bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural Requirements for the Neuroprotective and Anti-Inflammatory Activities of the Flavanone Sterubin
Authors: Liang Zhibin , Maher Pamela
Published: November 2022

Alzheimer’s disease (AD) is the most frequent age-associated disease with no treatments that can prevent, delay, slow, or stop its progression. Thus, new approaches to drug development are needed. One promising approach is the use of phenotypic screening assays that can identify compounds that have therapeutic efficacy in target pathways relevant to aging and cognition, as well as AD pathology. Using this approach, we identified the flavanone sterubin, from Yerba santa (Eriodictyon californicum), as a potential drug candidate for the treatment of AD. Sterubin is highly protective against multiple initiators of cell death that activate distinct death pathways, potently induces the antioxidant transcription factor Nrf2, and has strong anti-inflammatory activity. Moreover, in a short-term model of AD, it was able to prevent decreases in short- and long-term memory. In order to better understand which key chemical functional groups are essential to the beneficial effects of sterubin, we compared the activity of sterubin to that of seven closely related flavonoids in our phenotypic screening assays. Surprisingly, only sterubin showed both potent neuroprotective activity against multiple insults as well as strong anti-inflammatory activity against several distinct inducers of inflammation. These effects correlated directly with the ability of sterubin to strongly induce Nrf2 in both nerve and microglial cells. Together, these results define the structural requirements underlying the neuroprotective and anti-inflammatory effects of sterubin and they provide the basis for future studies on new compounds based on sterubin.
On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification
Authors: Wunderlich Dominik , Bernau Daniel , Aldà Francesco , Parra-Arnau Javier , Strufe Thorsten
Published: November 2022

Hierarchical text classification consists of classifying text documents into a hierarchy of classes and sub-classes. Although Artificial Neural Networks have proved useful to perform this task, unfortunately, they can leak training data information to adversaries due to training data memorization. Using differential privacy during model training can mitigate leakage attacks against trained models, enabling the models to be shared safely at the cost of reduced model accuracy. This work investigates the privacy–utility trade-off in hierarchical text classification with differential privacy guarantees, and it identifies neural network architectures that offer superior trade-offs. To this end, we use a white-box membership inference attack to empirically assess the information leakage of three widely used neural network architectures. We show that large differential privacy parameters already suffice to completely mitigate membership inference attacks, thus resulting only in a moderate decrease in model utility. More specifically, for large datasets with long texts, we observed Transformer-based models to achieve an overall favorable privacy–utility trade-off, while for smaller datasets with shorter texts, convolutional neural networks are preferable.
Mobile Cyber Forensic Investigations of Web3 Wallets on Android and iOS
Authors: Mirza Mohammad Meraj , Ozer Akif , Karabiyik Umit
Published: November 2022

Constant advancements in technology have a significant impact on our everyday lives and the ecosystem in which we live. The growing popularity of cryptocurrencies (e.g., Bitcoin and Ethereum), along with Non-Fungible Tokens (NFTs), which are founded on blockchain technology, has opened the way for these blockchain projects to be integrated into a wide range of other kinds of applications (apps). Today, cryptocurrencies are used as a popular method of payment online; however, their popularity on the dark Web is also increasing. For example, they can be used to buy and perform various illegal activities among criminals due to their anonymity. Web3 cryptocurrency wallets, used to store cryptocurrencies, have not been studied as thoroughly as many other apps from a digital forensic perspective on mobile devices, given the increasing number of these services and apps today for many platforms, including the leading mobile operating systems (i.e., iOS and Android). Therefore, the purpose of this research is to guide investigators to unlock the full potential of popular cryptocurrency Web3 wallets, Trust Wallet and Metamask, to understand what can be recovered, and to look at areas where there are knowledge gaps. We digitally analyzed and forensically examined two mobile wallets that do not require any personal identifiers to register and are widely used for Web3 cryptocurrencies on Android and iOS devices. We review the digital evidence we have collected and discuss the implications of the forensic tools we have used. Finally, we propose a proof of concept extension to the iOS Logs, Events, And Plists Parser (iLEAPP) tool to automatically recover artifacts.
Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
Authors: Burakovsky Leonid , Preston Dean L.
Published: November 2022

