bluuebunny/crossref_metadata_2025 huggingface dataset:
Inequities in Screening and HPV Vaccination Programs and Their Impact on Cervical Cancer Statistics in Romania
Authors: Simion Laurentiu , Rotaru Vlad , Cirimbei Ciprian , Gales Laurentia , Stefan Daniela-Cristina , Ionescu Sinziana-Octavia , Luca Dan , Doran Horia , Chitoran Elena
Published: August 2023

(1) Introduction: A Romanian woman is diagnosed with cervical cancer every two hours; the country ranks second in Europe in terms of the mortality and incidence rate of this disease. This paper aims to identify the main reasons that have led to this situation, focusing on the measures taken by the Romanian Ministry of Health for the prevention of this type of cancer—national programs for cervical cancer screening and HPV vaccination. (2) Materials and methods: We performed a study based on the available secondary data from the National Statistics Institute, World Health Organization and Bucharest Institute of Oncology in order to assess the burden associated with cervical cancer and place it in the context of known global and European incidence and mortality rates, thus evaluating the importance of this health issue in Romania. The second component of our study was a cross-sectional study. Here, we used a 14-question questionnaire applied to the women participating in the National Screening Program for Cervical Cancer and aimed to evaluate the women’s level of knowledge about screening and HPV vaccination and their access cervical-cancer-specific healthcare services. (3) Results: The results of this research show that a high percentage of women postpone routine checks due to a lack of time and financial resources and indicate that a low level of knowledge about the disease and the specific preventive methods determines the low participation in screening and HPV vaccination programs implemented in Romania, contributing to the country’s cervical cancer situation. (4) Conclusions: The national programs have complicated procedures, are underfunded and do not motivate healthcare workers enough. This, combined with the lack of information for the eligible population, adds up to an extremely low number of women screened and vaccinated. Our conclusion is that the Romanian Ministry of Health must take immediate action by conducting major awareness campaigns, implementing measures to make the programs functional and ensuring coherent funding.
Utility of the Serum Protein Electrophoresis in the Opportunistic Screening for the Deficiency of Alpha-1 Antitrypsin
Authors: Fernández-Gomez Beatriz , Menao-Guillén Sebastian , Fernandez Gonzalez Ayla , Arruebo Muñio Maria , Ramos Alvarez Monica , Inda Landaluce Mercedes , Castillo Arce Maria Angeles , Torralba-Cabeza Miguel Ángel
Published: August 2023

Background: A deficiency in alpha-1 antitrypsin (AAT1) is a rare disorder that represents a significant health threat and early diagnostic priority issue. We investigated the usefulness of the serum protein electrophoresis (SPE) as an opportunistic screening tool for AAT1 deficiency. Methods: For 6 months, all SPE carried out for any reasons were evaluated in our center. In those with less than 3% of alpha-1 globulins, AAT1 concentrations were studied. The SERPINA1 gene was subsequently sequenced in those patients displaying concentrations below 100 mg/dL. Results: Out of the total, 14 patients (0.3%) were identified with low AAT1 concentrations, with 11 of them agreeing to enter the study. Of those, mutations in the SERPINA1 gene were discovered in 10 patients (91%). Heterozygous mutations were detected in seven patients; three had the c.1096G>A mutation (p.Glu366Lys; Pi*Z), two had the c.863A>T mutation (p.Glu288Val; Pi*S), one had the c.221_223delTCT mutation (p.Phe76del; Pi*Malton), and the last one had the c.1066G>A (p.Ala356Thr) mutation, which was not previously described. Finally, one patient had the c.863A>T mutation in homozygosis, whereas two double heterozygous patients c.863A>T/c.1096G>A were detected. Conclusions: An altered result in the concentration of AAT1 anticipates a mutation in the SERPINA1 gene in a manner close to 91%. The relationship between a decrease in the alpha-1 globulin band of the SPE and an alteration in the AAT1 concentration is direct in basal states of health. The SPE is presented as a highly sensitive test for opportunistic screening of AAT1 deficiency.
MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features
Authors: Dominguez Ignacio , Rios-Ibacache Odette , Caprile Paola , Gonzalez Jose , San Francisco Ignacio F. , Besa Cecilia
Published: August 2023

This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. Methods: This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI–ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. Results: Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). Conclusion: Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics’ (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.
Ultrasound and Clinical Alterations in the Foot of Children with Obesity and Diabetes
Authors: Pappalardo Martina , Gori Laura , Randazzo Emioli , Morganti Riccardo , Scaglione Michelangelo , Valiani Margherita , Beni Alessandra , Di Cicco Maria , Peroni Diego G. , Franzoni Ferdinando , Comberiati Pasquale
Published: August 2023

