bluuebunny/crossref_metadata_2025 huggingface dataset:
Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
Authors: Shi Xuefeng , Hu Min , Deng Jiawen , Ren Fuji , Shi Piao , Yang Jiaoyun
Published: March 2023

Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in Aspect Based Sentiment Analysis. Furthermore, there have been a large number of approaches being proposed to handle this relevant task. However, existing methods for ASTE suffer from powerless interactions between different sources of textual features, and they usually exert an equal impact on each type of feature, which is quite unreasonable while building contextual representation. Therefore, in this paper, we propose a novel Multi-Branch GCN (MBGCN)-based ASTE model to solve this problem. Specifically, our model first generates the enhanced semantic features via the structure-biased BERT, which takes the position of tokens into the transformation of self-attention. Then, a biaffine attention module is utilized to further obtain the specific semantic feature maps. In addition, to enhance the dependency among words in the sentence, four types of linguistic relations are defined, namely part-of-speech combination, syntactic dependency type, tree-based distance, and relative position distance of each word pair, which are further embedded as adjacent matrices. Then, the widely used Graph Convolutional Network (GCN) module is utilized to complete the work of integrating the semantic feature and linguistic feature, which is operated on four types of dependency relations repeatedly. Additionally, an effective refining strategy is employed to detect whether word pairs match or not, which is conducted after the operation of each branch GCN. At last, a shallow interaction layer is designed to achieve the final textual representation by fusing the four branch features with different weights. To validate the effectiveness of MBGCNs, extensive experiments have been conducted on four public and available datasets. Furthermore, the results demonstrate the effectiveness and robustness of MBGCNs, which obviously outperform state-of-the-art approaches.
An Efficient Cellular Automata-Based Classifier with Variance Decision Table
Authors: Wanna Pattapon , Wongthanavasu Sartra
Published: March 2023

Classification is an important task of machine learning for solving a wide range of problems in conforming patterns. In the literature, machine learning algorithms dealing with non-conforming patterns are rarely proposed. In this regard, a cellular automata-based classifier (CAC) was proposed to deal with non-conforming binary patterns. Unfortunately, its ability to cope with high-dimensional and complicated problems is limited due to its applying a traditional genetic algorithm in rule ordering in CAC. Moreover, it has no mechanism to cope with ambiguous and inconsistent decision tables. Therefore, a novel proposed algorithm, called a cellular automata-based classifier with a variance decision table (CAV), was proposed to address these limitations. Firstly, we apply a novel butterfly optimization, enhanced with a mutualism scheme (m-MBOA), to manage the rule ordering in high dimensional and complicated problems. Secondly, we provide the percent coefficient of variance in creating a variance decision table, and generate a variance coefficient to estimate the best rule matrices. Thirdly, we apply a periodic boundary condition in a cellular automata (CA) boundary scheme in lieu of a null boundary condition to improve the performance of the initialized process. Empirical experiments were carried out on well-known public datasets from the OpenML repository. The experimental results show that the proposed CAV model significantly outperformed the compared CAC model and popular classification methods.
Coal to Biomass Transition as the Path to Sustainable Energy Production: A Hypothetical Case Scenario with the Conversion of Pego Power Plant (Portugal)
Authors: Nunes Leonel J. R. , Casau Margarida , Matias João C. O. , Dias Marta Ferreira
Published: March 2023

Fossil fuels, especially coal, contribute to carbon emissions, hindering the EU’s decarbonization goal by 2050. This article proposes converting the Pego Coal Power Plant into a biomass plant as a potential solution. Biomass, a renewable resource abundant in Portugal, can transform the Pego plant into a sustainable energy source, reducing greenhouse gas emissions and combating climate change. It also reduces rural fire risks and ensures regional social and economic stability. The study explores the feasibility, limitations, and socioeconomic impacts of this scenario. This solution prevents plant closure, reduces environmental impacts, and promotes sustainability. Aligning with Portugal’s 2030 Agenda and global climate change efforts, converting the Pego plant serves as a valuable example of renewable resource utilization for climate change mitigation and regional stability. The study’s results offer insights for policymakers and stakeholders in developing sustainable energy transition strategies. Adopting such solutions can help countries achieve decarbonization goals while promoting social and economic development.
Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
Authors: Ortegón-Aguilar Jaime , Castillo-Atoche Alejandro , Becerra-Nuñez Guillermo , Estrada-López Johan Jair , Osorio-de-la-Rosa Edith , Carrasco-Alvarez Roberto , Datta Asim , Vázquez-Castillo Javier
Published: March 2023

