bluuebunny/crossref_metadata_2025 huggingface dataset:
Optical and Electrical Properties of Low-Dimensional Crystalline Materials: A Review
Authors: Pura Jose Luis
Published: January 2023

Low-dimensional materials have been revolutionary in both the technological and research fields over the last decades. Since the discovery of graphene in 2004, and thanks to the technological improvements in nanotechnology achieved during this last century, the number of low-dimensional materials under research and their potential applications have not stopped increasing. In this review, we present a comprehensive tour of the principal 2D and 1D materials that compose the current state of the art and also the technological applications derived from them. In both cases, the focus will be on their optical and electrical properties, as well as the potential applications on novel photonic, electronic, or optoelectronic devices. For 2D materials, we will focus on a brief review of graphene-like materials, giving more emphasis to graphene derivatives, hexagonal boron nitride, and transition metal dichalcogenides. Regarding 1D materials, we will aim at metallic and semiconductor nanowires. Nevertheless, interesting 2D and 1D materials are mentioned in each section. The topic will be introduced using the related origin of their unique capabilities as a common thread. At the same time, we will try to remark on the differences and similarities between both groups and their physical relationship.
Effects of Pore Size Parameters of Titanium Additively Manufactured Lattice Structures on the Osseointegration Process in Orthopedic Applications: A Comprehensive Review
Authors: Alkentar Rashwan , Kladovasilakis Nikolaos , Tzetzis Dimitrios , Mankovits Tamás
Published: January 2023

Architected materials are increasingly applied in form of lattice structures to biomedical implant design for the purpose of optimizing the implant’s biomechanical properties. Since the porous design of the lattice structures affects the resulting properties of the implant, its parameters are being investigated by numerous research articles. The design-related parameters of the unit cells for a strut-architected material are mainly the pore size and the strut thickness. Until today, researchers have not been able to decide on the perfect values of the unit cell parameters for the osseointegration process and tissue regeneration. Based on in vivo and in vitro experiments conducted in the field, researchers have suggested a range of values for the parameters of the lattice structures where osseointegration is in acceptable status. The present study presents a comprehensive review of the research carried out until today, experimenting and proposing the optimum unit cell parameters to generate the most suitable lattice structure for the osseointegration procedure presented in orthopedic applications. Additional recommendations, research gaps, and instructions to improve the selection process of the unit cell parameters are also discussed.
Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set
Authors: Maletzky Alexander , Hofer Nikolaus , Thumfart Stefan , Bruckmüller Karin , Kasper Johannes
Published: January 2023

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated data set of images of almost all traffic signs on Austrian highways in 2014, and corresponding images of full traffic scenes they are contained in. Altogether, the data set consists of almost 7500 scene images with more than 28,000 detailed annotations of more than 100 distinct traffic sign classes. It covers diverse environments, ranging from urban to rural and mountainous areas, and includes many images recorded in tunnels. We further evaluate state-of-the-art traffic sign detectors and classifiers on ATSD to establish baselines for future experiments. The data set and our baseline models are freely available online.
Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
Authors: Irmici Giovanni , Cè Maurizio , Caloro Elena , Khenkina Natallia , Della Pepa Gianmarco , Ascenti Velio , Martinenghi Carlo , Papa Sergio , Oliva Giancarlo , Cellina Michaela
Published: January 2023

Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.
Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics
Authors: Mayrose Hilda , Bairy G. Muralidhar , Sampathila Niranjana , Belurkar Sushma , Saravu Kavitha
Published: January 2023

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS.
THSD7A Positivity Is Associated with High Expression of FAK in Prostate Cancer
Authors: Flockerzi Fidelis Andrea , Hohneck Johannes , Saar Matthias , Bohle Rainer Maria , Stahl Phillip Rolf
Published: January 2023

Prostate cancer is one of the most common malignancies, and there are a wide range of treatment options after diagnosis. Most prostate cancers behave in an indolent manner. However, a given sub-group has been shown to exhibit aggressive behavior; therefore, it is desirable to find novel prognostic and predictive (molecular) markers. THSD7A expression is significantly associated with unfavorable prognostic parameters in prostate cancer. FAK is overexpressed in several tumor types and is believed to play a role in tumor progression and metastasis. Furthermore, there is evidence that THSD7A might affect FAK-dependent signaling pathways. To examine whether THSD7A expression has an impact on the expression level of FAK in its unphosphorylated form, a total of 461 prostate cancers were analyzed by immunohistochemistry using tissue microarrays. THSD7A positivity and low FAK expression were associated with adverse pathological features. THSD7A positivity was significantly associated with high FAK expression. To our knowledge we are the first to show that THSD7A positivity is associated with high FAK expression in prostate cancer. This might be proof of the actual involvement of THSD7A in FAK-dependent signaling pathways. This is of special importance because THSD7A might also serve as a putative therapeutic target in cancer therapy.
An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis
Authors: Zhan Xiangbing , Liu Jun , Long Huiyun , Zhu Jun , Tang Haoyu , Gou Fangfang , Wu Jia
Published: January 2023

