bluuebunny/crossref_metadata_2025 huggingface dataset:
Internet of Things Adoption in the Manufacturing Sector: A Conceptual Model from a Multi-Theoretical Perspective
Authors: Ahmetoglu Sehnaz , Che Cob Zaihisma , Ali Nor’Ashikin
Published: March 2023

The manufacturing sector (MS) is considered one of the most important national economic sectors; therefore, global manufacturers strive to apply cutting-edge technologies to gain competitive advantages. The Internet of Things (IoT) has an inherent potential to enhance MS economic growth and maintain its dominance in global markets by using a vast network of smart sensors; nevertheless, IoT technology adoption in the MS remains in the early phase. This research aims to define the antecedents that affect IoT adoption in the MS and propose a conceptual model to explain the adoption intention. Based on an extensive literature review, the proposed model was constructed by three main antecedents: perceived value, perceived benefits, and perceived challenges, and 11 related variables. The model development used a multi-theoretical perspective by integrating three theories: the value-based adoption model, the diffusion of innovation theory, and the technology–organization–environment framework. This study provides decision-makers with valuable insight that promotes IoT adoption in MS and enriches the literature with a new perspective that encourages more studies on IoT adoption in organizations.
Consistency Discrimination and Experimental Study of Rock Discontinuities Based on Borehole Optical Images
Authors: Han Zengqiang , Li Minghong , Wang Chao , Wang Yiteng , Huang Xiaohua
Published: March 2023

The structural plane is a geological interface of a particular scale, and how to determine the extension of the structural plane in space is an essential issue in the study of rock structure. This paper analyses the structural plane information of multiple boreholes collected by the digital panoramic borehole camera system to determine the structural plane consistency. The discriminative conditions of structural plane consistency in multiple boreholes are deduced through mathematical analysis methods such as spatial geometry and vector. The structural planes that may be connected are quickly screened out. The consistency is further judged by combining the relevant characteristics of the structural planes, thus forming a complete method based on digital borehole images. The method has been validated by a series of indoor tests. Structural planes with specific parameters were presented in the test blocks in these tests, and several boreholes were drilled. The experimental results show that (1) the structural plane consistency discrimination method can be applied to both flat structural planes and structural planes with certain undulation angles and roughness; (2) the tilt of the borehole does not affect the structural plane consistency using the borehole camera, and the structural plane consistency can be judged by this method in any two boreholes in space; and (3) the analysis method proposed in this paper can quickly complete the structural plane consistency discrimination, and is also suitable for the consistency discrimination and screening of a large number of structural planes in deep boreholes. Finally, the method based on the optical borehole images is successfully applied in the slope survey of the Xichang iron ore mine.
Artificial Intelligence (AI)-Based Systems for Automatic Skeletal Maturity Assessment through Bone and Teeth Analysis: A Revolution in the Radiological Workflow?
Authors: Caloro Elena , Cè Maurizio , Gibelli Daniele , Palamenghi Andrea , Martinenghi Carlo , Oliva Giancarlo , Cellina Michaela
Published: March 2023

Bone age is an indicator of bone maturity and is useful for the treatment of different pediatric conditions as well as for legal issues. Bone age can be assessed by the analysis of different skeletal segments and teeth and through several methods; however, traditional bone age assessment is a complicated and time-consuming process, prone to inter- and intra-observer variability. There is a high demand for fully automated systems, but creating an accurate and reliable solution has proven difficult. Deep learning technology, machine learning, and Convolutional Neural Networks-based systems, which are rapidly evolving, have shown promising results in automated bone age assessment. We provide the background of bone age estimation, its usefulness and traditional methods of assessment, and review the currently artificial-intelligence-based solutions for bone age assessment and the future perspectives of these applications.
Analysis of the Failure Area of the Slope Using the Slip Line Method
Authors: Shin JunWoo , Baek Yong , Song JungHo
Published: March 2023

Most geotechnical engineers focus on ground strength and stability. However, when determining stability by analyzing the exact strength of the ground, the failure surface is essential. Nevertheless, limited studies have been conducted on the methods to determine the exact failure surface of the ground. This study shows that the failure surface and plastic area can be analyzed using the slip line method based on the slip line and lower bound analysis. To improve existing studies limited to single ground, we analyzed the failure area of heterogeneous ground conditions. The results of the slip line method were compared and verified through FELM, a finite element analysis method to which a lower limit analysis was applied. As a result, the failure area and maximum internal stress according to the ground properties and the slope angle are presented. In addition, the points of the slip line method and the finite element limit analysis are summarized. Finally, we propose limitations and solutions when applying the slip line method to the slope.
Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
Authors: Zhang Liting , Zhang Kun
Published: March 2023