Quantum melting is the phenomenon of cold (zero-temperature) melting of a pressure-ionized substance which represents a lattice of bare ions immersed in the background of free electrons, i.e., the so-called one-component plasma (OCP). It occurs when the compression of the substance corresponds to the zero-point fluctuations of its ions being so large that the ionic ordered state can no longer exist. Quantum melting corresponds to the classical melting curve reaching a turnaround point beyond which it starts going down and eventually terminates, when zero temperature is reached, at some critical density. This phenomenon, as well as the opposite phenomenon of quantum crystallization, may occur in dense stellar objects such as white dwarfs, and may play an important role in their evolution that requires a reliable thermoelasticity model for proper physical description. Here we suggest a modification of our unified analytic melt-shear thermoelasticity model in the region of quantum melting, and derive the corresponding Grüneisen parameters. We demonstrate how the new functional form for the cold shear modulus can be combined with a known equation of state. One of the constituents of the new model is the melting curve of OCP crystal which we also present. The inclusion of quantum melting implies that the modified model becomes applicable in the entire density range of the existence of the solid state, up to the critical density of quantum melting above which the solid state does not exist. Our approach can be generalized to model melting curves and cold shear moduli of different solid phases of a multi-phase material over the corresponding ranges of mechanical stability.
Finger Vein and Inner Knuckle Print Recognition Based on Multilevel Feature Fusion Network
Authors: Jiang Li , Liu Xianghuan , Wang Haixia , Zhao Dongdong
Published: November 2022

Multimodal biometric recognition involves two critical issues: feature representation and multimodal fusion. Traditional feature representation requires complex image preprocessing and different feature-extraction methods for different modalities. Moreover, the multimodal fusion methods used in previous work simply splice the features of different modalities, resulting in an unsatisfactory feature representation. To address these two problems, we propose a Dual-Branch-Net based recognition method with finger vein (FV) and inner knuckle print (IKP). The method combines convolutional neural network (CNN), transfer learning, and triplet loss function to complete feature representation, thereby simplifying and unifying the feature-extraction process of the two modalities. Dual-Branch-Net also achieves deep multilevel fusion of the two modalities’ features. We assess our method on a public FV and IKP homologous multimodal dataset named PolyU-DB. Experimental results show that the proposed method performs best and achieves an equal error rate (EER) of the recognition result of 0.422%.
Efficacy of Mask Wearing in Preventing the Deleterious Health Effects of the Ionic Components of PM2.5-Possibility Seen in Allergic Patients
Authors: Odajima Hiroshi , Matsuzaki Hiroshi , Akamine Yuko , Kojima Kaoru , Murakami Yoko , Yoshino Ayako , Takami Akinori , Hayakawa Kazuichi , Hara Akinori , Nakamura Hiroyuki
Published: November 2022

Mask wearing can protect health care workers. However, its efficacy in avoiding the health effects of air pollution is not well studied. Recently, we evaluated the relationship between air pollution and health status or peak expiratory flow rate (PEFR). At the beginning of that study, about 20% of people were wearing masks. However, in the same period one year later, most people were wearing masks because of the COVID-19 pandemic. Therefore, in this study, we examined the relationship between mask wearing, concentration of ionic components of PM2.5, respiratory symptoms, and PEFR. Seventy-two patients in Fukuoka City with asthma and allergic diseases measured PEFR daily and recorded respiratory symptoms and mask wearing in a diary. We measured the ionic components of PM2.5 in Fukuoka City and examined the relationships between patient measurements, mask wearing, and air pollution. Univariate analysis showed that, in 2020, but not in 2021, some ionic components of PM2.5, i.e., Cl−, NO3−, Na+, and Mg2+, were significantly correlated with the frequency of cough and nasal symptoms and a decrease in PEFR. Some ionic components of PM2.5 may increase the frequency of respiratory symptoms and decrease PEFR in patients with allergic diseases; however, this tendency is not seen when masks are used. The use of masks may be one of the factors that attenuate the effects of PM2.5 ionic components.
Aquila Optimization with Transfer Learning Based Crowd Density Analysis for Sustainable Smart Cities
Authors: Al Duhayyim Mesfer , Alabdulkreem Eatedal , Tarmissi Khaled , Aljebreen Mohammed , El Khier Bothaina Samih Ismail Abou , Zamani Abu Sarwar , Yaseen Ishfaq , I. Eldesouki Mohamed
Published: November 2022