Bone malignant tumors are metastatic and aggressive, with poor treatment outcomes and prognosis. Rapid and accurate diagnosis is crucial for limb salvage and increasing the survival rate. There is a lack of research on deep learning to segment bone malignant tumor lesions in medical images with complex backgrounds and blurred boundaries. Therefore, we propose a new intelligent auxiliary framework for the medical image segmentation of bone malignant tumor lesions, which consists of a supervised edge-attention guidance segmentation network (SEAGNET). We design a boundary key points selection module to supervise the learning of edge attention in the model to retain fine-grained edge feature information. We precisely locate malignant tumors by instance segmentation networks while extracting feature maps of tumor lesions in medical images. The rich contextual-dependent information in the feature map is captured by mixed attention to better understand the uncertainty and ambiguity of the boundary, and edge attention learning is used to guide the segmentation network to focus on the fuzzy boundary of the tumor region. We implement extensive experiments on real-world medical data to validate our model. It validates the superiority of our method over the latest segmentation methods, achieving the best performance in terms of the Dice similarity coefficient (0.967), precision (0.968), and accuracy (0.996). The results prove the important contribution of the framework in assisting doctors to improve the accuracy of diagnosis and clinical efficiency.
Adult Acquired Flatfoot Deformity: A Narrative Review about Imaging Findings
Authors: Polichetti Chiara , Borruto Maria Ilaria , Lauriero Francesco , Caravelli Silvio , Mosca Massimiliano , Maccauro Giulio , Greco Tommaso , Perisano Carlo
Published: January 2023

Adult acquired flatfoot deformity (AAFD) is a disorder caused by repetitive overloading, which leads to progressive posterior tibialis tendon (PTT) insufficiency. It mainly affects middle-aged women and occurs with foot pain, malalignment, and loss of function. After clinical examination, imaging plays a key role in the diagnosis and management of this pathology. Imaging allows confirmation of the diagnosis, monitoring of the disorder, outcome assessment and complication identification. Weight-bearing radiography of the foot and ankle are gold standard for the diagnosis of AAFD. Magnetic Resonance Imaging (MRI) is not routinely needed for the diagnosis; however, it can be used to evaluate the spring ligament and the degree of PTT damage which can help to guide surgical plans and management in patients with severe deformity. Ultrasonography (US) can be considered another helpful tool to evaluate the condition of the PTT and other soft-tissue structures. Computed Tomography (CT) provides enhanced, detailed visualization of the hindfoot, and it is useful both in the evaluation of bone abnormalities and in the accurate evaluation of measurements useful for diagnosis and post-surgical follow-up. Other state-of-the-art imaging examinations, like multiplanar weight-bearing imaging, are emerging as techniques for diagnosis and preoperative planning but are not yet standardized and their scope of application is not yet well defined. The aim of this review, performed through Pubmed and Web of Science databases, was to analyze the literature relating to the role of imaging in the diagnosis and treatment of AAFD.
Biportal Endoscopic Radiofrequency Ablation of the Sacroiliac Joint Complex in the Treatment of Chronic Low Back Pain: A Technical Note with 1-Year Follow-Up
Authors: Tseng Chun , Chen Kuo-Tai , Fong Yi-Chin , Lin Chung-Wei , Sun Li-Wei , Chen Chien-Min , Wang Guan-Chyuan
Published: January 2023

Background: Sacroiliac joint (SIJ) pain is a common source of low back pain. Previously reported management strategies for this pain include conservative treatment, SIJ injection, radiofrequency denervation ablation, and SIJ fusion. Herein, we describe the use of biportal endoscopic radiofrequency ablation (BERA) to treat patients with low back pain. Methods: We included 16 patients who underwent BERA from April 2018 to June 2020. We marked the S1, S2, and S3 foramina and the SIJ line under fluoroscopy. Skin entry points were positioned at 0.5 cm medial to the SIJ line and at the level of the S1 and S2 foramina. Under local anesthesia, we introduced a 30° arthroscope with a 4 mm diameter through the viewing portal; surgical instruments were inserted through another caudal working portal. We ablated the lateral branches of the S1–S3 foramina and L5 dorsal ramus, which were the sources of SIJ pain. Results: Clinically relevant improvements in both visual analog scale and Oswestry Disability Index scores were noted at 1-, 6-, and 12-month follow-up time points after surgery. The overall patient satisfaction score was 89.1%. Conclusions: BERA for SIJ pain treatment has the advantage of directly identifying and ablating the innervating nerve to the joint. Through this technique, an expanded working angle can be obtained compared with traditional single-port endoscopy. Our study demonstrated promising preliminary results.
OCT and OCT Angiography Update: Clinical Application to Age-Related Macular Degeneration, Central Serous Chorioretinopathy, Macular Telangiectasia, and Diabetic Retinopathy
Authors: Zhang Lyvia , Van Dijk Elon H. C. , Borrelli Enrico , Fragiotta Serena , Breazzano Mark P.
Published: January 2023

