bluuebunny/crossref_metadata_2025 huggingface dataset:
Probability Distributions of Asphalt Pavement Responses and Performance under Random Moving Loads and Pavement Temperature
Authors: Ma Xianyong , Shangguan Lingxiao , Si Chundi
Published: January 2023

Asphalt pavements are damaged by traffic load repetitions. Conventionally, the allowed number of load repetitions until pavement failure is calculated based on empirical transfer functions from deterministic pavement mechanical responses to performance. However, the mechanical responses and damage to the pavement are uncertain under a random realistic traffic load and pavement temperature. Therefore, the non-deterministic problem—that is, the probability distributions of asphalt pavement responses and performance under random moving loads and pavement temperatures—was investigated in this study. Random factors include the load pressure, vehicle wandering, speed, and temperature inside the asphalt layer. A combination of the response surface and first-order reliability methodologies was recommended to calculate the probability of mechanical responses at any point within the pavement, for reasons of computational efficiency. The accuracy of this method was verified by a Monte-Carlo simulation. Then, the effects of the mean values and standard deviations of the random factors on the probability distributions of the mechanical responses were discussed. Finally, probability distributions of pavement performance (i.e., probability density distributions of cumulative damage for fatigue failure and rutting after repeated random loads) were calculated using transfer functions and the probability distributions of the mechanical responses; thereby, the failure probability of the pavement after a given number of load repetitions was obtained. The results show that the previous deterministic analysis could not fully reflect the random characteristics of pavement mechanical responses under realistic random moving loads, and the mean values and standard deviations of the random factors have significant effects on the probability distributions of mechanical responses and performance. The failure probability of the pavement after a given number of load repetitions can be used as a guide to reliability-based pavement design. This study on the probability distributions of asphalt pavement responses and performance exhibits the potential to understand pavement behavior and could be beneficial as a complement during reliability-based pavement design.
Vibration Characterization and Fault Diagnosis of a Planetary Gearbox with a Wireless Embedded Sensor
Authors: Huang Li-Te , Chang Jen-Yuan
Published: January 2023

A planetary gearbox is more complex in structure and motion than a gearbox with a fixed shaft, making it difficult to monitor and make a fault diagnosis in practice. Components must be frequently inspected to avoid excessive wear, but there is no simple way to directly measure wear. The most direct method is to log vibration and temperature signals using external sensors. Wireless sensors offer more space advantages than a wired one, so this study developed a measurement system that features a three-axis MEMS accelerometer, temperature sensing and wireless modules that are integrated into a planetary gearbox. Along with the system, a virtual instrument (VI) utilizing graphics programming language LabVIEW was developed to acquire and display data time and frequency domains to detect the gear’s faults. To determine the root cause of vibrations in a planetary gearbox, determine the vibration signal model of amplitude modulation (AM) and frequency modulation (FM) due to gear damage and derive the characteristic frequencies of vibrations for a planetary gearbox, the characteristic frequencies and AMFM modulation were summarized in closed form. Different degrees of each gear damage were then detected in the planetary gearbox. The vibration signal model was validated by experiments to indicate the sideband around the gear meshing frequency and its feasibility for fault diagnosis of a planetary gearbox with the wireless embedded sensor.
Recent Trends in the Characterization and Application Progress of Nano-Modified Coatings in Corrosion Mitigation of Metals and Alloys
Authors: Thakur Abhinay , Kaya Savaş , Kumar Ashish
Published: January 2023

Nanotechnology is a discipline of science and engineering that emphasizes developing, modifying, characterizing, and using nanoscale components in a variety of applications. Owing to their multiple advantages, including adhesion strength, surface hardness, long-term and extra-high-temperature corrosion resistance, improvement of interfacial behavior, etc., nanocoatings are efficiently utilized to minimize the influence of a corrosive environment. Additionally, nanocoatings are often applied in thinner and finer concentrations, allowing for greater versatility in instrumentation and reduced operating and maintenance costs. The exemplary physical coverage of the coated substrate is facilitated by the fine dimensions of nanomaterials and the significant density of their grounded boundaries. For instance, fabricated self-healing eco-sustainable corrosion inhibitors including PAC/CuONPs, PAC/Fe3O4NPs, and PAC/NiONPs, with uniform distributions and particulate sizes of 23, 10, and 43 nm, correspondingly, were effective in producing PAC/MONPs nanocomposites which exhibited IE% of 93.2, 88.1, 96.1, and 98.6% for carbon steel corrosion in 1M HCl at the optimum concentration of 250 ppm. Therefore, in this review, further steps are taken into the exploration of the significant corrosion-mitigation potential and applications of nanomaterial-based corrosion inhibitors and nano-modified coatings, including self-healing nanocoatings, natural source-based nanocoatings, metal/metallic ion-based nanocoatings, and carbon allotrope-based nanocoatings, to generate defensive film and protection against corrosion for several metals and alloys. These have been illuminated through the in-depth discussion on characterization techniques such as scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), atomic force microscopy (AFM), energy dispersive spectroscopy (EDS), etc. After providing a general summary of the various types of nanomaterials and their protective mechanisms in wide corrosive media, we subsequently present a viewpoint on challenges and future directions.
Determining Factors of Fixed Offshore Platform Inspections in Indonesia
Authors: Sidiq Rizky Bahtiar , Utomo Christiono , Silvianita
Published: January 2023