Background. Alterations in plantar soft tissues are often reported in adults with diabetes, whereas data on children are conflicting. Also, the extent of foot damage caused by excess body fat in children has not been fully characterized yet. This study aimed to address the relationship between body mass and structural changes of the foot in children and adolescents with and without diabetes. Methods. In a case-control study, 43 participants (age 13 ± 2.6 years) were recruited, 29 (67%) with type 1 diabetes (T1D) and 14 (33%) controls. Anthropometric parameters [body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR)], foot posture index-6 (FPI-6) for static foot posture, and navicular drop test (NDT) for medial longitudinal arch height (MLA) were measured in all participants. The thickness of the midfoot plantar fascia (MPF) and medial midfoot fat pad (MMFP) were quantified using ultrasound. Results. No differences in clinical and ultrasonographical parameters were observed between the study groups. MMFP thickness was correlated with MPF thickness (p = 0.027). MMFP and MPF thicknesses were positively associated with BMI (p < 0.001 and p = 0.013, respectively), WC (p < 0.001 and p = 0.013), and WHtR (p < 0.001 and p = 0.026). The NDT measured on the right and left foot correlated with WHtR (p = 0.038 and p = 0.009, respectively), but not with WC and BMI. Conclusions. Children with T1D show structural alterations of plantar soft tissues which seem related to body mass increase rather than diabetes pathology. Ultrasound is a valuable tool to assess early structural changes of the foot in young people with an elevated BMI.
The Impact of International Student Mobility on Multicultural Competence and Career Development: The Case of Students from Latin America and the Caribbean in Barcelona
Authors: Valls-Figuera Robert G. , Torrado-Fonseca Mercedes , Borràs Judith
Published: August 2023

Every year, an increasing number of students decide to study abroad in non-English-speaking countries, and Spain is recently a very popular destination within Latin American and Caribbean (LAC) communities. This paper attempts to deepen our knowledge of the impact of international student mobility (ISM) on students’ multicultural identity and career development. To do so, the experiences of 10 LAC students who completed a one-year degree programme in Barcelona are examined. Semi-structured interviews were employed to evaluate the international experiences one year after their completion. During their post-mobility interviews, participants were able to reflect on the ISM through a structured dialogue that allowed them to analyse the experience from a distance. Findings indicate that the ISM helped them to grow personally and professionally and, one year after the stay, they are aware of this evolution. They show an increase in their self-confidence, and they see the experience as an opportunity for personal maturity. This suggests that universities should consider the importance of offering guidance to these students when they end their master’s degree and are considering their plans for the future.
Teaching and Investigating on Modelling through Analogy in Primary School
Authors: Colletti Leonardo , Krik Soufiane , Lugli Paolo , Corni Federico
Published: August 2023

Physics deals with complex systems by reducing them to relationships between a limited number of relevant quantities and general principles. Since we live in a reality characterised by an increasing complexity in all fields, an indispensable challenge arises for education to turn basic science instruction into a key stage of education per se. Is it possible to introduce some aspects of the physics approach as early as the first school years? Which ones, how, and with what results? Here, results of the initial phase of a three-year project on complexity are presented. This educational innovation path has been developed for elementary and middle schools and is designed as a gentle introduction to complex and systemic thinking. It aims to foster in children reasoning by analogies and the development of simple but effective and versatile basic concepts. The project exploits the use of the small set of primary metaphors already available in children’s cognitive toolkit to apply them to describing the characteristics of various circuits, from marbles to water and air to electricity. Pupils’ feedback was analysed through a single case study with a qualitative and quantitative methodology. Results were encouragingly positive and showed a wide range of abilities to capture and develop analogies on the topic of the circuit.
Framing School Governance and Teacher Professional Development Using Global Standardized School Assessments
Authors: Costa Estela , Carvalho Luís Miguel
Published: August 2023