Similar to ultrasound adapting soundwaves to depict the inner structures and tissues, optical coherence tomography (OCT) utilizes low coherence light waves to assess characteristics in the eye. Compared to the previous gold standard diagnostic imaging fluorescein angiography, OCT is a noninvasive imaging modality that generates images of ocular tissues at a rapid speed. Two commonly used iterations of OCT include spectral-domain (SD) and swept-source (SS). Each comes with different wavelengths and tissue penetration capacities. OCT angiography (OCTA) is a functional extension of the OCT. It generates a large number of pixels to capture the tissue and underlying blood flow. This allows OCTA to measure ischemia and demarcation of the vasculature in a wide range of conditions. This review focused on the study of four commonly encountered diseases involving the retina including age-related macular degeneration (AMD), diabetic retinopathy (DR), central serous chorioretinopathy (CSC), and macular telangiectasia (MacTel). Modern imaging techniques including SD-OCT, TD-OCT, SS-OCT, and OCTA assist with understanding the disease pathogenesis and natural history of disease progression, in addition to routine diagnosis and management in the clinical setting. Finally, this review compares each imaging technique’s limitations and potential refinements.
Successful Detection of the Characteristics of Tear Film Breakup Appearing Immediately after Eye Opening by Videokeratography with a Newly-Developed Indicator
Authors: Yokoi Norihiko , Kusada Natsuki , Kato Hiroaki , Furusawa Yuki , Sotozono Chie , Georgiev Georgi As.
Published: January 2023

Spot break (SB), a tear film breakup (TFBU) subtype seen in decreased wettability dry eye (DE), is characterized by a spot-like TFBU that appears immediately after eye opening. It is sometimes difficult to detect using currently available devices for evaluating non-invasive TFBU. The purpose of this study was to investigate the effectiveness of using a newly developed videokeratography indicator for detecting SB. The study involved 44 eyes of 44 DE patients (21 eyes with SB (SB group) and 23 eyes with random break in which fluorescein breakup time was ≤ 5 s (s) (RB group)). All eyes were examined using videokeratography, with digital Meyer-ring images being obtained. By calculation of the degree of luminance blur on the cornea in the Meyer-ring images, termed ‘disturbance value’ (DV), DVs at 0 s (DV(0)]), 2 s (DV(2)), and 5 s (DV(5)) after eye opening, and the changes of DV between each time, were compared between the SB and RB groups. Results: No significant differences in DV(2) and DV(5) and the rate of change between DV(2) and DV(5) were found between the two groups. However, DV(0) and rate of change between DV(0) and DV(2) in the SB group were significantly greater (p < 0.001) than those in the RB group. SB characteristics were successfully detected by videokeratography using a new videokeratography DV indicator.
Updates on HPV Vaccination
Authors: Illah Ojone , Olaitan Adeola
Published: January 2023

Cervical cancer still poses a significant global challenge. Developed countries have mitigated this challenge by the introduction of structured screening programmes and, more recently, the HPV vaccine. Countries that have successfully introduced national HPV vaccination programmes are on course for cervical cancer elimination in a few decades. In developing countries that lack structured screening and HPV vaccination programmes, cervical cancer remains a major cause of morbidity and mortality. The HPV vaccine is key to addressing the disproportionate distribution of cervical cancer incidence, with much to be gained from increasing vaccine coverage and uptake globally. This review covers the history and science of the HPV vaccine, its efficacy, effectiveness and safety, and some of the considerations and challenges posed to the achievement of global HPV vaccination coverage and the consequent elimination of cervical cancer.
Assessment of Calcium Score Cutoff Point for Clinically Significant Aortic Stenosis on Lung Cancer Screening Program Low-Dose Computed Tomography—A Cross-Sectional Analysis
Authors: Klein-Awerjanow Kaja , Rzyman Witold , Dziedzic Robert , Fijalkowska Jadwiga , Spychalski Piotr , Szurowska Edyta , Fijalkowski Marcin
Published: January 2023