Damage to fixed offshore platforms can affect their structural integrity in addition to environmental conditions. Inspections are important because they aim to detect and control defects or damage to make repairs and carry out maintenance, if necessary, to reduce the possibility of failure. This study aims to determine the factors that affect inspections of fixed offshore platforms in Indonesia. To determine these factors, questionnaires were distributed to experts, including senior practitioners and Ph.D. academics, involved in offshore platforms. For data processing, we first used descriptive statistics based on the mean and SD to indicate the ranking of a factor’s importance. Secondly, we performed factor analyses to summarize the information contained in multiple factors. The two methods are carried out to complement each other in determining the factors formed from the respondents’ assessments. This study finds three factors in inspections of fixed offshore platforms based on the percentage variance: the first factor is environmental conditions, the second is structural design, and the third is anomalies and defects. By knowing these factors, such comprehension can be used as a consideration in conducting inspections. Each factor must be identified because each factor can affect inspections of fixed offshore platforms.
Analysis of the Leap Motion Controller Workspace for HRI Gesture Applications
Authors: Tölgyessy Michal , Dekan Martin , Rodina Jozef , Duchoň František
Published: January 2023

The Leap Motion Controller is a sensor for precise hand tracking; it is a device used for human interaction with computer systems via gestures. The study presented in this paper evaluates its workspace in real-world conditions. An exact replica of a human operator’s hand was used to measure the sensor’s precision, and therefore determine its hand tracking abilities in varying positions above the sensor. The replica was moved randomly across the workspace defined by the manufacturer, and precision was measured in each position. The hand model was placed in the furthest distances from the sensor to find every position where the sensor was still capable of tracking. We found the dimensions of the workspace in some cases exceeded the datasheet values; in other cases, the real workspace was smaller than the proclaimed one. We also computed precision in all positions, which shows tracking reliability. This study serves researchers developing HMI and HRI algorithms as a reference for the real dimensions of the Leap Motion Controller workspace as it provides extra and more precise information compared to the datasheet.
A Biomimetic Polynucleotides–Hyaluronic Acid Hydrogel Promotes the Growth of 3D Spheroid Cultures of Gingival Fibroblasts
Authors: Colangelo Maria Teresa , Vicedomini Maria Luisa , Belletti Silvana , Govoni Paolo , Guizzardi Stefano , Galli Carlo
Published: January 2023

(1) Background: Three-dimensional cultures are useful tools to evaluate regenerative approaches in vitro, as they may mimic the spatial arrangement of cells more closely to natural tissues than routine 2D culture methods. (2) Methods: We investigated the effects of a polynucleotide, hyaluronic acid (PN, HA) compound on 3D spheroid cultures of primary gingival fibroblasts, by measuring their morphology over time, cell viability with Calcein-AM, a fluorescent marker, and cell growth potential by re-plating spheroids in attachment-permissive regular culture plates under routine conditions and following them up for 15 days. (3) Results: PN + HA induced an increase in spheroid size and perimeter and a decrease in spheroid circularity, as cells tended to grow and form small peripheral stacks around the spheroid. Levels of cell viability were also higher in this group. After re-plating, only the spheroids previously stimulated with PN + HA dissolved completely during the second week of culture and colonized the plate, thus indicating the retention of a higher level of viability by the cells forming the whole spheroid with this stimulus. (4) Conclusions: Taken together, our data support the idea that the combination of PN and HA has synergic effects on primary fibroblasts and promotes their viability, the growth of 3D cellular constructs, and the retention of a remarkable proliferative potential over the course of the experimental period, making it a promising compound for further investigations.
Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections
Authors: Olayode Isaac Oyeyemi , Tartibu Lagouge Kwanda , Alex Frimpong Justice
Published: January 2023