The semantic segmentation of point clouds has significant applications in fields such as autonomous driving, robot vision, and smart cities. As LiDAR technology continues to develop, point clouds have gradually become the main type of 3D data. However, due to the disordered and scattered nature of point cloud data, it is challenging to effectively segment them semantically. A three-dimensional (3D) shape provides an important means of studying the spatial relationships between different objects and their structures in point clouds. Thus, this paper proposes a semi-supervised semantic segmentation network for point clouds based on 3D shape, which we call SBSNet. This network groups and encodes the geometric information of 3D objects to form shape features. It utilizes an attention mechanism and local information fusion to capture shape context information and calculate the data features. The experimental results showed that the proposed method achieved an overall intersection ratio of 85.3% in the ShapeNet dataset and 90.6% accuracy in the ModelNet40 dataset. Empirically, it showed strong performance on par or even better than state-of-the-art models.
Estimation of Obesity Levels with a Trained Neural Network Approach optimized by the Bayesian Technique
Authors: Yagin Fatma Hilal , Gülü Mehmet , Gormez Yasin , Castañeda-Babarro Arkaitz , Colak Cemil , Greco Gianpiero , Fischetti Francesco , Cataldi Stefania
Published: March 2023

Background: Obesity, which causes physical and mental problems, is a global health problem with serious consequences. The prevalence of obesity is increasing steadily, and therefore, new research is needed that examines the influencing factors of obesity and how to predict the occurrence of the condition according to these factors. This study aimed to predict the level of obesity based on physical activity and eating habits using the trained neural network model. Methods: The chi-square, F-Classify, and mutual information classification algorithms were used to identify the most critical factors associated with obesity. The models’ performances were compared using a trained neural network with different feature sets. The hyperparameters of the models were optimized using Bayesian optimization techniques, which are faster and more effective than traditional techniques. Results: The results predicted the level of obesity with average accuracies of 93.06%, 89.04%, 90.32%, and 86.52% for all features using the neural network and for the features selected by the chi-square, F-Classify, and mutual information classification algorithms. The results showed that physical activity, alcohol consumption, use of technological devices, frequent consumption of high-calorie meals, and frequency of vegetable consumption were the most important factors affecting obesity. Conclusions: The F-Classify score algorithm identified the most essential features for obesity level estimation. Furthermore, physical activity and eating habits were the most critical factors for obesity prediction.
A Machine-Learning-Algorithm-Assisted Intelligent System for Real-Time Wireless Respiratory Monitoring
Authors: Zhang Chi , Zhang Lei , Tian Yu , Bao Bo , Li Dachao
Published: March 2023

Respiratory signals are basic indicators of human life and health that are used as effective biomarkers to detect respiratory diseases in clinics, including cardiopulmonary function, breathing disorders, and breathing system infections. Therefore, it is necessary to continuously measure respiratory signals. However, there is still a lack of effective portable electronic devices designed to meet the needs of daily respiratory monitoring. This study presents an intelligent, portable, and wireless respiratory monitoring system for real-time evaluation of human respiratory behaviors. The system consists of a triboelectric respiratory sensor; circuit board hardware for data acquisition, preprocessing, and wireless transmission; a machine learning algorithm for enhancing recognition accuracy; and a mobile terminal app. The triboelectric sensor—fabricated by the screen-printing method—is lightweight, non-invasive, and biocompatible. It provides a clear response to the frequency and intensity of respiratory airflow. The portable circuit board is reusable and cost-effective. The decision tree model algorithm is used to identify the respiratory signals with an average accuracy of 97.2%. The real-time signal and statistical results can be uploaded to a server network and displayed on various mobile terminals for body health warnings and advice. This work promotes the development of wearable health monitoring systems.
A Smart Framework for Managing Natural Disasters Based on the IoT and ML
Authors: Aljohani Fares Hamad , Abi Sen Adnan Ahmed , Ramazan Muhammad Sher , Alzahrani Bander , Bahbouh Nour Mahmoud
Published: March 2023

Natural disasters greatly threaten our lives in addition to adversely affecting all activities. Unfortunately, most solutions currently used in flood management are suffering from many drawbacks related to latency and accuracy. Moreover, the previous solutions consider that the whole city has the same level of vulnerability to damage, while each area in the city may have different topologies and conditions. This study presents a new framework that collects data in real-time about bad weather, which may cause floods, where the framework has a proposed classification algorithm to process sensed data to determine the level of danger in each area of the city. In case of a threat, the framework will send early alerts to users and rescue teams. The framework depends on the Internet of Things (IoT) and fog computing coupled with multiple models of machine learning (Rain Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Deep Learning) to enhance performance and reliability. In addition, the research suggests some assistant services. To prove the efficiency of the framework, we applied the proposed algorithm to real data for the city of Jeddah, Saudi Arabia, for the years 2009 to 2013 and for the years 2018 to 2022. Then, we depended on standard metrics (accuracy, precision, recall, F1-score, and ROC curve). The Rain Forest and Decision Tree achieved the highest accuracy, exceeding 99 percent, followed by the K-Nearest Neighbor. The framework will provide flood detection systems that can predict floods early, send a multi-level warning, and reduce financial, human, and infrastructural damage.
Development of an In-Situ Simulation Device for Testing Deep Pressure-Preserving Coring Tools under High-Temperature and Ultrahigh-Pressure Conditions
Authors: Huang Wei , Xie Heping , Li Jianan , Yang Yang , Li Cong , He Zhiqiang , Li Yihang , Zhang Zetian
Published: March 2023