The OECD’s education agenda has been marked since the 1990s by monitoring quality and manufacturing problems and solutions for the so-called knowledge economy. Among the instruments used by the OECD is “PISA for Schools” (Pisa-S), an assessment applied directly to schools worldwide since 2011. In Portugal, it was implemented in 2019 under the designation of “PISA for schools in the municipalities” (PISA-M), claiming to create opportunities for collaborative work between schools to promote the success of local educational policies and the quality of student learning. Taking PISA-M as a policy instrument and building on Coburn problem-framing typology, in this article, the revision of PISA-S to PISA-M is reread to analyze the regulatory rationale for the educational system that PISA-M encodes. This research draws on data from OECD PISA/PISA-S/PISA-M websites and two public hearings with the Portuguese PISA-M coordination in the Portuguese parliament and with education unionists, and in existing research relating to PISA-S. Overall, PISA-M appears to be an instrument to reframe local school governance and teacher professional development practices by capturing problematizations and solutions raised on education, teachers’ development, and how school education should be ordered and coordinated at a municipal scale.
Research on V2V Time Headway Strategy Considering Multi-Front Vehicles Based on Artificial Potential Field
Authors: Shen Chuanliang , Xiao Xiao , Su Wei , Tong Yan , Hu Hongyu
Published: August 2023

Within the Internet of Vehicles, the driver’s conduct is more affected by the information about the vehicles in front of them. To meet the driver’s demand for the design of the safety vehicle distance of the adaptive cruise control system in the Vehicle-to-Vehicle (V2V) environment, this paper employs the artificial potential field (APF) theory to enhance the vehicle time headway strategy and constructs a mixed force function in the global force field with three front vehicles as the center of the three potential fields. Taking into account the inadequate control effect of conventional APF in a dynamic environment, this paper describes the relative kinematic characteristics of the vehicles in front and the host vehicle as parameters and obtains the improved APF. The improved strategy based on APF enhances the forward-looking and anti-interference ability of the distance strategy. Also, the strategy presented in this paper can achieve time headway control under all operating conditions and prevent acceleration fluctuations due to switching between various control strategies. For collaborative simulation, this research uses MATLAB/Simulink and Carsim software. The simulation is conducted under three common traffic scenarios, and it is determined that the improved control method results in smoother changes in vehicle speed, acceleration, and car-following distance, hence improving driving and riding comfort.
An Intelligent Location Method for Power System Oscillation Sources Based on a Digital Twin
Authors: Yang Luojia , Wang Yuhong , Gao Shilin , Zheng Zongsheng , Jiang Qiliang , Zhou Chenyu
Published: August 2023

Aiming at the difficult problem of broadband oscillation localization in power systems, the intelligent localization method of an oscillation source based on a digital twin is proposed, and the oscillation source localization system is thus constructed. Firstly, a digital twin-based oscillation source localization method and its system architecture are proposed. Furthermore, an intelligent positioning method of the oscillation source, based on data-driven and mechanism fusion, is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the super-resolution technique is used to realize the super-resolution measurement of broadband oscillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, branch potential energy is used as the input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.
Fake Biometric Detection Based on Photoplethysmography Extracted from Short Hand Videos
Authors: An Byeongseon , Lim Hyeji , Lee Eui Chul
Published: August 2023

An array of authentication methods has emerged, underscoring the importance of addressing spoofing challenges arising from forgery and alteration. Previous studies utilizing palm biometrics have attempted to circumvent spoofing through geometric methods or the analysis of vein images. However, these approaches are inadequate when faced with hand-printed photographs or in the absence of near-infrared sensors. In this study, we propose using remote photoplethysmography (rPPG) signals to tackle spoofing concerns in palm images captured in RGB environments. rPPG signals were extracted using video durations of 3, 5, and 7 s, and 30 features within the heart rate band were identified through frequency conversion. A support vector machine (SVM) model was trained with the processed features, yielding accuracies of 97.16%, 98.4%, and 97.28% for video durations of 3, 5, and 7 s, respectively. These features underwent dimensionality reduction through a principal component analysis (PCA), and the results were compared with the initial 30 features. Additionally, we evaluated the confusion matrix with zero false-positives for each video duration, finding that the overall accuracy experienced a decline of 1 to 3%. The 5 s video retained the highest accuracy with the smallest decrement, registering a value of 97.2%.
AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems
Authors: Patil Ravindra R. , Calay Rajnish Kaur , Mustafa Mohamad Y. , Ansari Saniya M.
Published: August 2023

In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems.
SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images
Authors: Zabala-Blanco David , Hernández-García Ruber , Barrientos Ricardo J.
Published: August 2023

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.
Throughput/Area-Efficient Accelerator of Elliptic Curve Point Multiplication over GF(2233) on FPGA
Authors: Rashid Muhammad , Sonbul Omar S. , Zia Muhammad Yousuf Irfan , Arif Muhammad , Sajid Asher , Alotaibi Saud S.
Published: August 2023