In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road intersections. However, transportation researchers have not focused on prediction of vehicular traffic flow at road intersections using hybrid algorithms such as adaptive neuro-fuzzy inference systems optimized by genetic algorithms. In this research, we propose two models, namely the adaptive neuro-fuzzy inference system (ANFIS) and the adaptive neuro-fuzzy inference system optimized by genetic algorithm (ANFIS-GA), to model and predict vehicles at signalized road intersections using the South African public road transportation system. The traffic data used for this research were obtained via up-to-date traffic data equipment. Eight hundred fifty traffic datasets were used for the ANFIS and ANFIS-GA modelling. The traffic data comprised traffic volume (output), speed of vehicles, and time (inputs). We used 70% of the traffic data for training and 30% for testing. The ANFIS and ANFIS-GA results showed training performance of (R2) 0.9709 and 0.8979 and testing performance of (R2) 0.9790 and 0.9980. The results show that ANFIS-GA is more appropriate for modelling and prediction of traffic flow of vehicles at signalized road intersections. This research adds further to our knowledge of the application of hybrid genetic algorithms in traffic-flow prediction of vehicles at signalized road intersections.
Characterization of the Crack and Recrystallization of W/Cu Monoblocks of the Upper Divertor in EAST
Authors: Xi Ya , He Gaoyong , Zan Xiang , Wang Kang , Zhu Dahuan , Luo Laima , Ding Rui , Wu Yucheng
Published: January 2023

The microstructure of and damage to the upper divertor components in EAST were characterized by using metallography, EBSD, and SEM. Under the synergistic effect of heat load and plasma irradiation, cracking, recrystallization, and interface debonding were found in the components of the upper divertor target. The crack propagates downward from the heat loading surface along the heat flux direction, and the crack propagation mode is an intergranular fracture. The thermal loads deposited on the edge of monoblocks raise the temperature higher than the recrystallization temperature of pure tungsten, and the microstructure changes from being in a rolled state to being recrystallized. Additionally, cracks exist in both recrystallized and rolled areas. EBSD boundary maps show that the range of the recrystallization area is determined via the heat flux distribution. The Cu/CuCrZr interface of the cooling components near the thermal loading area is debonded, and the structural integrity is destroyed.
Static Wind Reliability Analysis of Long-Span Highway Cable-Stayed Bridge in Service
Authors: Zhang Tian , Cui Xinjia , Guo Weiwei , Zou Yunfeng , Qin Xuzhe
Published: January 2023

Long-span cable-stayed bridge is a typical wind-sensitive structure because of its light weight and great flexibility. The reliability evaluation of the cable-stayed bridge under wind action becomes a key issue during bridge design and operation. In this paper, taking Xiangshan Harbor highway cable-stayed bridge in Ningbo City in China as the engineering background, ANSYS 15.0 is used to build a finite element model of this bridge to explore its aerostatic stability and influencing parameters, then the static wind reliability of this bridge is analyzed. Firstly, considering the structural nonlinearity and the nonlinearity of static wind load comprehensively, the critical wind speed of aerostatic instability of the Xiangshan Harbor cable-stayed bridge is calculated by the internal and external double iteration method. Then the influence of the initial wind attack angle, steel wire breakage rate of the stay cables, static three-component force coefficients, and cable broken position on the static wind stability of this bridge are discussed. Thus, the variation rule of the critical aerostatic instability wind speed is obtained, and the parameter that has the greatest impact on the static wind stability of the cable-stayed bridge in service is determined. Finally, considering the randomness of these key parameters, they are treated as random variables, Latin hypercube sampling (LHS) method is used to obtain samples of random variables to calculate aerostatic instability wind speed for a given sample by self-written calculation program, and on this basis, static wind reliability analysis is conducted by the checking point method, which provides strong support for the static wind risk evaluation of the existing long-span cable-stayed bridge.
A Scientometric Review of Obstructive Sleep Apnea and Obesity
Authors: Neoh Michelle Jin Yee , Carollo Alessandro , Lim Mengyu , Dimitriou Dagmara , Esposito Gianluca
Published: January 2023