With the increasing mining depth of deep mineral resources, the underground temperature and pressure also increase, which requires more advanced mining equipment. Therefore, to adapt to the special application scenario of the research and development of pressure-preserving coring tools under the extreme environmental conditions of deep strata, in this study, an in-situ simulation device under high-temperature and ultrahigh-pressure conditions is developed. The principles and methods of applying temperature and pressure to the device are expounded. Furthermore, the two main modules of the device are analyzed and studied experimentally. On the one hand, a segmented simulated coring test cabin is constructed, and pressure testing of the test cabin is carried out. The results show that the test cabin with inner diameters of 150 mm and 500 mm runs stably under the working condition of a pressure up to 190 MPa (considering the influence of temperature of 150 °C), and the cabin remains in the stage of elastic deformation. There is no leakage of pressure or fluid in the whole test process. On the other hand, the performance of the driving module is tested. The results show that the driving module can provide a stable rotation speed of up to 150 r/min when the sealing pressure is 140 MPa. Therefore, the device can be applied to carry out simulated coring test and is suitable for the research and development of pressure-preserving coring tools in deep extreme environments, which may promote the development of deep mining engineering.
Association between Non-Alcoholic Steatohepatitis-Related Hepatocellular Carcinoma and Periodontopathic Bacteria: A Cross-Sectional Pilot Study
Authors: Takuma Ryo , Morozumi Toshiya , Yamamoto Yuko , Kobayashi Takashi , Matsui Takaaki , Yoneda Masato , Kessoku Takaomi , Nogami Asako , Tamura Muneaki , Kamata Yohei , Sugihara Shuntaro , Nomura Yoshiaki , To Masahiro , Minabe Masato , Mitsudo Kenji , Nakajima Atsushi , Komaki Motohiro
Published: March 2023

The incidence of non-alcoholic steatohepatitis (NASH)-related hepatocellular carcinoma (HCC) is increasing annually as the metabolic syndrome factors increase. This study aimed to analyze the involvement of periodontopathic bacteria in NASH-related HCC (NASH-HCC). Questionnaire investigation, periodontal examination, medical examination, and specimen collection (saliva, mouth-rinsed water, and peripheral blood) were performed in 40 patients with NASH and in 20 patients with NASH-HCC. Immunoglobulin (Ig) G antibody titers against Porphyromonas gingivalis (p = 0.031) and Fusobacterium nucleatum (p = 0.003) were significantly higher in the NASH-HCC group than in the NASH group. P. gingivalis and F. nucleatum ratios were higher in the NASH-HCC group than in the NASH group; however, only F. nucleatum ratio was significant (p = 0.009). The Shannon index of salivary bacterial flora was significantly lower in the NASH-HCC group than in the NASH group (p < 0.001). The NASH-HCC group had a significantly lower salivary IgA concentration (p = 0.007) and a slower salivary IgA flow rate (p = 0.003). In all participants, the salivary IgA flow rate and the F. nucleatum ratio showed a significant negative correlation (p = 0.02). Oral P. gingivalis and F. nucleatum were possibly associated with NASH-HCC pathogenesis, and salivary IgA levels were correlated with F. nucleatum.
Capacity Enhancement Analysis of an OAM-OFDM-SMM Multiplexed Free Space Communication System in Atmospheric Turbulence
Authors: Sinha Shivaji , Kumar Chakresh , Armghan Ammar , Singh Mehtab , Alsharari Meshari , Aliqab Khaled
Published: March 2023