Low-dose computed tomography (LDCT) is predominantly applied in lung cancer screening programs. Tobacco smoking is the main risk factor for developing lung cancer but is also common for cardiovascular diseases, including aortic stenosis (AS). Consequently, an increased prevalence of cardiovascular diseases is expected in lung cancer screenees. Therefore, initial aortic valve calcification evaluation should be additionally performed on LDCT. The aim of this study was to estimate a calcium score (CS) cutoff point for clinically significant AS diagnosis based on LDCT, confirmed by echocardiographic examination. The study included 6631 heavy smokers who participated in a lung cancer screening program (MOLTEST BIS). LDCTs were performed on all individuals and were additionally assessed for aortic valve calcification with the use of CS according to the Agatston method. Patients with CS ≥ 900 were referred for echocardiography to confirm the diagnosis of AS and to evaluate its severity. Of 6631 individuals, 54 met the inclusion criteria and underwent echocardiography for confirmation and assessment of AS. Based on that data, receiver operating characteristic (ROC) curves of CS were plotted, and cutoff points for clinically significant AS diagnosis were established: A CS of 1758 for at least moderate AS had 85.71% (CI 65.36–95.02%) sensitivity and 75.76% (CI 58.98–87.17%) specificity; a CS of 2665 for severe AS had 87.5% (CI 73.89–94.54%) sensitivity and 76.92% (CI 49.74–91.82%) specificity. This is the first study to assess possible CS cutoff points for diagnosing clinically significant AS detected by LDCT in lung cancer screening participants. LDCT with CS assessment could enable early detection of patients with clinically significant AS and therefore identify patients who require appropriate treatment.
Impediments to Construction Site Digitalisation Using Unmanned Aerial Vehicles (UAVs)
Authors: Onososen Adetayo Olugbenga , Musonda Innocent , Onatayo Damilola , Tjebane Motheo Meta , Saka Abdullahi Babatunde , Fagbenro Rasaki Kolawole
Published: January 2023

Utilising emerging innovative technologies and systems to improve construction processes in an effort towards digitalisation has been earmarked as critical to delivering resilience and responsive infrastructure. However, successful implementation is hindered by several challenges. Hence, this study evaluates the challenges facing the adoption of unmanned aerial vehicles towards the digitalisation of the built environment. The study adopted a quantitative survey of built environment stakeholders in developed and developing economies. A total of 161 completely filled forms were received after the survey, and the data were analysed using descriptive analysis and inferential statistics. The study’s findings show that there are different barriers experienced between developed and developing countries in the adoption of drones towards digitalising construction processes in the built environment. Moreover, economic/cost-related factors were identified as the most critical barriers to the adoption of drones, followed by technical/regulatory factors and education/organisation-related factors. The findings can assist the built environment in reducing the impact of these barriers and could serve as a policy instrument and helpful guidelines for governmental organisations, stakeholders, and others.
Political Uncertainty and Financial Firm Performance: Evidence from the Thai Economy as an Emerging Market in Asia
Authors: Trakarnsirinont Worraphan , Jitaree Wisuttorn , Buachoom Wonlop Writthym
Published: January 2023

This study analyzes the effects that certain political-uncertainty factors have on financial firm performance in the Stock Exchange of Thailand (SET). The results of a panel regression performed on a database of 7976 firm-years over 18-year unbalanced panel data from 2001 to 2018 show a mixed relationship between political uncertainty and firm performance. The constitutional reform harms the return on assets (ROA), and the government election and political protest significantly decreased the market value of equity (MVE). In contrast, constitutional reform increased MVE, and the government election positively impacted ROA. Therefore, this study emphasizes how political unpredictability is assumed to influence firm performance in Thailand’s economy, an Asian developing country.
Design and Transition of an Emergency E-Learning Pathology Course for Medical Students—Evaluation of a Novel Course Concept
Authors: Holzmann-Littig Christopher , Jedlicska Nana , Wijnen-Meijer Marjo , Liesche-Starnecker Friederike , Schmidt-Bäse Karen , Renders Lutz , Weimann Katja , Konukiewitz Björn , Schlegel Jürgen
Published: January 2023

Background: Around the world, the emergency brought about by the COVID-19 pandemic forced medical schools to create numerous e-learning supplements to provide instruction during this crisis. The question now is to determine a way in which to capitalize on this momentum of digitization and harness the medical e-learning content created for the future. We have analyzed the transition of a pathology course to an emergency remote education online course and, in the second step, applied a flipped classroom approach including research skills training. Methods: In the summer semester of 2020, the pathology course at the Technical University of Munich was completely converted to an asynchronous online course. Its content was adapted in winter 2021 and incorporated into a flipped classroom concept in which research skills were taught at the same time. Results: Screencasts and lecture recordings were the most popular asynchronous teaching formats. Students reported developing a higher interest in pathology and research through group work. The amount of content was very challenging for some students. Conclusion: Flipped classroom formats are a viable option when using pre-existing content. We recommend checking such content for technical and didactic quality and optimizing it if necessary. Content on research skills can be combined very well with clinical teaching content.
Facile Green Preparation of Reduced Graphene Oxide Using Citrus Limetta-Decorated rGO/TiO2 Nanostructures for Glucose Sensing
Authors: Gijare Medha , Chaudhari Sharmila , Ekar Satish , Shaikh Shoyebmohamad F. , Mane Rajaram S. , Pandit Bidhan , Siddiqui Muhammad Usman Hassan , Garje Anil
Published: January 2023

