, it was found that the disabled individuals were significantly less likely (odds ratio = 0.89) to be enrolled in the P4P program than the non-disabled individuals. The risk of dialysis was 1.08 times higher for people with disabilities than those without, regardless of their participation in the P4P program. After enrollment in the P4P program, the risk of death for people with disabilities decreased from 1.32 to 1.16 times that of persons without disabilities. Among the people with disabilities, the risk of death for those enrolled in the P4P program was 0.41 times higher than that of those not enrolled. The risk of death was reduced to a greater extent for people with disabilities than for those without disabilities upon enrollment in the DM P4P program. Conclusion: People with disabilities are less likely to be enrolled in the P4P program in Taiwan and have unequal access to care. However, the P4P program was more effective at reducing mortality among people with disabilities than among those without.
Herbal Medicine for Postpartum Pain: A Systematic Review of Puerperal Wind Syndrome (Sanhupung)
Authors: Kwon Na-Yoen , Lee Hee-Yoon , Hwang Su-In , Sung Soo-Hyun , Cho Su-Jin , Yoon Young-Jin , Park Jang-Kyung
Published: October 2023

Mothers in the postpartum period often experience musculoskeletal disorders and pain, impacting their ability to care for themselves and their infants. Conventional treatments have limitations, prompting interest in alternative options like herbal medicine. This systematic review aimed to confirm the effectiveness and safety of herbal medicine treatment to improve maternal health in patients with postpartum pain (puerperal wind syndrome). We searched eight electronic databases for randomized controlled trials (RCTs) to evaluate the effects of herbal medicines on puerperal wind syndrome. Nine RCTs, including 652 patients, were selected. Following a meta-analysis of RCTs, both herbal medicine and combination treatments improved the visual analog scale scores, total effective rate, scores of Traditional Chinese Medicine syndromes, Oswestry Disability Index, and quality of life in patients with role-emotional puerperal wind syndrome. All adverse events were minor, and the incidence rate was not high compared with that of the control group. In conclusion, herbal medicine supports the improvement in pain, other systemic symptoms, and the quality of life of patients with puerperal wind syndrome. Moreover, no serious side effects were observed; therefore, herbal medicines appear to be safe. It can be the preferred treatment option for puerperal wind syndrome, which is currently managed symptomatically.