Video surveillance in smart cities provides efficient city operations, safer communities, and improved municipal services. Object detection is a computer vision-based technology, which is utilized for detecting instances of semantic objects of a specific class in digital videos and images. Crowd density analysis is a widely used application of object detection, while crowd density classification techniques face complications such as inter-scene deviations, non-uniform density, intra-scene deviations and occlusion. The convolution neural network (CNN) model is advantageous. This study presents Aquila Optimization with Transfer Learning based Crowd Density Analysis for Sustainable Smart Cities (AOTL-CDA3S). The presented AOTL-CDA3S technique aims to identify different kinds of crowd densities in the smart cities. For accomplishing this, the proposed AOTL-CDA3S model initially applies a weighted average filter (WAF) technique for improving the quality of the input frames. Next, the AOTL-CDA3S technique employs an AO algorithm with the SqueezeNet model for feature extraction. Finally, to classify crowd densities, an extreme gradient boosting (XGBoost) classification model is used. The experimental validation of the AOTL-CDA3S approach is tested by means of benchmark crowd datasets and the results are examined under distinct metrics. This study reports the improvements of the AOTL-CDA3S model over recent state of the art methods.
Evaluation of the Sustainable Development Goals in the Diagnosis and Prediction of the Sustainability of Projects Aimed at Local Communities in Latin America and the Caribbean
Authors: García Villena Eduardo , Pascual Barrera Alina , Álvarez Roberto Marcelo , Dzul López Luís Alonso , Tutusaus Pifarré Kilian , Vidal Mazón Juan Luís , Miró Vera Yini Airet , Brie Santiago , López Flores Miguel A.
Published: November 2022

The purpose of this article is to help to bridge the gap between sustainability and its application to project management by developing a methodology based on artificial intelligence to diagnose, classify, and forecast the level of sustainability of a sample of 186 projects aimed at local communities in Latin American and Caribbean countries. First, the compliance evaluation with the Sustainable Development Goals (SDGs) within the framework of the 2030 Agenda served to diagnose and determine, through fuzzy sets, a global sustainability index for the sample, resulting in a value of 0.638, in accordance with the overall average for the region. Probabilistic predictions were then made on the sustainability of the projects using a series of supervised learning classifiers (SVM, Random Forest, AdaBoost, KNN, etc.), with the SMOTE resampling technique, which provided a significant improvement toward the results of the different metrics of the base models. In this context, the Support Vector Machine (SVM) + SMOTE was the best classification algorithm, with accuracy of 0.92. Lastly, the extrapolation of this methodology is to be expected toward other realities and local circumstances, contributing to the fulfillment of the SDGs and the development of individual and collective capacities through the management and direction of projects.
Foreword to the Special Issue on Deep Learning and Neuromorphic Chips
Authors: Fan Xuemeng , Zhang Yishu
Published: November 2022

With the advent of the Internet of Things and the era of big data, the ability of machine data processing to reach the level of human brain cognition and learning is an important goal in the field of Internet information technology, including cloud computing, data mining, machine learning, and artificial intelligence (AI) [...]
Liquid Nanofilms’ Condensation Inside a Heat Exchanger by Mixed Convection
Authors: Nasr Abdelaziz , Al-Ghamdi Abdulmajeed S.
Published: November 2022

Liquid nanofilm is used in industrial applications, such as heat exchangers, water desalination systems, heat pumps, distillation systems, cooling systems, and complex engineering systems. The present work focuses on the numerical investigation of the condensation of falling liquid film containing different types of nanoparticles with a low-volume fraction. The nanofluid film falls inside a heat exchanger by mixed convection. The heat exchanger is composed of two parallel vertical plates. One of the plates is wetted and heated, while the other plate is isothermal and dry. The effect of the dispersion of the Cu or Al nanoparticles in the liquid on the heat exchange, mass exchange, and condensation process was analysed. The results showed that the heat transfer was enhanced by the dispersion of the nanoparticles in the water. The copper–water nanofluid presented the highest efficiency compared to the aluminium–water nanofluid and to the basic fluid (pure water) in terms of the heat and mass exchange.
Sudden Event Prediction Based on Event Knowledge Graph
Authors: Li Yanhao , Liu Wei
Published: November 2022

Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. To cope with the situation of missing contexts, we propose a representation learning method based on the heterogeneous graph transformer. We generate event scenario representations from arguments of the initial event and other related concurrent events for the event prediction. We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class. The event classes with scores higher than a priori values are adopted as prediction outcomes. In this paper, we create an event knowledge graph in the domain of transportation for an event prediction experiment. The experimental results show that the HGEP model can effectively make predictions with event scenario representations and has a more accurate matching rate and higher precision than the baseline model.
Impact of Slope Orientation on Inlet Spacing: Gutter Flow Analyses
Authors: Cavdar Sevgi , Uyumaz Ali
Published: November 2022