To overcome atmospheric turbulence (AT) distortion during signal propagation through the optical link, orbital angular momentum (OAM) mode states employing multiple inputs and multiple outputs (MIMO) techniques have recently gained prominence in free space optical communication (FSO). As the various OAM modes propagate through the free space optical link, signal attenuation and crosstalk may occur, reducing system capacity and increasing the likelihood of bit errors. In this work, our objective is to propose a spectrally efficient, high-speed and channel capacity efficient crosstalk FSO communication system by combining the features of orthogonal frequency division multiplexing (OFDM), spatial mode multiplexing (SMM), and a mode diversity scheme into an existing OAM-FSO communication system. The incorporation of the OFDM-MIMO concept and spatial mode diversity into the existing OAM-MIMO-FSO system is extremely beneficial in enhancing the transmission capacity, mitigating multipath fading and atmospheric turbulence distortions. The Gamma–Gamma (GG) model is used to assess the performance of the proposed system under various atmospheric turbulence conditions in terms of the performance metrics such as BER vs. number of OAM states for different refractive index structure and Rytov constants, link distance, and an optical signal to noise ratio (OSNR). A FEC limit of 3.8 × 10−3 and a maximum link distance of 2 km are set to evaluate these performance parameters. Finally, the transmission capacity of the proposed system is compared to that of the existing MIMO and OAM-SMM-MIMO systems for different OSNR values under atmospheric turbulence conditions for the OAM state of l = +1, yielding an overall improvement of 3.3 bits/s/Hz compared to conventional MIMO systems and 1.6 bits/s/Hz for the OAM-SMM-MIMO system.
Significance of Annealing Twins in Laser Ultrasonic Measurements of Grain Size in High-Strength Low-Alloy Steels
Authors: Nyyssönen Tuomo , Malmström Mikael , Lindell David , Jansson Anton , Lönnqvist Johan , Bäcke Linda , Hutchinson Bevis
Published: March 2023

In this study, we demonstrate the significance of austenite annealing twin boundaries when calibrating laser ultrasonic measurements for gauging austenite grain size in situ during the thermomechanical processing of high-strength low-alloy steels. Simple calculations show how differences in twinning density can lead to errors in grain size measurements if twins are disregarded during calibration and the method is used for a broad range of steels. Conversely, when calibration is performed using alloys with a metastable austenite microstructure at room temperature, the same calibration is suitable for a broad range of HSLA steels, provided that annealing twins are taken into account. Since light optical microscopy does not allow the characterization of annealing twins in low-alloy steel, the verification of the laser ultrasonic results was conducted using the novel approach of comparing the twinned grain sizes obtained using the ultrasonic method in low-alloy steels with the austenite grain maps reconstructed from martensite orientation maps measured using electron backscatter diffraction. Finally, we show how differences in twinning density occur even for alloys with a roughly similar stacking fault energy, further highlighting the importance of annealing twins in the calibration of laser ultrasonic measurements for industrial use.
On the Regional Temperature Series Evolution in the South-Eastern Part of Romania
Authors: Bărbulescu Alina
Published: March 2023

In the context of reported climate variations in different regions of the world, this work investigates the evolution of the temperature series in the Dobrogea region, Romania, using the maximum, average, and minimum annual temperature series from 1965 to 2005. The Mann–Kendall test and Sen’s slope emphasized increasing trends of nine (out of ten) minimum temperature series (nine of them at significance levels less than or equal to 0.05, and two at 0.1), three average temperature series (at a significance level of 0.1), and five maximum temperature series (at significance levels less than or equal to 0.05). The selection of the representative series at the regional scale, called the ‘Regional series’, was performed using two algorithms proposed by the author that are easy to employ, even by individuals without deep knowledge in the field. The first (called MPPM) was initially introduced for evaluating the ‘Regional precipitation series’, and the second is a version of MPPM based on clustering the data series. Comparisons with the series from the ROCADA database were performed to prove the algorithms’ performances. The best results were obtained by running the second algorithm with two clusters, for the minimum and maximum temperature series, and with three clusters, for the average temperature series. In comparison with the initial data series, the average MAEs were, respectively, 1.39, 0.37, and 0.84 for the minimum, average, and maximum series, and the corresponding average MSEs were, respectively, 1.49, 0.41, and 0.93. Comparison of the ‘Regional series’ with the series from ROCADA led to a decrease in the modeling errors, with the best ones corresponding to the average ‘Regional series’—MAE = 0.36 and average MSE = 0.25.
Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
Authors: Liu Dong , Zhang Lingrong , Yang Jinglin , Lin Anping
Published: March 2023

Scoliosis is a common spinal deformity that seriously affects patients’ physical and mental health. An accurate Lenke classification is greatly significant for evaluating and treating scoliosis. Currently, the clinical diagnosis mainly relies on manual measurement; however, using computer vision assists with an intelligent diagnosis. Due to the complex rules of Lenke classification and the characteristics of medical imaging, the fully automated Lenke classification of scoliosis remains a considerable challenge. Herein, a novel Lenke classification method for scoliosis using X-rays based on segmentation networks and adaptive shape descriptors is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We used Unet++ to segment the vertebrae and designed a post-processing operation to improve the segmentation effect. Then, we proposed a new shape descriptor to extract the shape features for segmented vertebrae in greater detail. Finally, we proposed a new Lenke classification framework for scoliosis that contains two schemes based on Cobb angle measurement and shape classification, respectively. After rigorous experimental evaluations on a public dataset, our method achieved the best performance and outperformed other sophisticated approaches.
3D Evaluation of Upper Airway Morphological Changes in Growing Patients with Class II Malocclusion Using Sander Bite Jumping Appliance
Authors: Faccioni Paolo , Butera Andrea , Bazzanella Silvia , Albanese Massimo , Gallo Simone , Pascadopoli Maurizio , Scribante Andrea , Pardo Alessia
Published: March 2023