The important electrochemical measurements of reduced graphene oxide-titanium oxide (rGO)/TiO2) electrodes for the application of a glucose sensor are reported in the proposed work. Investigating the sensitivity, stability, and reproducibility of sensor electrodes that were made and used to evaluate the concentration of glucose in the serum is one of the novel aspects of this work. This study presents the use of citrus limetta (sweet lime) fruit peel waste to synthesize a green reduction of graphene oxide (rGO). The rGO/TiO2 composite obtained using the microwave heating method is applied for measuring the structural and morphological properties by various means. A conducting fluorine-tin oxide substrate is used to modify the enzymeless glucose sensor electrode. The electrochemical measurements of rGO/TiO2 sensor electrodes are carried out using the technique of cyclic voltammetry. The rGO/TiO2 sensor electrode exhibits a high sensitivity of 1425 µA/mM cm2 towards glucose concentration in the range of 0.1 to 12 mM. The sensor was found to be extremely stable and repeatable with a response time of 5 s along with a minimum detection limit of 0.32 μM of glucose. The rGO/TiO2 sensor shows relative standard deviation (RSD) of 1.14%, 1.34%, and 1.3% which reveals its excellent stability, repeatability, and reproducibility respectively. The sensor was used for glucose level detection in natural blood serum and shows an RSD of 1.88%. which is in good agreement with the commercial glucose sensor values.
CWAN: Covert Watermarking Attack Network
Authors: Wang Chunpeng , Liu Yushuo , Xia Zhiqiu , Li Qi , Li Jian , Wang Xiaoyu , Ma Bin
Published: January 2023

Digital watermarking technology is widely used in today’s copyright protection, data monitoring, and data tracking. Digital watermarking attack techniques are designed to corrupt the watermark information contained in the watermarked image (WMI) so that the watermark information cannot be extracted effectively or correctly. While traditional digital watermarking attack technology is more mature, it is capable of attacking the watermark information embedded in the WMI. However, it is also more damaging to its own visual quality, which is detrimental to the protection of the original carrier and defeats the purpose of the covert attack on WMI. To advance watermarking attack technology, we propose a new covert watermarking attack network (CWAN) based on a convolutional neural network (CNN) for removing low-frequency watermark information from WMI and minimizing the damage caused by WMI through the use of deep learning. We import the preprocessed WMI into the CWAN, obtain the residual feature images (RFI), and subtract the RFI from the WMI to attack image watermarks. At this point, the WMI’s watermark information is effectively removed, allowing for an attack on the watermark information while retaining the highest degree of image detail and other features. The experimental results indicate that the attack method is capable of effectively removing the watermark information while retaining the original image’s texture and details and that its ability to attack the watermark information is superior to that of most traditional watermarking attack methods. Compared with the neural network watermarking attack methods, it has better performance, and the attack performance metrics are improved by tens to hundreds of percent in varying degrees, indicating that it is a new covert watermarking attack method.
Neurofuzzy Data Aggregation in a Multisensory System for Self-Driving Car Steering
Authors: Luna-Álvarez Antonio , Mújica-Vargas Dante , Rendón-Castro Arturo , Matuz-Cruz Manuel , Kinani Jean Marie Vianney
Published: January 2023

In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases the computational cost and the number of processes. To reduce the cost and allow data fusion and vehicle control as a single process, this research proposes a data fusion approach by formulating a neurofuzzy aggregation deep learning layer; this approach integrates aggregation using fuzzy measures μ as fuzzy synaptic weights, hidden state using the Choquet fuzzy integral, and a fuzzy backpropagation algorithm, creating a data processing from different sources. In addition, implementing a previous approach, a self-driving neural model is proposed based on the aggregation of a steering control model and another for obstacle detection. This was tested in an ROS simulation environment and in a scale prototype. Experimentation showed that the proposed approach generates an average autonomy of 95% and improves driving smoothness by 9% compared to other state-of-the-art methods.
Evidence Network Inference Recognition Method Based on Cloud Model
Authors: Wang Haibin , Guan Xin , Yi Xiao
Published: January 2023