A roadway’s capacity to drain itself is of utmost importance for the safety and comfort of its users. Standing water and any amount of channelized flow on roadways create nuisances to the users, and the extent of encroachment into the lanes and the water-film thickness over the lanes are crucial for motorists with relatively high speed. Guidelines cover a wide range of subjects from size and type of inlets, which capture the channelized flow for conveyance into enclosed drains, to the decision for slope orientation, but the guidelines seem to lack in checking the depth of channelized flow. HEC-22 (the urban drainage design manual of US Department of Transportation) endorses limiting the flow depths to curb height (as if the concern is no longer the roadway users) and fixes the criterion for the inlet spacing (restricted to 90 to 150 m) to maximum allowable flow spreads. This study analyzed the maximum allowable inlet spacing via setting three criteria: fixed maximums to flow depth, spread for the channel flow, and to over-lane water-film thickness. The impact of slope orientation on inlet spacing is tested along with some other factors for roadways of two types (local and highway). The results were graphed for various uniform slope orientations under a wide range of rainfall intensities for the determined inlet spacing values. This was performed by combining a kinematic wave equation solution to dismiss the conditions that lead to hydroplaning depths when using the Rational Method and Manning’s equation to obtain water depths and inlet spacings for an inlet of full capture capacity. It is found that the allowable spacing values do not constitute any major restrictions in highway setting (3 m shoulder) in terms of recommended spacing. In the local setting, however, with a maximum spread of 1.8 m, maximum allowable inlet spacing becomes a limitation in many orientations, and slope optimization under such conditions becomes crucial at times when providing the same spacing for two orientations.
Application of Digital Twins and Building Information Modeling in the Digitization of Transportation: A Bibliometric Review
Authors: Gao Chao , Wang Jianwei , Dong Shi , Liu Zhizhen , Cui Zhiwei , Ma Ningyuan , Zhao Xiyang
Published: November 2022

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models.
Preliminary Investigation of Geopolymer Foams as Coating Materials
Authors: Kaczmarski Krzysztof , Pławecka Kinga , Kozub Barbara , Bazan Patrycja , Łach Michał
Published: November 2022

Various types of coatings are applied to the surface of an object or substrate to improve surface properties or extend service life, which in turn is associated with cost reductions. The main objective of this study was to develop a technique for the additive application of foamed geopolymers to existing structures and vertical surfaces. The base material was a fly ash-based geopolymer modified with sand. Hydrogen peroxide and aluminum powder were used as foaming agents. In this study, the feasibility of using an air gun with variable nozzles to apply the layers of foamed geopolymers was assessed, and the effects of nozzle diameter and the spray gun’s operating pressure were analyzed. The next stage of the study was a visual assessment of the layering of the foamed material. The foamed geopolymer layering tests verified the occurrence of the foaming process, and the applied geopolymer surface showed a reasonably good adhesive bond with the vertical wall. In addition, in this paper, we present the laser particle size results of the base materials and their oxide composition. In addition, thermal conductivity tests for the foamed geopolymer materials, compressive strength tests, and microstructure analysis via scanning electron microscopy were carried out.
Network Calculus Approach for Packet Delay Variation Analysis of Multi-Hop Wired Networks
Authors: Gore Rahul Nandkumar , Lisova Elena , Åkerberg Johan , Björkman Mats
Published: November 2022

The Industrial Internet of Things (IIoT) has revolutionized businesses by changing the way data are used to make products and services more efficient, reliable, and profitable. To achieve the improvement goals, the IIoT must guarantee the real-time performance of industrial applications such as motion control, by providing stringent quality of service (QoS) assurances for their (industrial applications) communication networks. An application or service may malfunction without adequate network QoS, resulting in potential product failures. Since an acceptable end-to-end delay and low jitter or packet delay variation (PDV) are closely related to quality of service (QoS), their impact is significant in ensuring the real-time performance of industrial applications. Although a communication network topology ensures certain jitter levels, its real-life performance is affected by dynamic traffic due to the changing number of devices, services, and applications present in the communication network. Hence, it is essential to study the jitter experienced by real-time traffic in the presence of background traffic and how it can be maintained within the limits to ensure a certain level of QoS. This paper presents a probabilistic network calculus approach that uses moment-generating functions to analyze the delay and PDV incurred by the traffic flows of interest in a wired packet switched multi-stage network. The presented work derives closed-form, end-to-end, probabilistic performance bounds for delay and PDV for several servers in series in the presence of background traffic. The PDV analysis conducted with the help of a Markovian traffic model for background traffic showed that the parameters from the background traffic significantly impact PDV and that PDV can be maintained under the limits by controlling the shape of the background traffic. For the studied configurations, the model parameters can change the PDV bound from 1 ms to 100 ms. The results indicated the possibility of using the model parameters as a shaper of the background traffic. Thus, the analysis can be beneficial in providing QoS assurances for real-time applications.
Inflammatory, Reactive, and Hypersensitivity Lesions Potentially Due to Metal Nanoparticles from Dental Implants and Supported Restorations: An Umbrella Review
Authors: Di Spirito Federica , Lo Giudice Roberto , Amato Massimo , Di Palo Maria Pia , D’Ambrosio Francesco , Amato Alessandra , Martina Stefano
Published: November 2022

