bluuebunny/crossref_metadata_2025 huggingface dataset:
Development, Implementation and Application of Confocal Laser Endomicroscopy in Brain, Head and Neck Surgery—A Review
Authors: Kakaletri Irini , Linxweiler Maximilian , Ajlouni Serine , Charalampaki Patra
Published: November 2022

When we talk about visualization methods in surgery, it is important to mention that the diagnosis of tumors and how we define tumor borders intraoperatively in a correct way are two main things that would not be possible to achieve without this grand variety of visualization methods we have at our disposal nowadays. In addition, histopathology also plays a very important role, and its importance cannot be neglected either. Some biopsy specimens, e.g., frozen sections, are examined by a histopathologist and lead to tumor diagnosis and the definition of its borders. Furthermore, surgical resection is a very important point when it comes to prognosis and life survival. Confocal laser endomicroscopy (CLE) is an imaging technique that provides microscopic information on the tissue in real time. CLE of disorders, such as head, neck and brain tumors, has only recently been suggested to contribute to both immediate tumor characterization and detection. It can be used as an additional tool for surgical biopsies during biopsy or surgical procedures and for inspection of resection margins during surgery. In this review, we analyze the development, implementation, advantages and disadvantages as well as the future directions of this technique in neurosurgical and otorhinolaryngological disciplines.
Qualitative (and Quantitative) Values of the Lung-RADS and Computed Tomography in Diagnosing Solitary Pulmonary Nodules
Authors: Duan Lizhen , Shan Wenli , Bo Genji , Lu Guangming , Guo Lili
Published: November 2022

Background: Lung-RADS classification and CT signs can both help in the differential diagnosis of SPNs. The purpose of this study was to investigate the diagnostic value of these two methods and the combination of the two methods for solitary pulmonary nodules (SPNs). Methods: A total of 296 cases of SPNs were retrospectively analyzed. All the SPNs were classified according to the Lung-RADS grading version 1.1. The scores of each lesion were calculated according to their CT signs. Imaging features, such as the size and margin of the lesions, pleural traction, spiculation, lobulation, bronchial cutoff, air bronchogram, vacuoles, tumor vasculature, and cavity signs, were analyzed. The imaging results were compared with the pathology examination findings. Receiver operating characteristic (ROC) curves were applied to compare the values of the different methods in differentially diagnosing benign and malignant SPNs. Results: The sensitivity, specificity, and accuracy of Lung-RADS grading for diagnosing SPNs were 34.0%, 94.4%, and 47.6%, respectively. The area under the ROC curve (AUC) was 0.600 (p < 0.001). The sensitivity, specificity, and accuracy of the CT sign scores were 56.3%, 70.0%, and 60.5%, respectively, and the AUC was 0.657 (p < 0.001). The sensitivity, specificity, and accuracy of the combination of the two methods for diagnosing SPNs were 93.2%, 61.1%, and 83.5%, and the AUC was 0.777 (p < 0.001). Conclusion: The combination of Lung-RADS classification and CT signs significantly improved the differential diagnosis of SPNs.
Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity
Authors: Kuanr Madhusree , Mohapatra Puspanjali , Mittal Sanchi , Maindarkar Mahesh , Fouda Mostafa M. , Saba Luca , Saxena Sanjay , Suri Jasjit S.
Published: November 2022

Background: Hospitals face a significant problem meeting patients’ medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell–Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.
Polymorphic Malignant Melanoma (PMM) of the Left Helix: Case Report with Clinical-Pathological Correlations
Authors: Cazzato Gerardo , Colagrande Anna , Maruccia Michele , Nacchiero Eleonora , Lupo Carmelo , Casatta Nadia , Ingravallo Giuseppe , Maiorano Eugenio , Marzullo Andrea , Giudice Giuseppe , Resta Leonardo
Published: November 2022

Malignant melanoma (MM) is known to be the great mimic in dermatopathology. Over time, several variants have been described, not all of which have repercussions on the clinical/oncological management of the affected patient. The existence, however, of these alternative forms of MM is of great interest to the pathologist, as they are potentially capable of inducing diagnostic errors affecting the diagnostic-therapeutic care pathway (PDTC). In this paper, we present a very rare case of polymorphic MM, in which five different morphological aspects coexisted in the same lesion, confirmed by immunohistochemical investigation and by RT-PCR for mutation of the BRAF gene and discuss the importance of correct recognition of these different morphological features to avoid misdiagnosis.
Comparison between Field Measured and UAV-Derived Pistachio Tree Crown Characteristics throughout a Growing Season
Authors: Jacygrad Ewelina , Kelly Maggi , Hogan Sean , Preece John , Golino Deborah , Michelmore Richard
Published: November 2022