Obstructive sleep apnea (OSA) is a common sleep disorder that has a high prevalence in the obese population. Studies have established the relationship between OSA and a multitude of adverse health outcomes including cardiovascular diseases and metabolic diseases, indicating the nature of OSA as a disorder with high comorbidity and mortality. Thus, OSA is a growing public health concern in the face of rising obesity trends globally. This study conducted a systematic analysis of the scientific literature on OSA from 1977 to 2022 in order to gain a better understanding of major research areas concerning OSA and the connections between these areas. Findings indicate that there are major clusters investigating the relationship between OSA and cardiovascular and metabolic diseases, which are health conditions commonly associated with obesity and have a significant disease burden. The findings from this scientometric analysis also indicate emerging clusters of research into more specific populations such as children with obesity and pregnant women.
Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework for Civilian Urban Air Mobility
Authors: Munir Md. Shirajum , Dipro Sumit Howlader , Hasan Kamrul , Islam Tariqul , Shetty Sachin
Published: January 2023

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and detecting cyber jamming and spoofing attacks. Then, the developed framework analyzes the conditional dependencies based on the Pearson’s correlation coefficient among the control messages for finding the cause of potential attacks based on the outcome of the AI algorithm. This work considers the UAM attitude control scenario for determining jam and spoofing attacks as a use case to validate the proposed framework with a state-of-the-art UAV attack dataset. The experiment results show the efficacy of the proposed framework in terms of around 99.9% accuracy for jamming and spoofing detection with a decision tree, random forests, and KNN while efficiently finding the root cause of the attack.
Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag
Authors: Wang Ting , Wang Huiqin , Wang Ke , Yang Zhe
Published: January 2023

Damaged edges of bone tag images contain external factors such as impurities and damage, which affect the stitching process and lead to repair errors. Therefore, this paper proposes a stitching method based on image edge position feature matching. The objective is to improve the accuracy of image stitching by matching feature points based on the position of the image edge pixel so as to solve the accurate stitching of broken edge contour. In the first step of this method, the image containing the broken edge is preprocessed by edge detection, and the location of the broken edge pixel is proposed. Secondly, the feature descriptors were calculated to extract the shape and texture information of the feature points on the fracture edge. Finally, the feature points are optimized by minimum correction and image mosaic is carried out. In terms of image stitching, pre-registration is performed by finding the feature descriptors that are most similar to the edge of the optimum fracture surface profile. The matching operator is added to the overlapping region to obtain the corrected image, and the panoramic image mosaic of the image fracture surface is performed. The experimental results show that feature descriptor matching can ensure the integrity of the fracture, improve the matching accuracy, optimize the uneven deformation of the fracture, ensure the quality of image stitching, and reduce the degree of image distortion.
Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
Authors: Arjona Rosario , López-González Paula , Román Roberto , Baturone Iluminada
Published: January 2023

Homomorphic encryption is a powerful mechanism that allows sensitive data, such as biometric data, to be compared in a protected way, revealing only the comparison result when the private key is known. This is very useful for non-device-centric authentication architectures with clients that provide protected data and external servers that authenticate them. While many reported solutions do not follow standards and are not resistant to quantum computer attacks, this work proposes a secure biometric authentication scheme that applies homomorphic encryption based on the Classic McEliece public-key encryption algorithm, which is a round 4 candidate of the NIST post-quantum standardization process. The scheme applies specific steps to transform the features extracted from biometric samples. Its use is proposed in a non-device-centric biometric authentication architecture that ensures user privacy. Irreversibility, revocability and unlinkability are satisfied and the scheme is robust to stolen-device, False-Acceptance Rate (FAR) and similarity-based attacks as well as to honest-but-curious servers. In addition to the security achieved by the McEliece system, which remains stable over 40 years of attacks, the proposal allows for very reduced storage and communication overheads as well as low computational cost. A practical implementation of a non-device-centric facial authentication system is illustrated based on the generation and comparison of protected FaceNet embeddings. Experimental results with public databases show that the proposed scheme improves the accuracy and the False-Acceptance Rate of the unprotected scheme, maintaining the False-Rejection Rate, allows real-time execution in clients and servers for Classic McEliece security parameter sets of 128 and 256 bits (mceliece348864 and mceliece6688128, respectively), and reduces storage requirements in more than 90.5% compared to the most reduced-size homomorphic encryption-based schemes with post-quantum security reported in the literature.
UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net
Authors: Wang Xiaodong , Guo Yangming , Lu Nan , He Pei
Published: January 2023