The present umbrella review aimed to assess the prevalence of cases diagnosed with lesions potentially due to Titanium (alloy) and other metal nanoparticles released from dental implants and implant-supported restorations, characterizing lesions’ macroscopic, imaging, and microscopic features. Secondary aims were to categorize the reported lesions as resembling or ascribable to peri-implant mucositis and peri-implantitis, reactive lesions of the peri-implant mucosa, or hypersensitivity reactions, and to evaluate their relationship with cases’, dental implants, and implant-supported restorations’ characteristics, and with the evidence of Titanium allergy. The study protocol, developed in advance and compliant with the PRISMA statement, was registered on PROSPERO (CRD42022354676). Systematic reviews were searched through the Web of Science, Scopus, MEDLINE/PubMed, Cochrane library databases, and the PROSPERO register until 19 August 2022; reference lists were also screened. Data from four systematic reviews of critically low/low quality (AMSTAR 2), one including a meta-analysis, were analyzed qualitatively. An overall prevalence of 16.9% of cases was estimated. Reported lesions resembled or were ascribable to peri-implant mucositis and peri-implantitis (55.17%), reactive lesions (17.22%), and hypersensitivity reactions (24.12%); no oral contact lichenoid lesions were described. Titanium allergy was hardly and heterogeneously investigated. Due to the severely incomplete data, no definitive conclusions could be drawn on the potential role of cases’ and implant characteristics and Titanium allergy on lesions onset, development, and treatment responsiveness.
Influence of Xenon–Fluorine–Sulfur Hexafluoride (Xe+–F−–SF6−) and Argon-Fluorine-Sulfur Hexafluoride (Ar+–F−–SF6−) Streaming on Dust Surface Potential (DSP) That Has Cairn–Tsallis Distributed Plasmas
Authors: Raffah Bahaaudin M. , Abid A. A. , Al-Hadeethi Y. , Somaily H. H.
Published: November 2022

The dust grain surface potential is examined analytically and numerically in dusty plasmas containing negative/positive ion species by using the Cairn Tsallis (non-Maxwellian) dusty plasma. The equations for the dust-charging process are derived to solve the current balance equation for the xenon–fluorine–sulfur hexafluoride and argon–fluorine–sulfur hexafluoride plasmas. The charging process affected by plasma properties such as spectral indices α and q, in addition to positive ion streaming (UAr+ and UXe+) and negative ion streaming (UF− and USF6−) of both types of plasmas, is examined. Our findings suggest that considering a wide range of Xe+−F−−SF6− and Ar+−F−−SF6− masses is critical for understanding plasma physics, specifically multi-component plasmas.
Experimental Study on Shear Behavior of Interface between Different Soil Materials and Concrete under Variable Normal Stress
Authors: Liu Hongyuan , Zhu Mingxing , Li Xiaojuan , Dai Guoliang , Yin Qian , Liu Jing , Ling Chen
Published: November 2022

At present, the interface shear test is mainly used to evaluate the anti-sliding performance of the new foundation base. However, the traditional interface shear test has certain limitations in simulating the load change during the construction process and cannot accurately simulate the interface shear characteristics between the structure and the soil under the continuous change of the normal stress. Based on the self-developed large-scale interface shear equipment, this paper carried out the interface shear test and mechanism research of cement soil concrete, sand concrete, clay concrete and other materials in different curing cycles under the loading and unloading modes of variable normal stress repeated steps and continuous loading modes of variable normal stress steps. In addition, this paper deduced the formula of the minimum interface friction coefficient based on Mohr–Coulomb criterion. The experimental results show that the curing effect of cement soil can significantly improve the shear mechanical properties of the interface, and the friction coefficient of the cement soil concrete interface will also increase step by step with the increase of the curing time of the cement soil. The sliding shear surface can be remolded under the preloading of normal pressure, so that the interface shear characteristics of each shear material under repeated loading and unloading can be approximately equal to the interface shear characteristics of multiple equivalent materials under separate loading. In the case of a continuous change of normal stress, the rapid increase of normal stress will lead to accelerated entry into the limit shear state, resulting in plastic failure of the shear plane as a whole. In the engineering with a continuous change of stress, the interface shear friction coefficient of the material with high cohesion fluctuates greatly. The minimum interface friction coefficient formula and test proposed in this paper can be used to evaluate the interface friction coefficient range, and the sand concrete interface shear performance under the continuous normal stress loading mode showed good consistency. The self-developed large-scale interface shearing equipment and its test data provide theoretical basis and solutions for the improvement of traditional interface shearing equipment.
Special Issue on Latest Trends in Free Electron Lasers
Authors: Principi Emiliano
Published: November 2022