Monitoring individual tree crown characteristics is an important component of smart agriculture and is crucial for orchard management. We focused on understanding how UAV imagery taken across one growing season can help understand and predict the growth and development of pistachio trees grown from rootstock seedlings. Tree crown characteristics (i.e., height, size, shape, and mean normalized difference vegetation index (NDVI)) were derived using an object-based image analysis method with multispectral Uncrewed Aerial Vehicles (UAV) imagery flown seven times over 472 five-year-old pistachio trees in 2018. These imagery-derived metrics were compared with field-collected tree characteristics (tree height, trunk caliper, crown height, width and volume, and leaf development status) collected over two months in 2018. The UAV method captured seasonal development of tree crowns well. UAV-derived tree characteristics were better correlated with the field tree characteristics when recorded between May and November, with high overall correlations in November. The highest correlation (R2 = 0.774) was found between trunk caliper and June UAV crown size. The weakest correlations between UAV and field traits were found in March and December. Spring leaf development stage was most variable, and mean NDVI values were lowest in March, when leaf development starts. Mean NDVI increased orchard-wide by May, and was consistently high through November. This study showcased the benefits of timely, detailed drone imagery for orchard managers.
RREV: A Robust and Reliable End-to-End Visual Navigation
Authors: Ou Wenxiao , Wu Tao , Li Junxiang , Xu Jinjiang , Li Bowen
Published: November 2022

With the development of deep learning, more and more attention has been paid to end-to-end autonomous driving. However, affected by the nature of deep learning, end-to-end autonomous driving is currently facing some problems. First, due to the imbalance between the “junctions” and “non-junctions” samples of the road scene, the model is overfitted to a large class of samples during training, resulting in insufficient learning of the ability to turn at intersections; second, it is difficult to evaluate the confidence of the deep learning model, so it is impossible to determine whether the model output is reliable, and then make further decisions, which is an important reason why the end-to-end autonomous driving solution is not recognized; and third, the deep learning model is highly sensitive to disturbances, and the predicted results of the previous and subsequent frames are prone to jumping. To this end, a more robust and reliable end-to-end visual navigation scheme (RREV navigation) is proposed in this paper, which was used to predict a vehicle’s future waypoints from front-view RGB images. First, the scheme adopted a dual-model learning strategy, using two models to independently learn “junctions” and “non-junctions” to eliminate the influence of sample imbalance. Secondly, according to the smoothness and continuity of waypoints, a model confidence quantification method of “Independent Prediction-Fitting Error” (IPFE) was proposed. Finally, IPFE was applied to weight the multi-frame output to eliminate the influence of the prediction jump of the deep learning model and ensure the coherence and smoothness of the output. The experimental results show that the RREV navigation scheme in this paper was more reliable and robust, especially, the steering performance of the model intersection could be greatly improved.
A Cube Attack on a Reduced-Round Sycon
Authors: Cho Minjeong , Eom Hyejin , Tcydenova Erzhena , Lee Changhoon
Published: November 2022

The cube attack was proposed at the 2009 Eurocrypt. The attack derives linear polynomials for specific output bits of a BlackBox cipher. Cube attacks target recovery keys or secret states. In this paper, we present a cube attack on a 5-round Sycon permutation and a 6-round Sycon permutation with a 320-bit state, whose rate occupies 96 bits, and whose capacity is 224 bits. We found cube variables related to a superpoly with a secret state. Within the cube variables, we recovered 32 bits of the secret state. The target algorithm was Sycon with 5-round and 6-round versions of permutation. For the 5-round Sycon, we found a cube variable and recovered a state with a total of 2192 Sycon computations and 237 bits of memory. For the 6-round Sycon, we found cube variables and recovered a state with a total of 2192 Sycon computations and 270 bits of memory. When using brute force in a 5-round attack, 2224 operations were required, but the cube attack proposed in this paper had 248 offline operations, and 232 operations were required. When using brute force in a 6-round attack, 2224 operations were required, but the cube attack proposed in this paper required 295 offline operations, and 263 operations were required. For both attacks, offline could be used continuously after performing only once. To the best of our knowledge, this is the first cube attack on Sycon.
Hierarchical Clustering-Based Image Retrieval for Indoor Visual Localization
Authors: Feng Guanyuan , Jiang Zhengang , Tan Xuezhi , Cheng Feihao
Published: November 2022