Uncertainty is widely present in target recognition, and it is particularly important to express and reason the uncertainty. Based on the advantage of the evidence network in uncertainty processing, this paper presents an evidence network reasoning recognition method based on a cloud fuzzy belief. In this method, a hierarchical structure model of an evidence network is constructed; the MIC (maximum information coefficient) method is used to measure the degree of correlation between nodes and determine the existence of edges, and the belief of corresponding attributes is generated based on the cloud model. In addition, the method of information entropy is used to determine the conditional reliability table of non-root nodes, and the target recognition under uncertain conditions is realized afterwards by evidence network reasoning. The simulation results show that the proposed method can deal with the random uncertainty and cognitive uncertainty simultaneously, overcoming the problem that the traditional method has where it cannot carry out hierarchical recognition, and it can effectively use sensor information and expert knowledge to realize the deep cognition of the target intention.
Student Perceptions of Online Education and Digital Technologies during the COVID-19 Pandemic: A Systematic Review
Authors: Cramarenco Romana Emilia , Burcă-Voicu Monica Ioana , Dabija Dan-Cristian
Published: January 2023

We designed a bibliometric review combined with a systematic review of relevant research on student perceptions of online education, which was conducted from March 2020 until September 2022 (focused on emergency remote teaching). By exploring the Scopus and Web of Science (WoS) databases, we selected 154 articles addressing mostly positive/negative as well as neutral student perceptions. The methodology followed the Khan five-step approach and included a PRISMA flowchart integrating keywords for selecting the relevant quantitative data for the analysis. Evidence was brought forward identifying the advantages of online education based on efficiency-related factors, contributing to the supportive attitudes of the learners (digital technology application, self-directed learning and flexibility). Our analysis revealed that the pursued research field is still insufficiently explored. Universities must prepare more consciously for embedding current technological challenges to cope with unforeseen situations, such as an immediate switch from the classic face-to-face teaching to online based on digital technology education. The greatest challenge for the universities in the digital society is given by the just-in-time, flexible adaptation to the unplanned situations. Therefore, universities must always be prepared to make the switch from offline to online and vice versa—at the same time, guaranteeing the quality of teaching processes and student satisfaction. Future trends in education reveal the role of digitalization of the educational sector in integrating innovative technologies in the educational curriculum and calls for a new pedagogical approach that is centred on learners’ self-directed learning, collaborative learning and the cognitive and social presence of students and teachers. The most important contributions of the paper reside in an in-depth analysis of student perceptions of online education processes and their perceived advantages and disadvantages related to online learning experienced during the COVID-19 pandemic. These advantages were analysed against efficiency-related factors, and, among them, the “application of technologies” is considered relevant for a niche investigation of the technology acceptance model (TAM).
Bridgeless PFC Converter without Electrolytic Capacitor Based on Power Decoupling
Authors: Jin Ning-Zhi , Wu Zhi-Qiang , Zhang Long , Feng Yu , Wu Xiao-Gang
Published: January 2023

Due to fewer conduction devices in operating condition, the bridgeless power factor correction (PFC) converter is more efficient than the traditional PFC circuit. However, to achieve a low output voltage ripple on the DC side, a large electrolytic capacitor must be connected in parallel to the output end. To reduce the value of capacitance, this paper proposes a dual-boost bridgeless PFC converter with a bidirectional buck/boost power decoupling converter in the latter stage. The bidirectional converter absorbs double-line-frequency ripple, lowering the power pulsation at the output end while realizing power decoupling. The one-cycle control is adopted in bridgeless PFC converter, so that the input current can follow the input voltage to achieve power factor correction and decrease harmonic pollution. The power decoupling circuit is designed with a voltage outer loop using PI control and a current inner loop using model predictive current control, which alleviates the output voltage fluctuation caused by the reduction of the capacitance value of the filter capacitor, for the purpose of realizing non-electrolytic capacitor. Finally, the topology and control strategy involved in this paper are simulated and experimented to verify the validity and superiority of the theory.
Swarm Optimization and Machine Learning Applied to PE Malware Detection towards Cyber Threat Intelligence
Authors: Kattamuri Santosh Jhansi , Penmatsa Ravi Kiran Varma , Chakravarty Sujata , Madabathula Venkata Sai Pavan
Published: January 2023