This paper presents a throughput/area-efficient hardware accelerator architecture for elliptic curve point multiplication (ECPM) computation over GF(2233). The throughput of the proposed accelerator design is optimized by reducing the total clock cycles using a bit-parallel Karatsuba modular multiplier. We employ two techniques to minimize the hardware resources: (i) a consolidated arithmetic unit where we combine a single modular adder, multiplier, and square block instead of having multiple modular operators, and (ii) an Itoh–Tsujii inversion algorithm by leveraging the existing hardware resources of the multiplier and square units for multiplicative inverse computation. An efficient finite-state-machine (FSM) controller is implemented to facilitate control functionalities. To evaluate and compare the results of the proposed accelerator architecture against state-of-the-art solutions, a figure-of-merit (FoM) metric in terms of throughput/area is defined. The implementation results after post-place-and-route simulation are reported for reconfigurable field-programmable gate array (FPGA) devices. Particular to Virtex-7 FPGA, the accelerator utilizes 3584 slices, needs 7208 clock cycles, operates on a maximum frequency of 350 MHz, computes one ECPM operation in 20.59 μs, and the calculated value of FoM is 13.54. Consequently, the results and comparisons reveal that our accelerator suits applications that demand throughput and area-optimized ECPM implementations.
BFD-YOLO: A YOLOv7-Based Detection Method for Building Façade Defects
Authors: Wei Guofeng , Wan Fang , Zhou Wen , Xu Chengzhi , Ye Zhiwei , Liu Wei , Lei Guangbo , Xu Li
Published: August 2023

Façade defects not only detract from the building’s aesthetics but also compromise its performance. Furthermore, they potentially endanger pedestrians, occupants, and property. Existing deep-learning-based methodologies are facing some challenges in terms of recognition speed and model complexity. An improved YOLOv7 method, named BFD-YOLO, is proposed to ensure the accuracy and speed of building façade defects detection in this paper. Firstly, the original ELAN module in YOLOv7 was substituted with a lightweight MobileOne module to diminish the quantity of parameters and enhance the speed of inference. Secondly, the coordinate attention module was added to the model to enhance feature extraction capability. Next, the SCYLLA-IoU was used to expedite the rate of convergence and increase the recall of the model. Finally, we have extended the open datasets to construct a building façade damage dataset that includes three typical defects. BFD-YOLO demonstrates excellent accuracy and efficiency based on this dataset. Compared to YOLOv7, BFD-YOLO’s precision and mAP@.5 are improved by 2.2% and 2.9%, respectively, while maintaining comparable efficiency. The experimental results indicate that the proposed method obtained higher detection accuracy with guaranteed real-time performance.
A Study on the Rapid Detection of Steering Markers in Orchard Management Robots Based on Improved YOLOv7
Authors: Gao Yi , Tian Guangzhao , Gu Baoxing , Zhao Jiawei , Liu Qin , Qiu Chang , Xue Jinlin
Published: August 2023

In order to guide the orchard management robot to realize autonomous steering in the row ends of a complex orchard environment, this paper proposes setting up steering markers in the form of fruit trees at the end of the orchard rows and realizing the rapid detection of the steering markers of the orchard management robot through the fast and accurate recognition and classification of different steering markers. First, a high-precision YOLOv7 model is used, and the depthwise separable convolution (DSC) is used instead of the 3 × 3 ordinary convolution, which improves the speed of model detection; at the same time, in order to avoid a decline in detection accuracy, the Convolutional Block Attention Module (CBAM) is added to the model, and the Focal loss function is introduced to improve the model’s attention to the imbalanced samples. Second, a binocular camera is used to quickly detect the steering markers, obtain the position information of the robot to the steering markers, and determine the starting point position of the robot’s autonomous steering based on the position information. Our experiments show that the average detection accuracy of the improved YOLOv7 model reaches 96.85%, the detection time of a single image reaches 15.47 ms, and the mean value of the localization error is 0.046 m. Comparing with the YOLOv4, YOLOv4-tiny, YOLOv5-s, and YOLOv7 models, the improved YOLOv7 model outperforms the other models in terms of combined detection time and detection accuracy. Therefore, the model proposed in this paper can quickly and accurately perform steering marker detection and steering start point localization, avoiding problems such as steering errors and untimely steering, shortening the working time and improving the working efficiency. This model also provides a reference and technical support for research on robot autonomous steering in other scenarios.
Enhancing QoS with LSTM-Based Prediction for Congestion-Aware Aggregation Scheduling in Edge Federated Learning
Authors: Tam Prohim , Kang Seungwoo , Ros Seyha , Kim Seokhoon
Published: August 2023