Visual localization is employed for indoor navigation and embedded in various applications, such as augmented reality and mixed reality. Image retrieval and geometrical measurement are the primary steps in visual localization, and the key to improving localization efficiency is to reduce the time consumption of the image retrieval. Therefore, a hierarchical clustering-based image-retrieval method is proposed to hierarchically organize an off-line image database, resulting in control of the time consumption of image retrieval within a reasonable range. The image database is hierarchically organized by two stages: scene-level clustering and sub-scene-level clustering. In scene-level clustering, an improved cumulative sum algorithm is proposed to detect change points and then group images by global features. On the basis of scene-level clustering, a feature tracking-based method is introduced to further group images into sub-scene-level clusters. An image retrieval algorithm with a backtracking mechanism is designed and applied for visual localization. In addition, a weighted KNN-based visual localization method is presented, and the estimated query position is solved by the Armijo–Goldstein algorithm. Experimental results indicate that the running time of image retrieval does not linearly increase with the size of image databases, which is beneficial to improving localization efficiency.
On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction
Authors: González-Zapata Astrid Maritza , Tlelo-Cuautle Esteban , Cruz-Vega Israel
Published: November 2022

Interest in chaotic time series prediction has grown in recent years due to its multiple applications in fields such as climate and health. In this work, we summarize the contribution of multiple works that use different machine learning (ML) methods to predict chaotic time series. It is highlighted that the challenge is predicting the larger horizon with low error, and for this task, the majority of authors use datasets generated by chaotic systems such as Lorenz, Rössler and Mackey–Glass. Among the classification and description of different machine learning methods, this work takes as a case study the Echo State Network (ESN) to show that its optimization can lead to enhance the prediction horizon of chaotic time series. Different optimization methods applied to different machine learning ones are given to appreciate that metaheuristics are a good option to optimize an ESN. In this manner, an ESN in closed-loop mode is optimized herein by applying Particle Swarm Optimization. The prediction results of the optimized ESN show an increase of about twice the number of steps ahead, thus highlighting the usefulness of performing an optimization to the hyperparameters of an ML method to increase the prediction horizon.
Artificial Neural Network-Based Abnormal Gait Pattern Classification Using Smart Shoes with a Gyro Sensor
Authors: Jeong Kimin , Lee Kyung-Chang
Published: November 2022

Recently, as a wearable-sensor-based approach, a smart insole device has been used to analyze gait patterns. By adding a small low-power sensor and an IoT device to the smart insole, it is possible to monitor human activity, gait pattern, and plantar pressure in real time and evaluate exercise function in an uncontrolled environment. The sensor-embedded smart soles prevent any feeling of heterogeneity, and WiFi technology allows acquisition of data even when the user is not in a laboratory environment. In this study, we designed a sensor data-collection module that uses a miniaturized low-power accelerometer and gyro sensor, and then embedded it in a shoe to collect gait data. The gait data are sent to the gait-pattern classification module via a Wi-Fi network, and the ANN model classifies the gait into gait patterns such as in-toeing gait, normal gait, or out-toeing gait. Finally, the feasibility of our model was confirmed through several experiments.
A Method for Predicting the Remaining Life of Rolling Bearings Based on Multi-Scale Feature Extraction and Attention Mechanism
Authors: Jiang Changhong , Liu Xinyu , Liu Yizheng , Xie Mujun , Liang Chao , Wang Qiming
Published: November 2022

In response to the problems of difficult identification of degradation stage start points and inadequate extraction of degradation features in the current rolling bearing remaining life prediction method, a rolling bearing remaining life prediction method based on multi-scale feature extraction and attention mechanism is proposed. Firstly, this paper takes the normalized bearing vibration signal as input and adopts a quadratic function as the RUL prediction label, avoiding identifying the degradation stage start point. Secondly, the spatial and temporal features of the bearing vibration signal are extracted using the dilated convolutional neural network and LSTM network, respectively, and the channel attention mechanism is used to assign weights to each degradation feature to effectively use multi-scale information. Finally, the mapping of bearing degradation features to remaining life labels is achieved through a fully connected layer for the RUL prediction of bearings. The proposed method is validated using the PHM 2012 Challenge bearing dataset, and the experimental results show that the predictive performance of the proposed method is superior to that of other RUL prediction methods.
SEMRAchain: A Secure Electronic Medical Record Based on Blockchain Technology
Authors: Mhamdi Halima , Ayadi Manel , Ksibi Amel , Al-Rasheed Amal , Soufiene Ben Othman , Hedi Sakli
Published: November 2022

A medical record is an important part of a patient’s follow-up. It comprises healthcare professionals’ views, prescriptions, analyses, and all information about the patient. Several players, including the patient, the doctor, and the pharmacist, are involved in the process of sharing, and managing this file. Any authorized individual can access the electronic medical record (EMR) from anywhere, and the data are shared among various health service providers. Sharing the EMR requires various conditions, such as security and confidentiality. However, existing medical systems may be exposed to system failure and malicious intrusions, making it difficult to deliver dependable services. Additionally, the features of these systems represent a challenge for centralized access control methods. This paper presents SEMRAchain a system based on Access control (Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)) and a smart contract approach. This fusion enables decentralized, fine-grained, and dynamic access control management for EMR management. Together, blockchain technology as a secure distributed ledger and access control provides such a solution, providing system stakeholders with not just visibility but also trustworthiness, credibility, and immutability.
Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network
Authors: Sakkarvarthi Gnanavel , Sathianesan Godfrey Winster , Murugan Vetri Selvan , Reddy Avulapalli Jayaram , Jayagopal Prabhu , Elsisi Mahmoud
Published: November 2022

Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy.
Automatic Pavement Crack Detection Fusing Attention Mechanism
Authors: Ren Junhua , Zhao Guowu , Ma Yadong , Zhao De , Liu Tao , Yan Jun
Published: November 2022

Pavement cracks can result in the degradation of pavement performance. Due to the lack of timely inspection and reparation for the pavement cracks, with the development of cracks, the safety and service life of the pavement can be decreased. To curb the development of pavement cracks, detecting these cracks accurately plays an important role. In this paper, an automatic pavement crack detection method is proposed. For achieving real-time inspection, the YOLOV5 was selected as the base model. Due to the small size of the pavement cracks, the accuracy of most of the pavement crack deep learning-based methods cannot reach a high degree. To further improve the accuracy of those kind of methods, attention modules were employed. Based on the self-building datasets collected in Linyi city, the performance among various crack detection models was evaluated. The results showed that adding attention modules can effectively enhance the ability of crack detection. The precision of YOLOV5-CoordAtt reaches 95.27%. It was higher than other conventional and deep learning methods. According to the pictures of the results, the proposed methods can detect accurately under various situations.
Robotic Weld Image Enhancement Based on Improved Bilateral Filtering and CLAHE Algorithm
Authors: Lu Peng , Huang Qingjiu
Published: November 2022

Robotic welding requires a higher weld image resolution for easy weld identification; however, the higher the resolution, the higher the cost. Therefore, this paper proposes an improved CLAHE algorithm, which can not only effectively denoise and retain edge information but also improve the contrast of images. First, an improved bilateral filtering algorithm is used to process high-resolution images to remove noise while preserving edge details. Then, the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm and Gaussian masking algorithm are used to enhance the enhanced image, and then differential processing is used to reduce the noise in the two images, while preserving the details of the image, enhancing the image contrast, and obtaining the final enhanced image. Finally, the effectiveness of the algorithm is verified by comparing the peak signal-to-noise ratio and structural similarity with other algorithms.
Returning to Leisure Activity Post-Stroke: Barriers and Facilitators to Engagement
Authors: Harrison Joanna , Thetford Clare , Reeves Matthew J. , Brown Christopher , Joshi Miland , Watkins Caroline
Published: November 2022

Objectives: To identify barriers and facilitators to engagement when returning to, or participating in, leisure activity post-stroke or Transient Ischemic Attack (TIA). Design: Sequential explanatory, mixed methods study. Setting: 21 hospital sites across England, Wales and Northern Ireland. Participants: Adults with a clinical diagnosis of first/recurrent stroke or TIA. Patients approaching end of life were excluded. Participants were recruited as in-patients or at first clinic appointment and a baseline questionnaire was completed. A 6-month follow-up questionnaire was sent to participants for self-completion. Open-text questions were asked about barriers and facilitators when returning to, or participating in, leisure activity. Responses were thematically analysed and explored by participant characteristics, including type of leisure activity undertaken. Characteristics also included measures of socioeconomic deprivation, mood, fatigue and disability. Results: 2000 participants returned a 6-month follow-up questionnaire (78% stroke, 22% TIA); 1045 participants responded to a question on barriers and 820 on facilitators. Twelve themes were identified and the proportion of responses were reported (%). Barriers: physical difficulties (69%), lower energy levels (17%), loss of independence (11%), psychological difficulties (10%), hidden disabilities (7%), and delay or lack of healthcare provision (3%). Facilitators: family support (35%), healthcare support (27%), well-being and fitness (22%), friendship support (20%), self-management (19%), and returning to normality (9%). ‘Physical difficulties’ was the most reported barrier across all participant characteristics and activity types. Family support was the most reported facilitator except for those with greater disability, where it was healthcare support and those without fatigue where it was well-being and exercise. Conclusions: Physical difficulties and lack of energy are problematic for stroke and TIA survivors who want to return to or participate in leisure activity. Healthcare support alone cannot overcome all practical and emotional issues related to leisure activity engagement. Family support and improving well-being are important facilitators and future research should explore these mechanisms further.
Effects of Parasitism on the Competitive Ability of Invasive and Native Species
Authors: Yuan Yongge , Li Junmin
Published: November 2022