Cyber threat intelligence includes analysis of applications and their metadata for potential threats. Static malware detection of Windows executable files can be done through the analysis of Portable Executable (PE) application file headers. Benchmark datasets are available with PE file attributes; however, there is scope for updating the data and also to research novel attribute reduction and performance improvement algorithms. The existing benchmark dataset contains non-PE header attributes, and few ignored attributes. In this work, a critical analysis was conducted to develop a new dataset called SOMLAP (Swarm Optimization and Machine Learning Applied to PE Malware Detection) with a value addition to the existing benchmark dataset. The SOMLAP data contains 51,409 samples that include both benign and malware files, with a total of 108 pure PE file header attributes. Further research was carried out to improve the performance of the Malware Detection System (MDS) by feature minimization using swarm optimization tools, viz., Ant Colony Optimization (ACO), Cuckoo Search Optimization (CSO), and Grey Wolf Optimization (GWO) wrapped with machine learning tools. The dataset was evaluated, and an accuracy of 99.37% with an optimized set of 12 features (ACO) proves the efficiency of the dataset, its attributes, and the algorithms used.
Unexpected Emission of H2S in an Excavation
Authors: McManus Thomas Neil , Henderson Sean
Published: January 2023

This article reports on the emission of H2S (hydrogen sulfide) during the excavation of disturbed material covering a former ocean shoreline. Operators of the excavators expressed concerns about the strong odors of H2S, the insensitivity of workplace instruments, and the safety of the work. This situation demanded an immediate and appropriate response. The literature contains no information concerning worker exposure to H2S in these situations. Levels detected by the Jerome 631-X ranged from 1 ppb (part per billion) to 25 ppb in 1-min samples during various activities. Levels decreased to zero when activity ceased. Unpredicted excursions peaking at 2500 ppb superimposed onto background levels immediately followed exposure to material containing H2S. Excursion levels rose abruptly, peaked, and decreased rapidly to the background. Excursions occurred once per day and lasted about 10 min. These emissions share characteristics of shear-thinning, pseudoplastic non-Newtonian fluids. A very conservative estimation of exposure during this work compared to the Threshold Limit Value-Time-Weighted Average of 1 part per million (ppm = 1000 ppb) suggested that this was considerably less than the lowest level of regulatory concern and that work under these conditions can proceed without overexposure to H2S. This work has relevance and application in similar situations occurring globally.
The Effectiveness of Natura 2000 Network in Conserving Salix alba and Populus alba Galleries against Invasive Species: A Case Study of Mureșul Mijlociu—Cugir Site, Romania
Authors: Avram Cristina Maria , Proorocu Marian , Mălinaș Anamaria , Mălinaș Cristian
Published: January 2023

There is currently a lack of knowledge regarding the effectiveness of the Natura 2000 (N2000) network in providing protection against biological invasion by invasive alien species (IAS). In this study, we analyzed two Salix alba and Populus alba riparian habitats located both inside and outside a N2000 site from Romania in order to assess the efficacy of N2000 network in conserving this type of ecosystem against IAS. Field investigations were conducted both using polygon method for vegetation study and semi-structured interviews focusing on the local people knowledge and their contribution to biological invasion. We found that the research area located within the N2000 network is highly exposed to biological invasion by IAS, the major threat to this type of habitat being expressed by Acer negundo, which occupied more than half of the vegetation cover. It seems that not only the management strategy should be blamed for this situation, but rather its failure in popularization and application. As such, in our opinion, future management plans developed by N2000 network should rely on the intertwined co-evolutionary mechanisms existing between poplar and willow stands diversity and cultural diversity, by integrating aspects related to local traditional knowledge.
Kombucha Healthy Drink—Recent Advances in Production, Chemical Composition and Health Benefits
Authors: Kitwetcharoen Haruthairat , Phung Ly Tu , Klanrit Preekamol , Thanonkeo Sudarat , Tippayawat Patcharaporn , Yamada Mamoru , Thanonkeo Pornthap
Published: January 2023

Kombucha, one of the ordinary fermented beverages consumed worldwide, is produced by fermenting tea and sugar with a symbiotic culture of bacteria and yeasts or so-called SCOBY. Kombucha can be made from different types of tea, such as black, green, white, red, and oolong teas, yielding various health benefits and properties. Several species of bacteria and yeasts are involved in the fermentation process, which generates many beneficial compounds, such as polyphenols, organic acids, amino acids, vitamins, minerals, organic nitrogens, and hydrolytic enzymes, which have significant health effects and therapeutic properties, such as antioxidant, anti-inflammatory, anticancer, and antimicrobial properties. This review describes recent research on kombucha fermentation, the microbial community in SCOBY, the chemical composition of kombucha, and its health benefits. The adverse effects and prospects of kombucha production were also discussed.
Selection of Yarrowia lipolytica Strains as Possible Solution to Valorize Untreated Cheese Whey
Authors: Gottardi Davide , Siroli Lorenzo , Braschi Giacomo , Rossi Samantha , Bains Narinder , Vannini Lucia , Patrignani Francesca , Lanciotti Rosalba
Published: January 2023