In the last decade, free electron laser (FEL) sources operating from the extreme ultraviolet (EUV) up to the hard X-ray photon energy range [...]
Special Issue on Biowaste Treatment and Valorization
Authors: Rico De La Hera Carlos
Published: November 2022

Biowaste has been defined as “Biodegradable waste from gardens and parks, food and kitchen waste from homes, restaurants, collective catering services and retail establishments, and comparable waste from food processing plants” (Directive (2008)/98/EC (EC—European Commission, 2008) [...]
Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
Authors: Pérez-Carabaza Sara , Gálvez Akemi , Iglesias Andrés
Published: November 2022

Ant Colony Optimization (ACO) encompasses a family of metaheuristics inspired by the foraging behaviour of ants. Since the introduction of the first ACO algorithm, called Ant System (AS), several ACO variants have been proposed in the literature. Owing to their superior performance over other alternatives, the most popular ACO algorithms are Rank-based Ant System (ASRank), Max-Min Ant System (MMAS) and Ant Colony System (ACS). While ASRank shows a fast convergence to high-quality solutions, its performance is improved by other more widely used ACO variants such as MMAS and ACS, which are currently considered the state-of-the-art ACO algorithms for static combinatorial optimization problems. With the purpose of diversifying the search process and avoiding early convergence to a local optimal, the proposed approach extends ASRank with an originality reinforcement strategy of the top-ranked solutions and a pheromone smoothing mechanism that is triggered before the algorithm reaches stagnation. The approach is tested on several symmetric and asymmetric Traveling Salesman Problem and Sequential Ordering Problem instances from TSPLIB benchmark. Our experimental results show that the proposed method achieves fast convergence to high-quality solutions and outperforms the current state-of-the-art ACO algorithms ASRank, MMAS and ACS, for most instances of the benchmark.
Brief Report on the Advanced Use of Prolog for Data Warehouses
Authors: Pinet François
Published: November 2022

Data warehouses have demonstrated their applicability in numerous application fields such as agriculture, the environment and health. This paper proposes a general framework for defining a data warehouse and its aggregations using logic programming. The objective is to show that data managers can easily express, in Prolog, traditional data warehouse queries and combine data aggregation operations with other advanced Prolog features. It is shown that this language provides advanced features to aggregate information in an in-memory database. This paper targets data managers; it shows them the direct writing of data warehouse queries in Prolog using an easily understandable syntax. The queries are not necessarily in an optimal form from a processing point of view, but a data manager can easily use or write them.
Robust Evaluation of Reference Tilt in Digital Holography
Authors: Xu Xianfeng , Wang Hao , Sheng Hui , Luo Weilong , Wang Xinwei
Published: November 2022

A robust approach is designed to evaluate the reference tilt angle (RTA) accurately and efficiently by local Gaussian fitting (LGF) for the distribution of one frequency peak on a spatial spectrum plane (SSP). The novel method proposed can avoid enlarging the data array on either a hologram or an SSP and then alleviate the computing burden on information processing hardware. Moreover, the RTA precision can be improved by one order of the magnitude in certain ranges, which benefits not only the accurate image recovery in an off-axis digital holography (DH) display but also the thorough removal of the tilt error effect on the image quality in phase-shifting digital holography (PSDH). The error source of the frequency peak position is analyzed theoretically and the principle with detailed steps is described. Several cases of numerical simulations have been carried out to demonstrate the availability and accuracy of this robust RTA evaluation method.
Backstepping Sliding Mode Control of a Permanent Magnet Synchronous Motor Based on a Nonlinear Disturbance Observer
Authors: Duan Jiandong , Wang Shuai , Sun Li
Published: November 2022

In this paper, a backstepping sliding mode controller based on a nonlinear disturbance observer (NDO-SMC) is proposed to realize the high-performance speed control of a permanent magnet synchronous motor (PMSM). This paper compares the advantages and disadvantages of the traditional backstepping sliding mode control algorithm (SMC) and integral backstepping sliding mode control algorithm (I-SMC) in the face of mismatched disturbances. In view of the shortcomings of these two algorithms, the idea of using a disturbance observer to observe disturbance and carry out dynamic compensation is proposed, and the composite controller is designed. The overshoot and settling time is improved by 30% and 8 s, respectively, for the proposed NDO-SMC controller compared with the SMC controller. The simulation and experimental results illustrate that the designed controller not only effectively solves the torque jitter problem of SMC, but also improves the overshoot problem caused by the integral module of I-SMC. There is also a better matching degree between the theoretical derivation, the simulation results, and experimental data. It also proves that the composite control algorithm proposed in this paper provides a meaningful solution to the operation disturbance suppression problem of the permanent magnet synchronous motor.
Assessment of an Implicit Discontinuous Galerkin Solver for Incompressible Flow Problems with Variable Density
Authors: Bassi Francesco , Botti Lorenzo Alessio , Colombo Alessandro , Massa Francesco Carlo
Published: November 2022