The advancement of the sensing capabilities of end devices drives a variety of data-intensive insights, yielding valuable information for modelling intelligent industrial applications. To apply intelligent models in 5G and beyond, edge intelligence integrates edge computing systems and deep learning solutions, which enables distributed model training and inference. Edge federated learning (EFL) offers collaborative edge intelligence learning with distributed aggregation capabilities, promoting resource efficiency, participant inclusivity, and privacy preservation. However, the quality of service (QoS) faces challenges due to congestion problems that arise from the diverse models and data in practical architectures. In this paper, we develop a modified long short-term memory (LSTM)-based congestion-aware EFL (MLSTM-CEFL) approach that aims to enhance QoS in the final model convergence between end devices, edge aggregators, and the global server. Given the diversity of service types, MLSTM-CEFL proactively detects the congestion rates, adequately schedules the edge aggregations, and effectively prioritizes high mission-critical serving resources. The proposed system is formulated to handle time series analysis from local/edge model parameter loading, weighing the configuration of resource pooling properties at specific congestion intervals. The MLSTM-CEFL policy orchestrates the establishment of long-term paths for participant-aggregator scheduling and follows the expected QoS metrics after final averaging in multiple industrial application classes.
Lightweight Reconstruction Network for Surface Defect Detection Based on Texture Complexity Analysis
Authors: Shi Hui , Li Gangyan , Bao Hanwei
Published: August 2023

Deep learning networks have shown excellent performance in surface defect recognition and classification of certain industrial products. However, most industrial product defect samples are scarce and have a wide variety of defect types, making methods that require a large number of defect samples for training unsuitable. In this paper, a lightweight surface defect detection network (LRN-L) based on texture complexity analysis is proposed. Only a large number of defect-free samples, which can be easily obtained, are needed to detect defects. LRN-L includes two stages: texture reconstruction stage and defect localization stage. In the texture reconstruction phase, a lightweight reconstruction network (LRN) based on convolutional autoencoder is designed, which can reconstruct defect-free texture images; a loss function combining structural loss and L1 loss is proposed to improve the detection effect; we built a calculation model for image complexity, calculated the texture complexity for texture samples, and divided textures into three levels based on complexity. In the defect localization stage, the residual between the reconstructed image and the original image is taken as the possible region of the defect, and the defect localization is realized via a segmentation algorithm. In this paper, the network structure, loss function, texture complexity and other factors of LRN-L are analyzed in detail and compared with other similar algorithms on multiple texture datasets. The results show that LRN-L has strong robustness, accuracy and generalization ability, and is more suitable for industrial online detection.
D2StarGAN: A Near-Far End Noise Adaptive StarGAN for Speech Intelligibility Enhancement
Authors: Li Dengshi , Zhu Chenyi , Zhao Lanxin
Published: August 2023

When using mobile communication, the voice output from the device is already relatively clear, but in a noisy environment, it is difficult for the listener to obtain the information expressed by the speaker with clarity. Consequently, speech intelligibility enhancement technology has emerged to help alleviate this problem. Speech intelligibility enhancement (IENH) is a technique that enhances speech intelligibility during the reception phase. Previous research has focused on IENH through normal versus different levels of Lombardic speech conversion, inspired by a well-known acoustic mechanism called the Lombard effect. However, these methods often lead to speech distortion and impair the overall speech quality. To address the speech quality degradation problem, we propose an improved (StarGAN)-based IENH framework by combining StarGAN networks with the dual discriminator idea to construct the conversion framework. This approach offers two main advantages: (1) Addition of a speech metric discriminator on top of StarGAN to optimize multiple intelligibility and quality-related metrics simultaneously; (2) a framework that is adaptive to different distal and proximal noise levels with different noise types. Experimental results from objective experiments and subjective preference tests show that our approach outperforms the baseline approach, and these enable IENH to be more widely used.
Efficient and Lightweight Visual Tracking with Differentiable Neural Architecture Search
Authors: Gao Peng , Liu Xiao , Sang Hong-Chuan , Wang Yu , Wang Fei
Published: August 2023