Class II malocclusion due to mandibular retrognathia is associated with a posterior positioning of the tongue and the hyoid bone, reducing the oropharyngeal volume. This could be a contributing factor to the development of respiratory and cardiovascular problems. This study evaluates the oropharyngeal volume variation in 13 patients with class II malocclusion undergoing functional orthopedic treatment with Sander Bite Jumping Appliance (SBJ). CBCT scans were performed before treatment (T0) and approximately after 12.5 months (T1): the retropalatal volume and retroglossal volume were quantified in mm3 using a segmentation software. At T1, the retropalatal volume increased in 2523 ± 2088 mm3, and the retroglossal volume increased in 2258 ± 1717 mm3. Both values were statistically significant (p < 0.05). This widening of the airways may allow prevention and treatment of sleep-disordered breathing, including obstructive sleep apnea syndrome.
Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
Authors: Amur Zaira Hassan , Kwang Hooi Yew , Bhanbhro Hina , Dahri Kamran , Soomro Gul Muhammad
Published: March 2023

In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.
Evaluating a Proposed E-Government Stage Model in Terms of Personal Data Protection
Authors: Muhammad Hemin , Hromada Martin
Published: March 2023

In today’s era, the e-government plays a significant role in providing better services to citizens. As a result, citizens are crucial in ensuring the success of the e-government and are involved in the government’s policies. Hence, the protection of personal information must be taken into consideration when designing any e-government model. Previous studies have shown that privacy and security are the main challenges in implementing an e-government in developing countries. This study aims to evaluate an e-government stage model that is proposed based on protecting personal information in developing countries. The methodology of this study uses SOAR (Strengths, Opportunities, Aspirations, and Results) analysis to evaluate the stages and combines it with the Analytic Hierarchy Process (AHP) to determine the feasibility of the model. The results of the study show that the model is acceptable and suitable for adoption. Therefore, the model is a viable option for implementing an e-government in developing countries.
RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis
Authors: Tan Kian Long , Lee Chin Poo , Lim Kian Ming
Published: March 2023

This paper proposes a novel hybrid model for sentiment analysis. The model leverages the strengths of both the Transformer model, represented by the Robustly Optimized BERT Pretraining Approach (RoBERTa), and the Recurrent Neural Network, represented by Gated Recurrent Units (GRU). The RoBERTa model provides the capability to project the texts into a discriminative embedding space through its attention mechanism, while the GRU model captures the long-range dependencies of the embedding and addresses the vanishing gradients problem. To overcome the challenge of imbalanced datasets in sentiment analysis, this paper also proposes the use of data augmentation with word embeddings by over-sampling the minority classes. This enhances the representation capacity of the model, making it more robust and accurate in handling the sentiment classification task. The proposed RoBERTa-GRU model was evaluated on three widely used sentiment analysis datasets: IMDb, Sentiment140, and Twitter US Airline Sentiment. The results show that the model achieved an accuracy of 94.63% on IMDb, 89.59% on Sentiment140, and 91.52% on Twitter US Airline Sentiment. These results demonstrate the effectiveness of the proposed RoBERTa-GRU hybrid model in sentiment analysis.
Event Detection Using a Self-Constructed Dependency and Graph Convolution Network
Authors: He Li , Meng Qingxin , Zhang Qing , Duan Jianyong , Wang Hao
Published: March 2023

The extant event detection models, which rely on dependency parsing, have exhibited commendable efficacy. However, for some long sentences with more words, the results of dependency parsing are more complex, because each word corresponds to a directed edge with a dependency parsing label. These edges do not all provide guidance for the event detection model, and the accuracy of dependency parsing tools decreases with the increase in sentence length, resulting in error propagation. To solve these problems, we developed an event detection model that uses a self-constructed dependency and graph convolution network. First, we statistically analyzed the ACE2005 corpus to prune the dependency parsing tree, and combined the named entity features in the sentence to generate an undirected graph. Second, we implemented an enhanced graph convolution network using the multi-head attention mechanism to understand the representation of nodes in the graph. Finally, a gating mechanism combined the semantic and structural dependency information of the sentence, enabling us to accomplish the event detection task. A series of experiments conducted on the ACE2005 corpus demonstrates that the proposed method enhances the performance of the event detection model.
Thermal Behavior of Biodegradable Compositions of Polylactide and Poly(3-hydroxybutyrate) with Chitosan and the Effect of UV Radiation on Their Structure
Authors: Rogovina Svetlana , Lomakin Sergei , Usachev Sergey , Yakhina Anastasia , Zhorina Lubov , Berlin Alexander
Published: March 2023