Parasitic plants can often seriously harm host plants and, thus, alter competitive dominance between hosts and neighbouring species. However, whether and how parasitic plants differently affect the competitive abilities of invasive and the native plants have not been tested. In this study, we used Cuscuta grovonii as the parasitic plants and three invasive plants and three native plants as host plants. Host plants grown alone or in competition with Coix lacryma-jobi were either parasitized with Cuscuta grovonii or not parasitized. Parasitism caused similar damage to invasive and native plants when grown with Cuscuta grovonii alone but caused less damage to invasive species than native species when grown in competition. Parasitism increased the competitive ability of invasive plants but did not affect the competitive ability of native plants. In the absence of parasitism, the competitive ability of host plants was significantly negatively correlated with the competitive ability of Coix lacryma-jobi, but under parasitism, there was no significant relationship of the competitive ability between host and competitor plants. Our results indicated that parasitic plants can increase the competitive tolerance of invasive plants, but have no effect on native plants. Thus, parasitism may play an important role in the process of plant invasion.
The Use of Marble Dust, Bagasse Ash, and Paddy Straw to Improve the Water Absorption and Linear Shrinkage of Unfired Soil Block for Structure Applications
Authors: Sharma Tarun , Singh Sandeep , Sharma Shubham , Sharma Prashant , Gehlot Anita , Shukla Anand Kumar , Eldin Sayed M.
Published: November 2022

Unfired admixed soil blocks are made up of soil plus stabilizers such as binders, fibers, or a combination of both. Soil is abundant on Earth, and it has been used to provide shelter to millions of people. The manufacturing and usage of cement and cement blocks raise several environmental and economic challenges. Due to disposal issues, agricultural and industrial waste is currently the biggest hazard to the environment and humanity in the world. Consequently, environmental degradation brought on by agricultural waste harms the ecology. As a result, researchers are attempting to develop an alternative to cement blocks, and various tests on unfired admixed soil blocks have been done. This investigation uses agricultural waste (i.e., paddy straw fiber and sugarcane bagasse ash) and industrial waste (i.e., marble dust) in manufacturing unfired admixed soil blocks. Under this investigation, the applicability of unfired soil blocks admixed with marble dust, paddy straw fiber, and bagasse ash was studied. The marble dust level ranged from 25% to 35%, bagasse ash content ranged from 7.5% to 12.5%, and the content of paddy straw fiber ranged from 0.8% to 1.2% by soil dry weight. Various tests were conducted on the 81 mix designs of the prepared unfired admixed soil blocks to find out the physical properties of the block followed by modeling and optimization. The findings demonstrate that the suggested method is a superior alternative to burned bricks for improving the physical properties of admixed soil blocks without firing.
Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete
Authors: Amin Muhammad Nasir , Al-Hashem Mohammed Najeeb , Ahmad Ayaz , Khan Kaffayatullah , Ahmad Waqas , Qadir Muhammad Ghulam , Imran Muhammad , Al-Ahmad Qasem M. S.
Published: November 2022

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles. The data set was based on 11 input parameters, such as cement, limestone, fly ash, ground granulated blast-furnace slag, silica fume, rice husk ash, coarse aggregate, fine aggregate, superplasticizers, water, viscosity modifying admixtures, and one output with compressive strength of SCC. In terms of properly predicting the CS of SCC, the BR technique outperformed both the SVM and MLP models, as determined by the research results. In contrast to SVM and MLP, the coefficient of determination (R2) for the BR model was 0.95, whereas for SVM and MLP, the R2 was 0.90 and 0.86, respectively. In addition, a k-fold cross-validation approach was adopted to check the accuracy of the employed models. The statistical measures mean absolute percent error, mean absolute error, and root mean square error ensure the validity of the model. Using sensitivity analysis, the influence of input factors on the intended CS of SCC was also explored. This analysis reveals that the highest contributing parameter towards the CS of SCC was cement with 16.2%, while rice husk ash contributed the least with 4.25% among all the input variables.
Controlling Charge Transport in Molecular Wires through Transannular π–π Interaction
Authors: Song Jianjian , Zhu Jianglin , Wang Zhaoyong , Liu Gang
Published: November 2022

This paper describes the influence of the transannular π–π interaction in controlling the carrier transport in molecular wires by employing the STM break junction technique. Five pentaphenylene-based molecular wires that contained [2.2]paracyclophane-1,9-dienes (PCD) as the building block were prepared as model compounds. Functional substituents with different electronic properties, ranging from strong acceptors to strong donors, were attached to the top parallel aromatic ring and used as a gate. It was found that the carrier transport features of these molecular wires, such as single-molecule conductance and a charge-tunneling barrier, can be systematically controlled through the transannular π–π interaction.
Review on Stress-Fractional Plasticity Models
Authors: Qu Pengfei , Sun Yifei , Sumelka Wojciech
Published: November 2022