Currently, we are facing an increasing trend in the use of unmanned aerial vehicles (UAV) in various activities, both civilian and military. The application of UAVs in the battlefield has received extensive attention amid a global new military revolution. A UAV cluster is a large and complex real-time feedback system that integrates a communication and sensor network, control system, calculation, and physical process. The heterogeneous UAVs conduct complex behaviors, which requires a comprehensive description and analysis of UAV cluster modeling. The integrated modeling of the heterogeneous UAV cluster is of great significance and value to test and verify the new combat mode. In this paper, we present a novel representation framework based on the Petri nets. We used a spatial-temporal hybrid Petri net to illustrate the discrete state and continuous process of a heterogeneous UAV cluster system, and effectively achieved the fusion of a physical and computational process and interaction event modeling in the cluster system. Finally, the formal verification of UAV cluster attack mission modeling was carried out by UPPAAL, suggesting the proposed modeling method is feasible and effective.
Smart Contract Vulnerability Detection Based on Hybrid Attention Mechanism Model
Authors: Wu Huaiguang , Dong Hanjie , He Yaqiong , Duan Qianheng
Published: January 2023

A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.
Analytical Approximation of the Jackknife Linking Error in Item Response Models Utilizing a Taylor Expansion of the Log-Likelihood Function
Authors: Robitzsch Alexander
Published: January 2023

Linking errors in item response models quantify the dependence on the chosen items in means, standard deviations, or other distribution parameters. The jackknife approach is frequently employed in the computation of the linking error. However, this jackknife linking error could be computationally tedious if many items were involved. In this article, we provide an analytical approximation of the jackknife linking error. The newly proposed approach turns out to be computationally much less demanding. Moreover, the new linking error approach performed satisfactorily for datasets with at least 20 items.
Simulation of the Spatiotemporal Distribution of PM2.5 Concentration Based on GTWR-XGBoost Two-Stage Model: A Case Study of Chengdu Chongqing Economic Circle
Authors: Liu Minghao , Luo Xiaolin , Qi Liai , Liao Xiangli , Chen Chun
Published: January 2023

Natural environmental factors and human activity intensity factors, the two main factors that affect the spatial and temporal distribution of PM2.5 concentration near the surface, have different mechanisms of action on PM2.5 concentration. In this paper, a GTWR-XGBoost two-stage sequential hybrid model is proposed aiming at detecting the expression of spatiotemporal heterogeneity in the traditional machine learning retrieval model of PM2.5 concentration and the difficulty of expressing the complex nonlinear relationship in the statistical regression model. In the first stage, the natural environmental factors are used to predict PM2.5 concentration with spatiotemporal characteristics by collinearity diagnosis method and Geographically and Temporally Weighted Regression method (GTWR). In the second stage, the simulation results in the first stage and the natural factors eliminated through LUR stepwise regression in the first stage are into the XGBoost model together with the human activity intensity factors in the buffer zone with the best correlation coefficient of PM2.5, and finally the temporal and spatial distribution of PM2.5 concentration. Taking the Chengdu Chongqing Economic Circle as an example, the proposed model is used to retrieve PM2.5 concentration and compared with the single GTWR, XGBoost, and coupling model published recently. The experimental results show that the R2, RMSE, and MAE of the GTWR-XGBoost two-stage model cross-validation are 0.92, 5.44 ug·m−3, and 4.12 ug·m−3, respectively. Compared with the above single models, R2 increased by 0.01 and 0.12, and MAE decreased by more than 0.11 and 3.1, respectively. Compared with the coupling model published recently, R2 is increased by 0.02, and MAE is reduced by more than 0.4. In addition, the PM2.5 concentration in Chengdu Chongqing showed obvious seasonal temporal and spatial changes, and the influence ratios of natural environmental factors and human activity intensity activities factors on PM2.5 were 0.66 and 0.34. The results show that the GTWR-XGBoost two-stage Model can not only describe the heterogeneity and objectively reflect the complex nonlinear relationship between the phenomenon and the influencing factors, but also enhance the interpretability of the phenomenon when simulating the spatiotemporal distribution characteristics of PM2.5 concentration.
Recent Progress and Perspectives of Solid State Na-CO2 Batteries
Authors: Wang Zelin , Sun Chunwen , Lu Liang , Jiao Lifang
Published: January 2023