Multi-component flow problems are typical of many technological and engineering applications. In this work, we propose an implicit high-order discontinuous Galerkin discretization of the variable density incompressible (VDI) flow model for the simulation of multi-component problems. Indeed, the peculiarity of the VDI model is that the density is treated as an advected property, which can be used to possibly track multiple (more than two) components. The interface between fluids is described by a smooth, but sharp, variation in the density field, thus not requiring any geometrical reconstruction. Godunov numerical fluxes, density positivity, mass conservation, and Gibbs-type phenomena at material interfaces are challenges that are considered during the numerical approach development. To avoid Courant-related time step restrictions, high-order single-step multi-stage implicit schemes are applied for the temporal integration. Several test cases with known analytical solutions are used to assess the current approach in terms of space, time, and mass conservation accuracy. As a challenging application, the simulation of a 2D droplet impinging on a thin liquid film is performed and shows the capabilities of the proposed DG approach when dealing with high-density (water–air) multi-component problems.
Preface to the Special Issue on the New Frontiers of Acoustic Modeling and Optimization
Authors: Arena Maurizio , Viscardi Massimo
Published: November 2022

The field of acoustics involves the study of sound propagation, both in free and closed environments, through the development of both forecasting simulations and experimental techniques [...]
Pretreatment of the Leaves of Ethiopian Cassava (Manihot esculenta Crantz) Varieties: Effect of Blanching on the Quality of Dried Cassava Leaves
Authors: Ayele Haimanot Hailegiorgis , Latif Sajid , Müller Joachim
Published: November 2022

The aim of this work was to study the effect of blanching and drying on the quality of four Ethiopian cassava varieties (Chichu, Hawassa-4, Quelle, and Kello). Cassava leaves were subjected to blanching at 100 °C in plain water, 0.2% NaHCO3, and 0.4% NaHCO3 for five minutes. Cassava leaves without blanching were considered as a control. The drying temperature was set at 60 °C with a constant air velocity of 0.14 ms−1. A high reduction in vitamin C (95.6% in Chichu, 95.0% in Hawassa-4, 94.9% in Quelle, and 94.4% in Kello) was noticed in leaves blanched in the 0.4% NaHCO3 solution. After blanching, the reduction in the ash content was higher for those blanched in plain water. The crude fiber and protein content were improved by blanching. Blanching in clear water was more effective among the different solutions in reducing the cyanide content in the leaves of Kello, Quelle, and Chichu by 51, 33, and 60%, respectively. While for Hawassa-4, the reduction was higher (47%) with the 0.2%NaHCO3 solution. After blanching, the h° values decreased, while a*, b*, and c* increased. Plain water blanching resulted in a better nutritional quality, while Hawassa-4 exhibited the best nutritional value among the four varieties.
Unified Evolutionary Algorithm Framework for Hybrid Power Converter
Authors: Ghorbanpour Samira , Seo Mingyu , Park Jeong-Ju , Kim Musu , Jin Yuwei , Han Sekyung
Published: November 2022

A significant amount of the literature is focused on converters that supply the required voltage with low input-current ripple to electronic devices. A hybrid converter that combines boost and Cuk converters was developed recently. This hybrid converter achieved a relatively low input-current ripple based on earlier strategies. This paper proposes a new model that simulates a hybrid power converter system using a unified evolutionary algorithm (EA). As part of this paper, we present an improved framework for a hybrid power converter. Moreover, a unified EA is developed to incorporate differential evolution (DE) and genetic algorithm (GA) properties in order to analyze the proposed modified hybrid power converter. This research describes a modified hybrid power converter that optimizes the zero-ripple duty cycle (DZ) through the proposed algorithm for minimizing the input-current ripple. Based on our simulation results, comparing the proposed method with the baseline algorithm reveals that the proposed approach is significantly more efficient than the baseline algorithm and achieves the minimum input-current ripple in different gain values. In addition, we observe that the proposed algorithm performs better than the DE and GA algorithms in terms of obtaining low input-current ripple results. Therefore, the proposed hybrid algorithm is becoming more efficient with hybridization.
Clinical Evaluation of the Use of Desensitizing Agents in the Management of Dentinal Hypersensitivity
Authors: Fambrini Eleonora , Miceli Marco , Pasini Marco , Giuca Maria Rita
Published: November 2022