Fractional calculus plays an increasingly important role in mechanics research. This review investigates the progress of an interdisciplinary approach, fractional plasticity (FP), based on fractional derivative and classic plasticity since FP was proposed as an efficient alternative to modelling state-dependent nonassociativity without an additional plastic potential function. Firstly, the stress length scale (SLS) is defined to conduct fractional differential, which influences the direction and intensity of the nonassociated flow of geomaterials owing to the integral definition of the fractional operator. Based on the role of SLS, two branches of FP, respectively considering the past stress and future reference critical state can be developed. Merits and demerits of these approaches are then discussed, which leads to the definition of the third branch of FP, by considering the influences of both past and future stress states. In addition, some specific cases and potential applications of the third branch can be realised when specific SLS are adopted.
Graphene Synthesis Techniques and Environmental Applications
Authors: Abbas Qaisar , Shinde Pragati A. , Abdelkareem Mohammad Ali , Alami Abdul Hai , Mirzaeian Mojtaba , Yadav Arti , Olabi Abdul Ghani
Published: November 2022

Graphene is fundamentally a two-dimensional material with extraordinary optical, thermal, mechanical, and electrical characteristics. It has a versatile surface chemistry and large surface area. It is a carbon nanomaterial, which comprises sp2 hybridized carbon atoms placed in a hexagonal lattice with one-atom thickness, giving it a two-dimensional structure. A large number of synthesis techniques including epitaxial growth, liquid phase exfoliation, electrochemical exfoliation, mechanical exfoliation, and chemical vapor deposition are used for the synthesis of graphene. Graphene prepared using different techniques can have a number of benefits and deficiencies depending on its application. This study provides a summary of graphene preparation techniques and critically assesses the use of graphene, its derivates, and composites in environmental applications. These applications include the use of graphene as membrane material for the detoxication and purification of water, active material for gas sensing, heavy metal ions detection, and CO2 conversion. Furthermore, a trend analysis of both synthesis techniques and environmental applications of graphene has been performed by extracting and analyzing Scopus data from the past ten years. Finally, conclusions and outlook are provided to address the residual challenges related to the synthesis of the material and its use for environmental applications.
Lapping Quality Prediction of Ceramic Fiber Brush Based on Gaussian-Restricted Boltzmann Machine
Authors: Yuan Xiuhua , Wang Chong , Li Mingqing , Sun Qun
Published: November 2022

Although ceramic fiber brushes have been widely used for deburring and surface finishing, the associated relationship between process parameters and lapping quality is still unclear. In order to optimize the lapping process of ceramic fiber brushes, this paper proposes a multi-layer neural network based on the Gaussian-restricted Boltzmann machine (GRBM), and verified its prediction effectiveness. Compared with a traditional back-propagation neural network, its prediction error was reduced from 7.6% to 4.5%, and the determination coefficient was increased from 0.96 to 0.98, respectively. The comparison results showed that the proposed model can better grasp the relationship between process parameters and machining quality, which can be used as a decision-making foundation for lapping-process optimization.
Effect of Temperature on the Complex Modulus of Mg-Based Unidirectionally Aligned Carbon Fiber Composites
Authors: Kúdela Stanislav , Koráb Juraj , Štefánik Pavol
Published: November 2022

Composite materials based on magnesium–lithium (MgLi) and magnesium–yttrium (MgY) matrices reinforced with unidirectional carbon fibers were prepared using the gas pressure infiltration method. Two types of carbon fibers were used, high-strength PAN-based T300 fibers and high-modulus pitch-based Granoc fibers. The PAN-based carbon fibers have an internal turbostratic structure composed of crystallites. The pitch-based carbon fibers have a longitudinally aligned graphite crystal structure. The internal carbon fiber structure is crucial in the context of the interfacial reaction with the alloying element. There are various mechanisms of bonding to carbon fibers in the case of magnesium–lithium and magnesium–yttrium alloys. This paper presents the use of the DMA method for the characterization of the role of alloying elements in the quality of interfacial bonding and the influence on the complex modulus at increasingly elevated temperatures (50–250 °C). The complex modulus values of the composites with T300 fibers were in the range of 118–136 GPa. The complex modulus values of the composites with Granoc fibers were in the range of 198–236 GPa. The damping capacity of magnesium-based unidirectionally aligned carbon fiber composites is related to the quality of the interfacial bonding.
Time-Resolved Four-Channel Jones Matrix Measurement of Birefringent Materials Using an Ultrafast Laser
Authors: Cheng Zhenjia , Zhang Yuqin , Liu Xuan , Guo Chengshan , He Changwei , Liu Guiyuan , Song Hongsheng
Published: November 2022