Solid state Na-CO2 batteries are a kind of promising energy storage system, which can use excess CO2 for electrochemical energy storage. They not only have high theoretical energy densities, but also feature a high safety level of solid-state batteries and low cost owing to abundant sodium metal resources. Although many efforts have been made, the practical application of Na-CO2 battery technology is still hampered by some crucial challenges, including short cycle life, high charging potential, poor rate performance and lower specific full discharge capacity. This paper systematically reviews the recent research advances in Na-CO2 batteries in terms of understanding the mechanism of CO2 reduction, carbonate formation and decomposition reaction, design strategies of cathode electrocatalysts, solid electrolytes and their interface design. In addition, the application of advanced in situ characterization techniques and theoretical calculation of metal–CO2 batteries are briefly introduced, and the combination of theory and experiment in the research of battery materials is discussed as well. Finally, the opportunities and key challenges of solid-state Na-CO2 electrochemical systems in the carbon-neutral era are presented.
A Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis
Authors: Subramani Neelakandan , Veerappampalayam Easwaramoorthy Sathishkumar , Mohan Prakash , Subramanian Malliga , Sambath Velmurugan
Published: January 2023

Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As a result of technology advances (“big” data) and an increasing trend toward institutionalizing ethics regulation, social network analysis (SNA) research is currently confronted with serious ethical challenges. A significant percentage of human interactions occur on social networks online. In this instance, content freshness is essential, as content popularity declines with time. Therefore, we investigate how influencer content (i.e., posts) generates interactions, as measured by the number of likes and reactions. The Gradient Boosted Decision Tree (GBDT) and the Chaotic Gradient-Based Optimizer are required for estimation (CGBO). Using earlier group interactions, we develop the Influencers Prediction issue in this study’s setting of SN-created groups. We also provide a GBDT-CGBO framework and an efficient method for identifying users with the ability to influence the future behaviour of others. Our contribution is based on logic, experimentation and analytic techniques. The goal of this paper is to find domain-based social influencers using a framework that uses semantic analysis and machine learning modules to measure and predict users’ credibility in different domains and at different times. To solve these problems, future research will have to focus on co-authorship networks and economic networks instead of online social networks. The results show that our GBDT-CGBO method is both useful and effective. Based on the test results, the GBDT-CGBO model can correctly classify unclear data, which speeds up processing and makes it more efficient.
Bioactive Compounds in Different Coffee Beverages for Quality and Sustainability Assessment
Authors: Gobbi Laura , Maddaloni Lucia , Prencipe Sabrina Antonia , Vinci Giuliana
Published: January 2023

Coffee is one of the most widely consumed beverages worldwide, mainly due to its organoleptic, and psychoactive properties. Coffee brewing techniques involve the use of different extraction/infusion conditions (i.e., time, temperature, pressure, water/powder ratio, etc.), which can influence the quality of the final product. The study aimed to analyze the effect of four brewing coffee techniques (industrial espresso machine, Moka machine, pod machine, and capsule machine), which are the most used coffee brewing methods in Italy, on the quality and safety of the coffee brews, taking into account the profile of biogenic amines (BAs), total polyphenol content (TPC), total flavonoid content (TFC) and anti-radical activity (DPPH and ABTS assay). Eight coffee powders and brewed beverages from two different brands belonging to the 100% Arabica variety (country of origin Brazil) were analysed. The brewing techniques all resulted in a reduction of both BA content (27–30%), TPC (55–60%), TFC (50–55%), and anti-radical assays (45–50%) in coffee beverages compared to ground coffee samples. The study also showed that Moka is the method that yields the highest TPC (2.71–3.52 mg GAE/g coffee powder) and TFC (8.50–8.60 mg RUT/g coffee powder) content and highest anti-radical capacity in coffee beverages. The multivariate statistical analysis revealed a difference between coffee powder and infusions and coffee infusions obtained by different extraction techniques. Moreover, an analysis of the environmental impacts related to the different coffee preparation methods examined was conducted. This was performed by applying the Life Cycle Assessment (LCA) methodology through SimaPro v.9.2.2. software.
Radiotherapy Advances in Renal Disease—Focus on Renal Ischemic Preconditioning
Authors: Khbouz Badr , Gu Shiyang , Pinto Coelho Tiago , Lallemand François , Jouret François
Published: January 2023

Ionizing irradiation is widely applied as a fundamental therapeutic treatment in several diseases. Acute kidney injury (AKI) represents a global public health problem with major morbidity and mortality. Renal ischemia/reperfusion (I/R) is the main cause of AKI. I/R injury occurs when blood flow to the kidney is transiently interrupted and then restored. Such an ischemic insult significantly impairs renal function in the short and long terms. Renal ischemic preconditioning (IPC) corresponds to the maneuvers intended to prevent or attenuate the ischemic damage. In murine models, irradiation-induced preconditioning (IP) renders the renal parenchyma resistant to subsequent damage by activating defense pathways involved in oxidative stress, angiogenesis, and inflammation. Before envisioning translational applications in patients, safe irradiation modalities, including timing, dosage, and fractionation, need to be defined.
In Vitro Models of Ovarian Cancer: Bridging the Gap between Pathophysiology and Mechanistic Models
Authors: Lopez Elliot , Kamboj Sahil , Chen Changchong , Wang Zixu , Kellouche Sabrina , Leroy-Dudal Johanne , Carreiras Franck , Lambert Ambroise , Aimé Carole
Published: January 2023