Dentinal hypersensitivity represents one of the most widespread dental problems and symptoms in the general population. It mainly affects the age group between 18 and 65 years old, presents an incidence of 35%, and may negatively affect the oral health-related quality of life of these patients. This longitudinal study aims to measure dentinal hypersensitivity in adult patients after the use of domiciliary desensitizing devices. In this study, 52 patients were included (32 females and 20 males, mean age: 53 ± 9.4) At T0 (baseline), patients were instructed to use a desensitizing toothpaste and, at T1, 27 patients reported using sodium monofluorophosphate (1450 ppm) and arginine 8% product (product A), while 25 patients reported using a toothpaste containing n-HAp (nano-hydroxyapatite) in effective dose 2.25% and 0.15% fluoride (1500 ppm) (product B). The study was carried out through three follow up appointments, 14 days apart from one another (T1 and T2). Results showed that the air sensitivity test did not exhibit a significant difference between the time points (p > 0.05), while a significant improvement of DH was recorded for tactile, osmotic, cold thermic, acid, and omni-comprehensive tests in both groups. Both desensitizing agents were effective in reducing DH for different stimuli.
MA-Xnet: Mobile-Attention X-Network for Crack Detection
Authors: Wang Yujie , Wang Jun , Wang Chao , Wen Xin , Yan Chen , Guo Yuxiang , Cao Rui
Published: November 2022

Modern crack detection algorithms based on deep learning have unsolved issues, such as an abundance of parameters in the resulting models and lack of context information. Such issues may lower the efficiency of feature extraction and lead to unexpected task performance. Based on two semantic segmentation models, U-Net and the dual-attention network (DANet), an efficient mobile-attention X-network (MA-Xnet) is proposed for crack detection. For performance evaluation, segmentation experiments were performed on concrete crack images from an internationally recognized dataset, which were collected from various campus buildings of Middle East Technical University. The experimental results demonstrated that, compared with U-Net, the proposed method parameters were reduced by 82.33%, and improved by 11.32% and 12.37% in the key indices of the F1-Score and the mean intersection of union (mIoU), respectively, providing a reference for subsequent related lightweight crack-segmentation research.
Effect of Heat Treatment on Some Titanium Alloys Used as Biomaterials
Authors: Baltatu Madalina Simona , Chiriac-Moruzzi Cristiana , Vizureanu Petrica , Tóth László , Novák János
Published: November 2022

Titanium-based alloys are constantly improved to obtain properties suitable for their use. Improving titanium alloys is very important for performing alloys without side effects. In this paper effects of structure, microhardness, and indentation test of eight titanium alloys were investigated after aging. The heat treatment consisted of a high-temperature quenching accomplished in three steps (650 °C for 25 min, 850 °C for 20 min, and 950 °C for 20 min). The cooling process was accomplished using N2 gas, introduced in the chamber at a 9-bar pressure for 37 min. Then, followed by heating to a constant temperature tempering (550 °C) at 1.5 bar pressure and kept for 2 h and 10 min at 2 bar pressure. Optical microscopy images were obtained of Ti-Mo-Zr-Ta alloys with grain-specific aspects of titanium alloys; acicular and coarse structures are specific to β alloys. Microhardness results showed significantly influenced by the heat treatment, increased by approximately 5% for Ti15Mo7Zr15Ta1Si and Ti20Mo7Zr15Ta0.5Si, while for Ti15Mo7Zr15Ta0.5Si and Ti20Mo7Zr15Ta an approximately 9% decrease has been noted. The modulus of elasticity results obtained by the indentation method for the experimental alloys were between 36.25–66.24 GPa. The heat treatments applied to the alloys had a pronounced effect, improving both the structure of the alloys and the results of the indentation test.
ConBERT: A Concatenation of Bidirectional Transformers for Standardization of Operative Reports from Electronic Medical Records
Authors: Park Sangjee , Bong Jun-Woo , Park Inseo , Lee Hwamin , Choi Jiyoun , Park Pyoungjae , Kim Yoon , Choi Hyun-Soo , Kang Sanghee
Published: November 2022

This operative report documents the details of a surgery. Standardization of the medical terminology for the operative report written in free text is significant for performing medical research and establishing insurance systems by accurately sharing information on treatment. However, standardization of operative reports is a labor-intensive task that has a risk of induced errors. We have proposed a concatenation of bidirectional encoder representations from transformers (ConBERT) model for predicting the International Classification of Disease-9 code using the operative report and diagnosis recorded in free text to standardize the operative report automatically. We compared the pre-trained models of BERT and character BERT and created a new model by concatenating the combinations of each model. The proposed ConBERT model showed a micro AP score of 0.7672, F1 score of 0.7415, and AUC of 0.9842. In addition, we developed a web-based application to demonstrate the performance of our model and make it publicly accessible.