Over the last decade, Siamese network architectures have emerged as dominating tracking paradigms, which have led to significant progress. These architectures are made up of a backbone network and a head network. The backbone network comprises two identical feature extraction sub-branches, one for the target template and one for the search candidate. The head network takes both the template and candidate features as inputs and produces a local similarity score for the target object in each location of the search candidate. Despite promising results that have been attained in visual tracking, challenges persist in developing efficient and lightweight models due to the inherent complexity of the task. Specifically, manually designed tracking models that rely heavily on the knowledge and experience of relevant experts are lacking. In addition, the existing tracking approaches achieve excellent performance at the cost of large numbers of parameters and vast amounts of computations. A novel Siamese tracking approach called TrackNAS based on neural architecture search is proposed to reduce the complexity of the neural architecture applied in visual tracking. First, according to the principle of the Siamese network, backbone and head network search spaces are constructed, constituting the search space for the network architecture. Next, under the given resource constraints, the network architecture that meets the tracking performance requirements is obtained by optimizing a hybrid search strategy that combines distributed and joint approaches. Then, an evolutionary method is used to lighten the network architecture obtained from the search phase to facilitate deployment to devices with resource constraints (FLOPs). Finally, to verify the performance of TrackNAS, comparison and ablation experiments are conducted using several large-scale visual tracking benchmark datasets, such as OTB100, VOT2018, UAV123, LaSOT, and GOT-10k. The results indicate that the proposed TrackNAS achieves competitive performance in terms of accuracy and robustness, and the number of network parameters and computation volume are far smaller than those of other advanced Siamese trackers, meeting the requirements for lightweight deployment to resource-constrained devices.
Infrared Dim and Small Target Sequence Dataset Generation Method Based on Generative Adversarial Networks
Authors: Zhang Leihong , Lin Weihong , Shen Zimin , Zhang Dawei , Xu Banglian , Wang Kaimin , Chen Jian
Published: August 2023

With the development of infrared technology, infrared dim and small target detection plays a vital role in precision guidance applications. To address the problems of insufficient dataset coverage and huge actual shooting costs in infrared dim and small target detection methods, this paper proposes a method for generating infrared dim and small target sequence datasets based on generative adversarial networks (GANs). Specifically, first, the improved deep convolutional generative adversarial network (DCGAN) model is used to generate clear images of the infrared sky background. Then, target–background sequence images are constructed using multi-scale feature extraction and improved conditional generative adversarial networks. This method fully considers the infrared characteristics of the target and the background, which can achieve effective expansion of the image data and provide a test set for the infrared small target detection and recognition algorithm. In addition, the classifier’s performance can be improved by expanding the training set, which enhances the accuracy and effect of infrared dim and small target detection based on deep learning. After experimental evaluation, the dataset generated by this method is similar to the real infrared dataset, and the model detection accuracy can be improved after training with the latest deep learning model.
Pulse-Width-Modulation-Based Time-Delay Compensation Control for High-Speed On/Off Valves
Authors: Li Quanzhen , Hao Peng , Wang Jian , Deng Hua
Published: August 2023

The dynamic characteristics of high-speed on/off valves (HSVs) are a key factor in measuring their performance, and determining the control accuracy of valve-controlled systems. Furthermore, the hysteresis characteristics of HSVs can seriously affect their dynamic characteristics. This study evaluated the hysteresis characteristics of HSVs in a valve-controlled hydraulic control system, and considered the pressure changes in front of the valve during the opening and closing process of the valve core. A time-delay compensation control (TDCC) based on pulse-width modulation (PWM) was proposed. The reference PWM signal was used to control the opening and closing time of the HSV, while the loading signal was composed of an opening compensation PWM, an excitation PWM, an opening holding PWM, and a closing compensation PWM. Using an opening compensation PWM to start the initial current, combined with current feedback and pressure changes in front of the valve, the amplitude and duty cycle of different PWM signals were determined in real time. This reduced the time delay and working current of the HSV during opening and closing. A simulation comparison analysis was conducted, with a single PWM control and a pre-excitation control algorithm (PECA). The results showed that, compared to a single PWM control, the TDCC can reduce the overall opening and closing time delay by 78.1%, and the energy consumption by 64.7%. Compared with PECA, the overall opening and closing time delay was reduced by 10.9%, and the energy consumption was reduced by 28%. At the same time, the frequency response of the valve core displacement increased by 70%, compared to the single PWM control.
Accelerating Bayesian Estimation of Solar Cell Equivalent Circuit Parameters Using JAX-Based Sampling
Authors: Tada Kazuya
Published: August 2023