Precision medicine is now evolving to include internet-of-wearable-things (IoWT) applications. This trend requires the development of novel systems and digital signal processing algorithms to process large amounts of data in real time. However, performing continuous measurements and complex computational algorithms in IoWT systems demands more power consumption. A novel solution to this problem consists in developing energy-aware techniques based on low-power machine learning (ML) algorithms to efficiently manage energy consumption. This paper proposes a multimodal dynamic power management strategy (DPMS) based on the ML-decision tree algorithm to implement an autonomous IoWT system. The multimodal approach analyzes the supercapacitor storage level and the incoming biosignal statistics to efficiently manage the energy of the wearable device. A photoplethysmography (PPG) sensing prototype was developed to evaluate the proposed ML-DPMS programmed in a Nordic nRF52840 processor. The experimental results demonstrate an IoWT system’s low consumption of 25.74 J, and a photovoltaic solar power generation capacity of 380 mW. The proposed ML-DPMS demonstrates a battery life extension of 3.87×, i.e., 99.72 J of energy harvested, which represents the possibility to achieve at least 2.4× more data transmissions, in comparison with the widely used uniform power management approach. In addition, when the supercapacitor’s energy is compromised, the decision tree technique achieves a good energy conservation balance consuming in the same period of time 39.6% less energy than the uniform power approach.
Dynamics Modeling and Load-Sharing Performance Optimization of Concentric Face Gear Split-Torque Transmission Systems
Authors: Gong Fei , Zhu Rupeng , Wang Qibo
Published: March 2023

The concentric face gear split-torque transmission system (CFGSTTS) has the advantages of a large reduction ratio and high power density. The CFGSTTS has considerable potential to be applied in helicopter main reducers. As such, in this study, we analyzed the load distribution characteristics of a dual input–dual output concentric face gear split-torque transmission system. A load-dependent time-varying meshing stiffness surrogate model was designed based on a feedforward neural network. The difference in the meshing stiffness between the pinion driving and face gear driving was analyzed. The coupled lumped parameter dynamic model of the bending–torsion–axis–pendulum was developed through Newton’s second law, and the influences of the time-varying meshing stiffness, backlash, comprehensive transmission error, support stiffness, and damping were considered. Finally, the impact of the support stiffness on the load-sharing coefficient was analyzed. An optimization model was constructed with the objective function of minimizing the sum of the load-sharing coefficients and was solved by the marine predator algorithm. In addition, the validity of the optimization results was verified with a finite element model. The results indicate that (1) smaller support stiffnesses of input gears benefit the corresponding load balance; (2) the support stiffnesses of the face gears have different laws of influence on the load-sharing coefficient at the input gear and idler, and the support stiffnesses of the other gears need to be comprehensively considered; (3) the larger supporting stiffnesses of the idler gears and tail gear are beneficial for decreasing the load-sharing coefficient at the input gear; and (4) the optimized load-sharing coefficients at Input Gears 1 and 2 and the idler gear decrease by 23.7%, 24.2%, and 4.6%, respectively.
A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
Authors: Wang Xinzhe , Yao Wenbin
Published: March 2023

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%.
Data Rights Confirmation Scheme Based on Auditable Ciphertext CP-ABE in the Cloud Storage Environment
Authors: Zhang Lingyun , Chen Yuling , Luo Yun , He Zhongxiang , Li Tao
Published: March 2023

Advances in information technology have made data accessible anytime and anywhere. Currently, data confirmation is a popular area of research. Many current approaches to data confirmation rely on submitting certificates of ownership, embedding digital watermarks, or using blockchain. However, none of these approaches can avoid exposing source data to third parties that are not fully trusted. To address this issue, this paper proposes a new data confirmation method based on ciphertext policy attribute-based encryption (CP-ABE), which is widely used in cloud storage environments. The unique identifier of the data owner is encrypted by Paillier encryption and embedded into the ciphertext, so that the ownership corresponding to the plaintext is converted to the ownership corresponding to the ciphertext. During the entire confirmation process, third-party organizations cannot access the source data, reducing the risk of source data leakage. Finally, the feasibility of the scheme is proved by security proof and experiment comparison.
PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
Authors: North Jacob L. , Hsu Victor L.
Published: March 2023