Ovarian cancer (OC) is a disease of major concern with a survival rate of about 40% at five years. This is attributed to the lack of visible and reliable symptoms during the onset of the disease, which leads over 80% of patients to be diagnosed at advanced stages. This implies that metastatic activity has advanced to the peritoneal cavity. It is associated with both genetic and phenotypic heterogeneity, which considerably increase the risks of relapse and reduce the survival rate. To understand ovarian cancer pathophysiology and strengthen the ability for drug screening, further development of relevant in vitro models that recapitulate the complexity of OC microenvironment and dynamics of OC cell population is required. In this line, the recent advances of tridimensional (3D) cell culture and microfluidics have allowed the development of highly innovative models that could bridge the gap between pathophysiology and mechanistic models for clinical research. This review first describes the pathophysiology of OC before detailing the engineering strategies developed to recapitulate those main biological features.
The Potential Role of Integrin Signaling in Memory and Cognitive Impairment
Authors: Biose Ifechukwude Joachim , Ismael Saifudeen , Ouvrier Blake , White Amanda Louise , Bix Gregory Jaye
Published: January 2023

Dementia currently has no cure and, due to the increased prevalence and associated economic and personal burden of this condition, current research efforts for the development of potential therapies have intensified. Recently, targeting integrins as a strategy to ameliorate dementia and other forms of cognitive impairment has begun to gain traction. Integrins are major bidirectional signaling receptors in mammalian cells, mediating various physiological processes such as cell–cell interaction and cell adhesion, and are also known to bind to the extracellular matrix. In particular, integrins play a critical role in the synaptic transmission of signals, hence their potential contribution to memory formation and significance in cognitive impairment. In this review, we describe the physiological roles that integrins play in the blood–brain barrier (BBB) and in the formation of memories. We also provide a clear overview of how integrins are implicated in BBB disruption following cerebral pathology. Given that vascular contributions to cognitive impairment and dementia and Alzheimer’s’ disease are prominent forms of dementia that involve BBB disruption, as well as chronic inflammation, we present current approaches shown to improve dementia-like conditions with integrins as a central focus. We conclude that integrins are vital in memory formation and that their disruption could lead to various forms of cognitive impairment. While further research to understand the relationships between integrins and memory is needed, we propose that the translational relevance of research efforts in this area could be improved through the use of appropriately aged, comorbid, male and female animals.
Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches to Classify Lung Diseases
Authors: Azam Sami , Rafid A.K.M. Rakibul Haque , Montaha Sidratul , Karim Asif , Jonkman Mirjam , De Boer Friso
Published: January 2023

Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images.
Insights into Pathogenesis, Nutritional and Drug Approach in Sarcopenia: A Systematic Review
Authors: Mellen Rodrigo Haber , Girotto Otávio Simões , Marques Eduarda Boni , Laurindo Lucas Fornari , Grippa Paulo Cesar , Mendes Claudemir Gregório , Garcia Lorena Natalino Haber , Bechara Marcelo Dib , Barbalho Sandra Maria , Sinatora Renata Vargas , Haber Jesselina Francisco dos Santos , Flato Uri Adrian P. , Bueno Patricia Cincotto dos Santos , Detregiachi Claudia Rucco Penteado , Quesada Karina
Published: January 2023

Sarcopenia is a multifactorial condition related to the loss of muscle mass and strength due to aging, eating habits, physical inactivity, or even caused by another disease. Affected individuals have a higher risk of falls and may be associated with heart disease, respiratory diseases, cognitive impairment, and consequently an increased risk of hospitalization, in addition to causing an economic impact due to the high cost of care during the stay in hospitals. The standardization of appropriate treatment for patients with sarcopenia that could help reduce pathology-related morbidity is necessary. For these reasons, this study aimed to perform a systematic review of the role of nutrition and drugs that could ameliorate the health and quality of life of sarcopenic patients and PRISMA guidelines were followed. Lifestyle interventions have shown a profound impact on sarcopenia treatment but using supplements and different drugs can also impact skeletal muscle maintenance. Creatine, leucine, branched-chain amino acids, omega 3, and vitamin D can show benefits. Although with controversial results, medications such as Metformin, GLP-1, losartan, statin, growth hormone, and dipeptidyl peptidase 4 inhibitors have also been considered and can alter the sarcopenic’s metabolic parameters, protect against cardiovascular diseases and outcomes, while protecting muscles.
Quantifying the Leaping Motion Using a Self-Propelled Bionic Robotic Dolphin Platform
Authors: Yu Junzhi , Wang Tianzhu , Chen Di , Meng Yan
Published: January 2023