Equivalent circuit models that reproduce the current–voltage characteristics of solar cells are useful not only to gain physical insight into the power loss mechanisms that take place in solar cells but also for designing systems that use renewable solar energy as a power source. As mentioned in a previous paper, Bayesian estimation of equivalent circuit parameters avoids the drawbacks of nonlinear least-squares methods, such as the possibility of evaluating estimation errors. However, it requires a long computation time because the estimated values are obtained by sampling using a Markov chain Monte Carlo method. In this paper, a trial to accelerate the calculation by upgrading the Bayesian statistical package PyMC is presented. PyMC ver. 4, the next version of PyMC3 used in the previous paper, started to support the latest sampling libraries using a machine learning framework JAX, in addition to PyMC-specific methods. The acceleration effect of JAX is remarkable, achieving a calculation time of less than 1/20 times that of the case without JAX. Recommended calculation conditions were disclosed based on the results of a number of trials, and a demonstration with testable Python code on Google Colaboratory using the recommended conditions is published on GitHub.
Research on Morphological Indicator Extraction Method of Pinus massoniana Lamb. Based on 3D Reconstruction
Authors: Li Yurong , Xia Haifei , Liu Ying , Ji Kaihao , Huo Lintao , Ni Chao
Published: August 2023

Pinus massoniana (Lamb.) is an important plantation species in southern China. Accurate measurement of P. massoniana seedling morphological indicators is crucial for accelerating seedling quality assessment. Machine vision, with its objectivity and stability, can replace human eyes in performing these measurements. In this paper, a measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed. Firstly, multi-angle sequence images of 30 one-year-old P. massoniana seedlings were collected, distorted, and corrected to generate a sparse point cloud through the Structure-from-Motion (SFM) and dense point cloud through the Patch-Based Multiple View Stereo (PMVS). Secondly, a Dense Weighted Semantic Segmentation Model based on PointNet++ was designed, achieving effective segmentation of the P. massoniana seedling point clouds. Finally, a multi-iteration plane method based on Laplacian contraction was proposed. The new skeleton points were refined by minimizing the Euclidean distance, iteratively generating the optimal morphological skeleton, thus facilitating the extraction of morphological indicators. The experimental results demonstrated a good correlation between the machine vision-extracted morphological indicators (including plant height, ground diameter, and height-to-diameter ratio) and manually measured data. The improved PointNet++ model achieved an accuracy of 0.9448 on the training set. The accuracy and Mean Intersection over Union (MIoU) of the test set reached 0.9430 and 0.7872, respectively. These findings can provide reliable technical references for the accurate assessment of P. massoniana seedling quality and the promotion of digital forestry construction.
Single-Cell Oil Production by Engineered Ashbya gossypii from Non-Detoxified Lignocellulosic Biomass Hydrolysate
Authors: Francisco Miguel , Aguiar Tatiana Q. , Abreu Gabriel , Marques Susana , Gírio Francisco , Domingues Lucília
Published: August 2023

In this work, microbial lipid production from non-detoxified Eucalyptus bark hydrolysate (EBH) with oleaginous xylose-utilizing Ashbya gossypii strains was explored. The best producing strain from a set of engineered strains was identified in synthetic media mimicking the composition of the non-detoxified EBH (SM), the lipid profile was characterized, and yeast extract and corn steep liquor (CSL) were pinpointed as supplements enabling a good balance between lipid accumulation, biomass production, and autolysis by A. gossypii. The potential of the engineered A. gossypii A877 strain to produce lipids was further validated and optimized with minimally processed inhibitor-containing hydrolysate and high sugar concentration, and scaled up in a 2 L bioreactor. Lipid production from non-detoxified EBH supplemented with CSL reached a lipid titer of 1.42 g/L, paving the way for sustainable single-cell oil production within the concept of circular economy and placing lipids as an alternative by-product within microbial biorefineries.
Case Studies of Small-Medium Food Enterprises around the World: Major Constraints and Benefits from the Implementation of Food Safety Management Systems
Authors: Lee Jocelyn C. , Neonaki Marina , Alexopoulos Athanasios , Varzakas Theodoros
Published: August 2023

Global food safety and security are key principles to be followed in the context of the implementation of food safety management systems. The objective of this paper is to assess the contemporary developments of Food Safety Management System standards (FSMS) worldwide and to identify the primary constraints and advantages associated with their implementation by small and medium-sized enterprises across different regions. The effectiveness of these systems has also been evaluated. 116 case studies have been employed across developing and developed regions worldwide across 27 primary food sectors. After the implementation of FSMS, there was a significant increase in the percentage of companies that have implemented the international FSMS, both in developed (16.7% to 63.9%) and developing countries (26.6% to 48.1%). Certification has also increased from 34.2% to 59.6% in the total sample, namely from 33.3% to 61.1% in developed countries and from 34.6% to 59.0% in developing countries. There was a significant increase in medium vs. small company size (57.1% to 62.3%, p = 0.046), only in developing countries. Food safety culture and manager leadership implementation were increased to over 80% after FSMS implementation in both developed and developing countries (p < 0.001). Training, resources, and technology adequacy were also increased in all companies (p < 0.001).
Cellulose Isolation from Tomato Pomace: Part II—Integrating High-Pressure Homogenization in a Cascade Hydrolysis Process for the Recovery of Nanostructured Cellulose and Bioactive Molecules
Authors: Pirozzi Annachiara , Olivieri Federico , Castaldo Rachele , Gentile Gennaro , Donsì Francesco
Published: August 2023