The negative influence of water pollution by heavy metals ions on human health represents a serious ecological problem which requires effective methods in the search for its solution. The creation of eco-friendly biodegradable polymer materials capable of performing the sorption of the water media of heavy metals followed by decomposition into harmless substances after the end of their service life presents an actual task. To this aim, binary compositions synthesized from natural raw polyesters polylactide (PLA) and poly(3-hydroxybutyrate) (PHB) with polysaccharide chitosan, corresponding to these requirements, were obtained in the liquid phase. The polyesters have mechanical characteristics close to the characteristics of synthetic polymers, while the chitosan containing the amino groups is capable of performing the sorption of heavy metals. The use of compositions on their base allows one to create the new inexpensive biodegradable sorbents stable in aqueous media as well as apply them as packing materials. The sorption capacity of PLA–chitosan and PHB–chitosan compositions in relation to iron ions from aqueous solutions was explored by a method of X-ray fluorescence analysis and it was established that the sorption of Fe3+ ions by PHB–chitosan composition is more than twice as high as that by the PLA–chitosan composition (2.30 and 0.66 wt. %, correspondingly, after sorption from 0.008 mol/L FeCl3 solution during 24 h). A comparative study of thermophysical parameters and the degree of crystallinity of PLA and PHB, as well as in their initial compositions and compositions, containing sorbed iron ions, was carried out by DSC method. The DSC analysis of the PLA–chitosan and PHB–chitosan compositions, containing sorbed iron ions, showed a slight decrease in the values of Tg, Tcc, and Tm as well as an increase in the enthalpy of cold crystallization and a reduction in the degree of crystallinity of these polyesters. At the same time, an increasing of the thermal stability of polyester compositions in the presence of iron ions was established. The influence of UV irradiation on the structure of PLA and PHB for 2, 5, 24, and 144 h was analyzed by FTIR spectroscopy and significant changes in the spectrum were observed. Based on the analysis of the IR spectra of PHB and PLA, it was concluded that, under the action of UV radiation, the destruction of ester bonds takes place, which is expressed in the appearance of intense bands characterizing the formation of new structural units, resulting in the decrease in the molecular weight of polyesters.
Evaluation of Chronic Kidney Disease Risk Factors after Radical Nephrectomy
Authors: Makevičius Jurijus , Pajaurytė Akvilė , Samuilis Artūras , Lukšaitė-Lukštė Raminta , Jasiūnas Eugenijus , Jankevičius Feliksas , Miglinas Marius
Published: March 2023

Intraoperative hypotension (IOH) and loss of blood during radical nephrectomy (RN) cause postoperative clinically significant renal dysfunction, which after 12 months can cause a reduction in serum creatinine clearance of <60 mL/min. We conducted a prospective study of 93 adult patients in which we investigated the risk factors for developing chronic kidney disease (CKD) after RN. Forty-six (49.5%) patients had CKD, and of them, 43 patients had acute kidney injury (AKI) 48 h after surgery. Sixty-six (73.1%) of the postoperative AKI patients had CKD upstage. With each 1 mL estimated blood loss during RN (OR 1.01, p < 0.001), IOH was evaluated as the main risk factor of postoperative CKD development (OR 1.09, p < 0.01). Dunn’s t-test revealed that only clinically significant AKI had a main effect (g = −1.08, p < 0.0001) on renal function 1 year after RN. A higher preoperative estimated glomerular filtration rate (eGFR), OR 0.89, p = 0.02, and contralateral kidney CT volume (OR 0.97, p = 0.04) had a clinically significantly decreased risk of postoperative CKD. Risk factors of AKI with CKD upstage were a small contralateral kidney CT volume (OR 46.70), NLR > 3.5 (OR 1.42), higher primary eGFR (OR 1.13) and longer IOH (OR 1.05), and for all of these, p < 0.03. A half of all patients after RN are at increased risk of CKD. Longer IOH and increased blood loss during RN are significant risk factors for CKD. Clinically significant postoperative AKI is related with a developed risk for postoperative eGFR decline and the presence of CKD 12 months after RN, and can be predicted by NLR > 3.5. A higher preoperative eGFR and contralateral kidney CT volume reduces the risk of postoperative CKD.
A Survey of Secure Time Synchronization
Authors: Weng Ying , Zhang Yiming
Published: March 2023

Today, the use of wireless sensor networks has grown rapidly; however, wireless sensor networks are prone to receiving cyber-physical attacks. Time synchronization is a fundamental requirement for protocols in wired and wireless sensor network applications; hence, secure time synchronization is also crucial. This paper presents an introduction to time synchronization, including the concepts, challenges, and requirements of time synchronization protocols. The scope of the paper includes both software- and hardware-based protocols. Then, different time synchronization methods are analyzed. Moreover, research progress in secure time synchronization is reviewed. The survey also discusses the weaknesses of current secure time synchronization protocols and provides suggestions for future research directions. This survey aims to highlight research progress and trends in time synchronization and secure time synchronization.
Multi-Analytical Investigation on a Renaissance Polychrome Earthenware Attributed to Giovanni Antonio Amadeo
Authors: Guglielmi Vittoria , Lombardi Chiara Andrea , Fiocco Giacomo , Comite Valeria , Bergomi Andrea , Borelli Mattia , Azzarone Monica , Malagodi Marco , Colella Mario , Fermo Paola
Published: March 2023