Kinematic analysis of leaping motions can provide meaningful insights into unraveling the efficient and agile propulsive mechanisms in dolphin swimming. However, undisturbed kinematic examination of live dolphins has been very scarce due to the restriction of close-up biological observation with a motion capture system. The main objective of this study is to quantify the leaping motion of a self-propelled bionic robotic dolphin using a combined numerical and experimental method. More specifically, a dynamic model was established for the hydrodynamic analysis of a changeable submerged portion, and experimental data were then employed to identify hydrodynamic parameters and validate the effectiveness. The effects of wave-making resistance were explored, indicating that there is a varying nonlinear relationship between power and speed at different depths. In addition, the wave-making resistance can be reduced significantly when swimming at a certain depth, which leads to a higher speed and less consumed power. Quantitative estimation of leaping motion is carried out, and the results suggest that with increase of the exiting velocity and angle, the maximum height of the center of mass (CM) increases as well; furthermore, a small exiting angle usually requires a much larger exiting velocity to achieve a complete exiting motion. These findings provide implications for optimizing motion performance, which is an integral part of underwater operations in complex aquatic environments.
Multielectrode Arrays as a Means to Study Exocytosis in Human Platelets
Authors: González Brito Rosalía , Montenegro Pablo , Méndez Alicia , Carabelli Valentina , Tomagra Giulia , Shabgahi Ramtin E. , Pasquarelli Alberto , Borges Ricardo
Published: January 2023

Platelets are probably the most accessible human cells to study exocytosis by amperometry. These cell fragments accumulate biological amines, serotonin in particular, using similar if not the same mechanisms as those employed by sympathetic, serotoninergic, and histaminergic neurons. Thus, platelets have been widely recognized as a model system to study certain neurological and psychiatric diseases. Platelets release serotonin by exocytosis, a process that entails the fusion of a secretory vesicle to the plasma membrane and that can be monitored directly by classic single cell amperometry using carbon fiber electrodes. However, this is a tedious technique because any given platelet releases only 4–8 secretory δ-granules. Here, we introduce and validate a diamond-based multielectrode array (MEA) device for the high-throughput study of exocytosis by human platelets. This is probably the first reported study of human tissue using an MEA, demonstrating that they are very interesting laboratory tools to assess alterations to exocytosis in neuropsychiatric diseases. Moreover, these devices constitute a valuable platform for the rapid testing of novel drugs that act on secretory pathways in human tissues.
Complete Breast Cancer Detection and Monitoring System by Using Microwave Textile Based Antenna Sensors
Authors: Elsheakh Dalia N. , Mohamed Rawda A. , Fahmy Omar M. , Ezzat Khaled , Eldamak Angie R.
Published: January 2023

This paper presents the development of a new complete wearable system for detecting breast tumors based on fully textile antenna-based sensors. The proposed sensor is compact and fully made of textiles so that it fits conformably and comfortably on the breasts with dimensions of 24 × 45 × 0.17 mm3 on a cotton substrate. The proposed antenna sensor is fed with a coplanar waveguide feed for easy integration with other systems. It realizes impedance bandwidth from 1.6 GHz up to 10 GHz at |S11| ≤ −6 dB (VSWR ≤ 3) and from 1.8 to 2.4 GHz and from 4 up to 10 GHz at |S11| ≤ −10 dB (VSWR ≤ 2). The proposed sensor acquires a low specific absorption rate (SAR) of 0.55 W/kg and 0.25 W/kg at 1g and 10 g, respectively, at 25 dBm power level over the operating band. Furthermore, the proposed system utilizes machine-learning algorithms (MLA) to differentiate between malignant tumor and benign breast tissues. Simulation examples have been recorded to verify and validate machine-learning algorithms in detecting tumors at different sizes of 10 mm and 20 mm, respectively. The classification accuracy reached 100% on the tested dataset when considering |S21| parameter features. The proposed system is vision as a “Smart Bra” that is capable of providing an easy interface for women who require continuous breast monitoring in the comfort of their homes.