A method for ultrafast time-resolved four-channel Jones matrix measurement of birefringent materials using an ultrafast laser is investigated. This facilitated the acquisition of a four-channel angular multiplexing hologram in a single shot. The Jones matrix information of a birefringent sample was retrieved from the spatial spectrum of a hologram. The feasibility of this approach was established by measuring the Jones matrix of starch granules in microfluidic chips and the complex amplitude distribution and phase delay distribution of liquid crystal cell at different voltages. Moreover, when the picosecond laser was switched to a femtosecond laser, ultrafast measurements were possible provided that the time interval between two detection pulses was larger than the pulse width.
Effects of Boride Orientation and Si Content on High-Temperature Oxidation Resistance of Directionally Solidified Fe–B Alloys
Authors: Guo Pengjia , Ma Shengqiang , He Xuebin , Lv Ping , Luo Yang , Jia Junhong , Cui Xudong , Xu Liujie , Xing Jiandong
Published: November 2022

In this work, the as-cast directionally solidified (DS) Fe–B alloys with various Si contents and different boride orientation were designed and fabricated, and the as-cast microstructures and static oxidation behaviors of the DS Fe–B alloys were investigated extensively. The as-cast microstructure of the DS Fe–B alloys consists of the well-oriented Fe2B columnar grains and α-Fe, which are strongly refined by Si addition. The oxidation interface of the scales in the DS Fe–B alloy with 3.50 wt.% Si demonstrates an obvious saw-tooth shaped structure and is embedded into the alternating distributed columnar layer structures of the DS Fe–B alloy with oriented Fe2B and α-Fe matrix, which is beneficial to improve the anti-peeling performance of the oxide film compared with lower amounts of Si addition in DS Fe–B alloys with oriented Fe2B [002] orientation parallel to the oxidation direction (i.e., oxidation diffusion direction, labeled as Fe2B// sample). In the DS Fe–B alloys with oriented Fe2B [002] orientation vertical to the oxidation direction (i.e., labeled as Fe2B⊥ sample), due to the blocking and barrier effect of laminated-structure boride, Si is mainly enriched in the lower part of the oxide film to form a dense SiO2 thin layer adhered to layered boride. As a result, the internal SiO2 thin layer plays an obstructed and shielded role in oxidation of the substrate, which hinders the further internal diffusion of oxygen ions and improves the anti-oxidation performance of the Fe2B⊥ sample, making the average anti-oxidation performance better than that of the Fe2B// sample.
Influence of the Carbidized Tungsten Surface on the Processes of Interaction with Helium Plasma
Authors: Skakov Mazhyn , Miniyazov Arman , Batyrbekov Erlan , Baklanov Viktor , Koyanbayev Yerbolat , Gradoboev Aleksandr , Kozhakhmetov Yernat , Sokolov Igor , Tulenbergenov Timur , Zhanbolatova Gainiya
Published: November 2022

This paper presents the results of experimental studies of the interaction of helium plasma with a near-surface tungsten carbide layer. The experiments were implemented at the plasma-beam installation. The helium plasma loading conditions were close to those expected in the ITER divertor. The technology of the plasma irradiation was applied in a stationary type linear accelerator. The impact of the helium plasma was realized in the course of the experiment with the temperatures of ~905 °C and ~1750 °C, which were calculated by simulating heat loading on a tungsten monoblock of the ITER divertor under the plasma irradiation at the load of 10 MW/m2 and 20 MW/m2, respectively. The structure was investigated with scanning microscopy, transmitting electron microscopy and X-ray analysis. The data were obtained showing that the surface morphology changed due to the erosion. It was found that the carbidization extremely impacted the plasma–tungsten interaction, as the plasma–tungsten interaction with the carbide layer led to the carbon sputtering and partial diffusion towards to the depth of the sample. According to these results, WC-based tungsten carbide is less protected against fracture by helium than W and W2C. An increase in temperature leads to much more extensive surface damage accompanied by the formation of molten and recrystallized flanges.
Effect of Severe Plastic Deformation and Post-Deformation Heat Treatment on the Microstructure and Superelastic Properties of Ti-50.8 at.% Ni Alloy
Authors: Lee Tae-Jin , Kim Woo-Jin
Published: November 2022

Severe plastic deformation via high-ratio differential speed rolling (HRDSR) was applied to the Ni-rich Ti-50.8Ni alloy. Application of HRDSR and a short annealing time of 5 min at 873 K leads to the production of a partially recrystallized microstructure with a small grain size of 5.1 μm. During the aging process for the annealed HRDSR sample at 523 K for 16 h, a high density of Ni3Ti4 particles was uniformly precipitated over the matrix, resulting in the formation of an R phase as the major phase at room temperature. The aged HRDSR sample exhibits excellent superelasticity and superelastic cyclability. This achievement can be attributed to an increase in strength through effective grain refinement and particle strengthening by Ni3Ti4 and a decrease in the critical stress for stress-induced martensite (B19′) due to the presence of the R-phase instead of B2 as a major phase at room temperature. The currently proposed method for using HRDSR and post-deformation heat treatment allows for the production of Ni-rich NiTi alloys with excellent superelasticity in sheet form.
Functional Evaluation of 3D Liver Models Labeled with Polysaccharide Functionalized Magnetic Nanoparticles
Authors: Miyamoto Yoshitaka , Koshidaka Yumie , Murase Katsutoshi , Kanno Shoichiro , Noguchi Hirofumi , Miyado Kenji , Ikeya Takeshi , Suzuki Satoshi , Yagi Tohru , Teramoto Naozumi , Hayashi Shuji
Published: November 2022