This research aimed to characterise pigments used to decorate a polychrome earthenware bas-relief of the 15th century entitled “Madonna with Child, Saint Catherine of Siena, and a Carthusian Prior”, attributed to Giovanni Antonio Amadeo (Pavia, 1447–Milan, 1522) and owned by the Sforzesco Castle Museum of Milan. The artwork underwent a cleaning procedure whose aims were the removal of the dark coating that obscured its surface and restoration work that could bring back its original features. Before the cleaning, six microsamples were collected and analysed using optical microscopy (OM), scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDXS), and Fourier-transform infrared microspectroscopy in ATR mode (ATR-FTIR), providing the restorers with decisive information on the materials underlying the coating. After the cleaning, the terracotta appeared vibrantly coloured, mainly with bright red, blue, green, black, and white tones. Then, some in situ, non-destructive, spectroscopic measurements were performed by a portable Raman spectrometer on some of the areas that could not otherwise have been sampled. The analyses revealed the presence of natural pigments, including lead white, azurite, yellow ochre, carbon black, calcite, cinnabar, and gypsum. For Madonna’s mantle, cobalt and Prussian blue were employed. Furthermore, the presence of barium sulphate was widely evidenced on the bas-relief. Albeit cobalt blue is of synthetic origin, its presence is compatible with the 15th-century palette, whereas Prussian blue and barium sulphate could be imputed to a previous restoration. Finally, the use of true gold for the background of the earthenware attests to the artwork’s importance and value.
Scene Text Detection Based on Multi-Headed Self-Attention Using Shifted Windows
Authors: Huang Baohua , Feng Xiaoru
Published: March 2023

Scene text detection has become a popular topic in computer vision research. Most of the current research is based on deep learning, using Convolutional Neural Networks (CNNs) to extract the visual features of images. However, due to the limitations of convolution kernel size, CNNs can only extract local features of images with small perceptual fields, and they cannot obtain more global features. In this paper, to improve the accuracy of scene text detection, a feature enhancement module is added to the text detection model. This module acquires global features of an image by computing the multi-headed self-attention of the feature map. The improved model extracts local features using CNNs, while extracting global features through the feature enhancement module. The features extracted by both of these are then fused to ensure that visual features at different levels of the image are extracted. A shifted window is used in the calculation of the self-attention, which reduces the computational complexity from the second power of the input image width-height product to the first power. Experiments are conducted on the multi-oriented text dataset ICDAR2015 and the multi-language text dataset MSRA-TD500. Compared with the pre-improvement method DBNet, the F1-score improves by 0.5% and 3.5% on ICDAR2015 and MSRA-TD500, respectively, indicating the effectiveness of the model improvement.
Connections between Metallic Nanoparticles and Chlorin e6—An Overview of Physicochemical and Biological Properties and Prospective Medical Applications
Authors: Koczorowski Tomasz , Glowacka-Sobotta Arleta , Michalak Maciej , Mlynarczyk Dariusz T. , Güzel Emre , Goslinski Tomasz , Sobotta Lukasz
Published: March 2023

Photodynamic therapy is a non-invasive method of treatment for both neoplastic diseases and miscellaneous non-cancerous illnesses. It is complementary and, in some way, counter to various traditional treatment techniques, including chemotherapy, radiotherapy, and surgery. To date, various types of nanoparticles and compounds, including those belonging to the porphyrinoid group, have been researched in terms of future applications in technology and medicine. Among them, chlorins and their conjugates, combined with metallic nanoparticles, deserve special attention due to their enhanced photodynamic activity and the accompanied synergic photothermal effect. Many hybrid nanosystems reveal increased cellular uptake and improved stability and, therefore, can be applied in enhanced MRI imaging, as well as in targeting therapy. This review is focused on conjugates of metallic nanoparticles and chlorins, having in mind prospective applications as photosensitizers in multimodal neoplastic therapy, as well as tumor diagnosis.
Effect of Yoghourt Starter Culture and Nickel Oxide Nanoparticles on the Activity of Enterotoxigenic Staphylococcus aureus in Domiati Cheese
Authors: Ahmed Ahmed A.-H. , Maharik Nagah , Valero Antonio , Elsherif Walaa , Kamal Sahar Mahmoud
Published: March 2023