This work proposes a biorefinery approach for utilizing tomato pomace (TP) through a top-down deconstructing strategy, combining mild chemical hydrolysis with high-pressure homogenization (HPH). The objective of the study is to isolate cellulose pulp using different combinations of chemical and physical processes: (i) direct HPH treatment of the raw material, (ii) HPH treatment following acid hydrolysis, and (iii) HPH treatment following alkaline hydrolysis. The results demonstrate that these isolation routes enable the production of cellulose with tailored morphological properties from TP with higher yields (up to +21% when HPH was applied before hydrolysis and approximately +6% when applied after acid or after alkaline hydrolysis). Additionally, the side streams generated by this cascade process show a four-fold increase in phenolic compounds when HPH is integrated after acid hydrolysis compared to untreated sample, and they also contain nanoparticles composed of hemicellulose and lignin, as shown by FT-IR and SEM. Notably, the further application of HPH treatment enables the production of nanostructured cellulose from cellulose pulp derived from TP, offering tunable properties. This approach presents a sustainable pathway for the extraction of cellulose and nanocellulose, as well as the valorization of value-added compounds found in residual biomass in the form of side streams.
Preliminary Estimation of Nutritional Quality of the Meat, Liver, and Fat of the Indigenous Yakutian Cattle Based on Their Fatty Acid Profiles
Authors: Makhutova Olesia N. , Nokhsorov Vasiliy V. , Stoyanov Kirill N. , Dudareva Lyubov V. , Petrov Klim A.
Published: August 2023

The Yakutian cattle is an indigenous Siberian cattle breed living in an extremely cold climate in some parts of Yakutia. There are only a few thousand animals of this breed, and the conservation of the Yakutian cattle is embedded in the international agenda. We studied the fatty acid profiles in the meat, liver, and fat of the Yakutian cattle (five individuals) of different ages and their main food resource–pasture plants. The fatty acid profile of the tissues of the Yakutian cattle differed from that of pasture plants: 16:0, 18:2n–6, and 18:3n–3 dominated in the pasture plants; 16:0, 18:0, 18:1n–9, 18:2n–6, 20:4n–6, 20:5n–3, and 22:5n–3 dominated in the meat and liver; and 16:0, 18:0, and 18:1n–9 dominated in the fat. The fatty acid composition of food products is related to the risk of developing cardiovascular disease (CVD). The meat and liver of the Yakutian cattle are health food products that contribute to decreasing the risk of developing CVD because of their rather high content of eicosapentaenoic and docosahexaenoic fatty acids, optimal n–6/n–3 and polyunsaturated fatty acids/saturated fatty acids ratios, low values of indexes of atherogenicity and thrombogenicity, and high values of hypocholesterolemic/hypercholesterolemic and health-promoting indexes. The results of the present study support the importance of preserving this valuable cattle breed. Actions should be taken to increase their population while retaining their contemporary housing and feeding conditions.
UV-C Light: A Promising Preservation Technology for Vegetable-Based Nonsolid Food Products
Authors: Tchonkouang Rose Daphnee , Lima Alexandre R. , Quintino Andreia C. , Cristofoli Nathana L. , Vieira Margarida C.
Published: August 2023

A variety of bioactive substances present in fruit- and vegetable-processed products have health-promoting properties. The consumption of nutrient-rich plant-based products is essential to address undernutrition and micronutrient deficiencies. Preservation is paramount in manufacturing plant-based nonsolid foods such as juices, purees, and sauces. Thermal processing has been widely used to preserve fruit- and vegetable-based products by reducing enzymatic and microbial activities, thereby ensuring safety and prolonged shelf life. However, the nutritional value of products is compromised due to the deleterious effects of thermal treatments on essential nutrients and bioactive compounds. To prevent the loss of nutrients associated with thermal treatment, alternative technologies are being researched extensively. In studies conducted on nonsolid food, UV-C treatment has been proven to preserve quality and minimize nutrient degradation. This review compiles information on the use of UV-C technology in preserving the nutritional attributes of nonsolid foods derived from fruit and vegetables. The legislation, market potential, consumer acceptance, and limitations of UV-C are reviewed.