Establishing a rapid in vitro evaluation system for drug screening is essential for the development of new drugs. To reproduce tissues/organs with functions closer to living organisms, in vitro three-dimensional (3D) culture evaluation using microfabrication technology has been reported in recent years. Culture on patterned substrates with controlled hydrophilic and hydrophobic regions (Cell-ableTM) can create 3D liver models (miniature livers) with liver-specific Disse luminal structures and functions. MRI contrast agents are widely used as safe and minimally invasive diagnostic methods. We focused on anionic polysaccharide magnetic iron oxide nanoparticles (Resovist®) and synthesized the four types of nanoparticle derivatives with different properties. Cationic nanoparticles (TMADM) can be used to label target cells in a short time and have been successfully visualized in vivo. In this study, we examined the morphology of various nanoparticles. The morphology of various nanoparticles showed relatively smooth-edged spherical shapes. As 3D liver models, we prepared primary hepatocyte–endothelial cell heterospheroids. The toxicity, CYP3A, and albumin secretory capacity were evaluated in the heterospheroids labeled with various nanoparticles. As the culture period progressed, the heterospheroids labeled with anionic and cationic nanoparticles showed lower liver function than non-labeled heterospheroids. In the future, there is a need to improve the method of creation of artificial 3D liver or to design a low-invasive MRI contrast agent to label the artificial 3D liver.
Utilizing and Valorizing Oat and Barley Straw as an Alternative Source of Lignocellulosic Fibers
Authors: Borrega Marc , Hinkka Ville , Hörhammer Hanna , Kataja Kirsi , Kenttä Eija , Ketoja Jukka A. , Palmgren Rosa , Salo Minna , Sundqvist-Andberg Henna , Tanaka Atsushi
Published: November 2022

The transition to sustainable, biodegradable, and recyclable materials requires new sources of cellulose fibers that are already used in large volumes by forest industries. Oat and barley straws provide interesting alternatives to wood fibers in lightweight material applications because of their similar chemical composition. Here we investigate processing and material forming concepts, which would enable strong fiber network structures for various applications. The idea is to apply mild pretreatment processing that could be distributed locally so that the logistics of the raw material collection could be made efficient. The actual material production would then combine foam-forming and hot-pressing operations that allow using all fractions of fiber materials with minimal waste. We aimed to study the technical features of this type of processing on a laboratory scale. The homogeneity of the sheet samples was very much affected by whether the raw material was mechanically refined or not. Straw fibers did not form a bond spontaneously with one another after drying the sheets, but their effective bonding required a subsequent hot pressing operation. The mechanical properties of the formed materials were at a similar level as those of the conventional wood-fiber webs. In addition to the technical aspects of materials, we also discuss the business opportunities and system-level requirements of using straw as an alternative source of lignocellulosic fibers.
Double-Layered Polymer Microcapsule Containing Non-Flammable Agent for Initial Fire Suppression
Authors: Lee Dong Hun , Kwon Soonhyun , Kim Young Eun , Kim Na Yeon , Joo Ji Bong
Published: November 2022

Fire in energy storage systems, such as lithium-ion batteries, has been raised as a serious concern due to the difficulty of suppressing it. Fluorine-based non-flammable agents used as internal substances leaked through the fine pores of the polymer outer shell, leading to a degradation of fire extinguishing performance. To improve the durability of the fire suppression microcapsules and the stability of the ouster shell, a complex coacervation was used, which could be microencapsulated at a lower temperature, and the polymer shell was coated with urea-formaldehyde (UF) resin. The outermost UF resin formed elaborate bonds with the gelatin-based shell, and thus, the structure of the outer shell became denser, thereby improving the loss resistance of the inner substance and thermal stability. The double-layered microcapsules had an average particle diameter of about 309 μm, and a stable outer shell formed with a mass loss of 0.005% during long-term storage for 100 days. This study confirmed that the double-layered microcapsules significantly improved thermal stability, resistance to core material loss, core material content and fire suppression performance compared to single wall microcapsules. These results indicated that the double-layered structure was suitable for the production of microcapsules for initial fire suppression, including highly volatile non-flammable agents with a low boiling point.