Domiati cheese is the most popular type of white soft cheese in Egypt. Staphylococcus aureus is a common microorganism that can easily contaminate Domiati cheese during processing and distribution. Enterotoxigenic S. aureus strains produce staphylococcal enterotoxins (SE) that have been involved in food poisoning outbreaks worldwide. The aim of the present study was to examine the inhibitory effect of yoghourt starter culture and nickel oxide nanoparticles (NiO NPs) on the development of the enterotoxigenic S. aureus together with the enterotoxin production during the manufacturing and storage of Domiati cheese. Fresh cow’s milk was inoculated with S. aureus in a count of six log CFU/mL with the addition of either yoghourt starter culture or NiO Nps. The cytotoxicity of NiO NPs on normal human epithelial cells (HEC) was assessed using the MTT assay. In the current study, the inoculated milk was used for making Domiati cheese and the survival Weibull and log-linear models were fitted to the observed data. The obtained results showed that the mean log count of S. aureus decreased one week earlier by using yoghourt starter culture. Staphylococcal enterotoxin A (SEA) was identified only in the control cheese. Notably, Domiati cheese contained MIC of NiO NPs (35 µg/mL), which resulted in a significant decrease in S. aureus counts since at day 21 of cheese ripening it was not detected (<10 CFU/g). Overall, the current study indicated that the addition of yoghourt starter culture and NiO NPs during the processing of Domiati cheese could be useful candidates against S. aureus and enterotoxin production in the dairy industry.
EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
Authors: Yu Seunghyun , Son Seungwook , Ahn Hanse , Baek Hwapyeong , Nam Kijeong , Chung Yongwha , Park Daihee
Published: March 2023

While detecting surrounding vehicles in autonomous driving is possible with advances in object detection using deep learning, there are cases where small vehicles are not being detected accurately. Additionally, real-time processing requirements must be met for implementation in autonomous vehicles. However, detection accuracy and execution speed have an inversely proportional relationship. To improve the accuracy–speed tradeoff, this study proposes an ensemble method. An input image is downsampled first, and the vehicle detection result is acquired for the downsampled image through an object detector. Then, warping or upsampling is performed on the Region of Interest (RoI) where the small vehicles are located, and the small vehicle detection result is acquired for the transformed image through another object detector. If the input image is downsampled, the effect on the detection accuracy of large vehicles is minimal, but the effect on the detection accuracy of small vehicles is significant. Therefore, the detection accuracy of small vehicles can be improved by increasing the pixel sizes of small vehicles in the transformed image more than the given input image. To validate the proposed method’s efficiency, the experiment was conducted with Argoverse vehicle data used in an autonomous vehicle contest, and the accuracy–speed tradeoff improved by up to a factor of two using the proposed ensemble method.
An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
Authors: Sun Gen , Liu Ning , Wang Bingnan , Xiang Maosheng , Shi Ruihua , Li Lanyu , Wang Yachao
Published: March 2023

Interferometric synthetic aperture radar (InSAR) is an integrated navigation technique that can be used for aircraft positioning and attitude retrieval, regardless of weather conditions. The key aspect of the entire system is interference fringe matching, which has not been extensively researched in existing literature. To address this gap, this paper proposes a terrain-feature-based interference fringe-matching algorithm. The proposed algorithm first extracts mountain line features from the interference fringes and identifies mountain branch points as key points for feature matching. A threshold is set to eliminate false detections of mountain branch points caused by phase mutation. Matching is then carried out by combining the mountain line features and curvature design feature descriptors of the area around a branch point. The proposed algorithm is verified using interference fringe data obtained from an actual flight experiment in Inner Mongolia, China, compared to a reference interference fringe dataset with errors. The results show that, under the condition of position error, the proposed algorithm yields 72 matching inliers with a precision of 0.103, a recall of 0.128, and an F1-Score of 0.144. Compared to traditional algorithms, our proposed algorithm significantly improves the problem of mismatch and opens up new possibilities for downstream interference fringe matching navigation technology. Furthermore, the proposed algorithm provides a new approach for remote sensing image matching using terrain features.
RSII: A Recommendation Algorithm That Simulates the Generation of Target Review Semantics and Fuses ID Information
Authors: Ren Qiulin , Qin Jiwei , Shao Jianjie , Song Xiaoyuan
Published: March 2023

The target review has been proven to be able to predict the target user’s rating of the target item. However, in practice, it is difficult to obtain the target review promptly. In addition, the target review and the rating may sometimes be inconsistent (such as preference reviews and low ratings). There is currently a lack of research on the above issues. Therefore, this paper proposed a Recommendation algorithm that Simulates the generation of target review semantics and fuses the ID Information (RSII). Specifically, based on the characteristics of the target review available during the model training, this paper designed a teacher module and a review semantics learning module. The teacher module learned the semantics of the target review and guided the review semantics learning model to learn these semantics. Then, this study used the fusion module to dynamically fuse the target review semantics and the ID information, enriching the representation of predictive features, thereby, alleviating the problem of inconsistency between the target review and the rating. Finally, the RSII model was extensively tested on three public datasets. The results showed that compared with seven of the latest and most advanced models, the RSII model improved the MSE metric by 8.81% and the MAE metric by 10.29%.