Cheese whey management and disposal is a major issue for dairy industries due to its high level of chemical and biochemical oxygen demand. However, it can still represent a source of nutrients (i.e., sugars, proteins and lipids) that can be applied, among other options, as substrate for microbial growth. Yarrowia lipolytica can grow in different environments, consuming both hydrophilic and hydrophobic substrates, and tolerates high salt concentrations. In this work, the lipolytic and proteolytic profile of 20 strains of Y. lipolytica were tested on caseins and butter. Then, their growth potential was evaluated in four types of whey (caciotta, ricotta, squacquerone and their mix). Y. lipolytica showed a very strain-dependent behavior for both hydrolytic profiles and growth capabilities on the different substrates. The best growers for all the types of whey tested were PO1, PO2, and RO2, with the first one reaching up to 8.77 log cfu/mL in caciotta whey after 72 h. The volatile molecule profile of the samples incubated with the best growers were characterized by higher amounts of esters, acids, ketones and alcohols. In this way, cheese whey can become a source of microbial cultures exploitable in the dairy sector.
Synergistic Effect of Surfactant on Disperser Energy and Liquefaction Potential of Macroalgae (Ulva intestinalis) for Biofuel Production
Authors: Puthiya Veettil Rinsha , Rabia  , Mathew Dinesh Kumar , Gondi Rashmi , Sankarapandian Kavitha , Kannan Meganathan , Kumar Gopalakrishnan , Al-Qaradawi Siham Y. , Jeyakumar Rajesh Banu
Published: January 2023

The objective of this study was to evaluate the effect of surfactant on disperser homogenization pretreatment for macroalgae (Ulva intestinalis) to enhance biogas production. The macroalgae are subjected to surfactant coupled disperser pretreatment, which enhanced the liquefaction and improved the biomethane production. The outcome of this study revealed that 10,000 rpm at 20 min with a specific energy input of 1748.352 kJ/ kg total solids (TS) are the optimum conditions for surfactant disperser pretreatment (SDP), which resulted in the liquefaction rate of 20.08% with soluble organics release of 1215 mg/L and showed a better result than disperser pretreatment (DP) with a liquefaction rate of 14%. Biomethane production through the SDP method was found to be 0.2 g chemical oxygen demand (COD)/g COD, which was higher than DP (0.11 g COD/g COD). SDP was identified to be a synergetic pretreatment method with an energy ratio and net profit of about 0.91 and 104.04 United States dollars (USD)/ton, respectively.
The Hindered Settling Velocity of Particles of Any Shape in Low Reynolds Number Flow
Authors: Mendez Yuri
Published: January 2023

This article takes insights from a previously derived mathematical framework for the free settling velocity of particles of any shape to model analytical constructs to solve the hindered settling velocity of hard particles of any shape. Because the geometry of the physical environment and continuity can be strictly enforced in the construct model, the relative velocity of the fluid front pumped upward by the settling particles can be found, thus allowing for calculation by subtracting the front velocity from the calculated velocity.
Analysis of Endocrine Disrupting Nonylphenols in Foods by Gas Chromatography-Mass Spectrometry
Authors: Lee Sang Mi , Cheong Daeun , Kim Meehye , Kim Young-Suk
Published: January 2023

Nonylphenols (NPs) are classified as endocrine-disrupting chemicals (EDCs), which are known to cause disorders in the endocrine systems of organisms. Due to their high lipophilicity and low degradability, these harmful substances are known to accumulate and persist in the environment, and even enter into the food chain. Analytical methods of liquid–liquid extraction using solid-phase extraction for sample clean-up combined with gas chromatography/mass spectrometry were established to determine the presence of NPs in foods. This study aimed to develop and validate these methods using four food matrices representing high-fat and low-fat solid food, as well as high-fat and low-fat liquid food, groups. The single linear isomer 4-n-NP was used to validate the quantification of NPs, which exist in complex isomer mixtures. Our results showed good linearity, with correlation coefficients exceeding 0.998 for all four matrices. The limits of detection and quantification were 0.37–1.79 and 1.11–5.41 μg/kg, respectively. Recovery rates were 86.8–108.6% and 92.6–101.9% for intraday and interday assays, respectively, and the relative standard deviations (RSDs) were below 12% for both assays. The method was applied to analyze 1185 domestic food samples consumed by Koreans, with NPs detected at concentration ranges of 2.57–269.07 μg/kg. Results for each food type over wide concentration ranges indicated that these compounds are highly dependent on the area of cultivation, and are affected by the levels of those contaminants in different environments. The contents of NPs in foods from animal sources were generally higher than those from plant sources, in particular being higher in the intestines than in lean tissue. The present findings could form the basis for determining the level of dietary exposure to NPs and how each food source contributes to it in South Korea.