Of fundamental importance in biochemical and biomedical research is understanding a molecule’s biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction of protein–protein and protein–ligand interactions to drug discovery and design. While choosing the most appropriate DL architecture is vitally important to accurately model the task at hand, equally important is choosing the features used as input to represent molecular properties in these DL models. Through hypothesis testing, bioinformaticians have created thousands of engineered features for biomolecules such as proteins and their ligands. Herein we present an organizational taxonomy for biomolecular features extracted from 808 articles from across the scientific literature. This objective view of biomolecular features can reduce various forms of experimental and/or investigator bias and additionally facilitate feature selection in biomolecular analysis and design tasks. The resulting dataset contains 1360 nondeduplicated features, and a sample of these features were classified by their properties, clustered, and used to suggest new features. The complete feature dataset (the Public Repository of Engineered Features for Molecular Deep Learning, PREFMoDeL) is released for collaborative sourcing on the web.
A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
Authors: Liu Rui , Fu Rong , Xu Kang , Shi Xuanzhe , Ren Xiaoning
Published: March 2023

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems.
Delamination Effect on the Buckling Behaviour of Carbon–Epoxy Composite Typical Aeronautical Panels
Authors: Riccio Aniello , Castaldo Rossana , Palumbo Concetta , Russo Angela
Published: March 2023

The instability of structures due to compression is one of the most critical issues related to aircraft components. Especially in composite materials, which have poor out-of-plane mechanical properties, the buckling load must be assessed to ensure that the structures are within the safe limits compared to the operating loads. In the presence of delamination, the compression instability of structures becomes catastrophic, as the propagation of delamination can dramatically reduce the stiffness of the structure almost instantaneously. During the operational life of composite aircraft components, one of the most common events that can occur is low-velocity impact with foreign objects, which is one of the primary reasons for delamination. In this paper, a sensitivity analysis is presented on a typical aerospace reinforced panel with a circular delamination, representative of an impact damage. Different configurations have been analysed, varying the radius and position along the thickness of the delamination. Furthermore, some geometric parameters of the panel have been modified to evaluate how the buckling load and the propagation of interlaminar damage evolve.
Characterizing Variances of Adulterated Extra Virgin Olive Oils by UV-Vis Spectroscopy Combined with Analysis of Variance-Projected Difference Resolution (ANOVA-PDR) and Multivariate Classification
Authors: Gao Boyan , Zhang Jingyao , Lu Weiying
Published: March 2023

The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined by the ANOVA-derived methods. The ANOVA-PDR quantitatively presented the separation of the internal classes according to the three main factors. Specifically, the average ANOVA-derived PDRs of the EVOO origination and adulteration level, respectively, is 4.01 and 1.78, while the conventional PDRs of the three factors are all less than 1.5. Furthermore, the partial least-squares-discriminant analysis (PLS-DA) and the PLS regression (PLSR) modeling with the selected sub-datasets from different origins were used to verify the results. The resulting models suggested that the three main factors and their interactions were all important sources of spectral variations.
Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence
Authors: Baghbani Abolfazl , Costa Susanga , Faradonbeh Roohollah Shirani , Soltani Amin , Baghbani Hasan
Published: March 2023

This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter.
Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
Authors: Gudić Senka , Vrsalović Ladislav , Matošin Ante , Krolo Jure , Oguzie Emeka Emanuel , Nagode Aleš
Published: March 2023

The effect of temperature (from 288 to 308 K) and concentration of sulfide ions (up to 40 ppm) on the corrosion behavior of AISI 304L and AISI 316L stainless steels in seawater was studied with measurements of open-circuit potential, linear and potentiodynamic polarization, and electrochemical impedance spectroscopy. An increase in temperature and pollutant concentration negatively affects the corrosion stability of stainless steels at the open circuit (the resistance, compactness, and thickness of the surface layer decrease and the corrosion current increases), in the passive region (the passivation current increases, the depassivation potential decreases, and the passive potential region narrows), and in the transpassive potential region (the rate of metal dissolution increases). The occurrence of pitting corrosion on the surface of the samples was confirmed with optical microscopy and a non-contact 3D profilometer. A few large pits (depth 80–100 μm and width 100 μm) were formed on the surface of AISI 304L steel, while several smaller pits (depth 40–50 μm and width 50 μm) were formed on the surface of AISI 316L steel. With increasing temperature and sulfide ion concentration, the width, depth, and density of the pits increased on both steel samples. In the studied temperature and concentration range of sulfide ions, the AISI 316L steels exhibited higher corrosion resistance. Overall, the influence of sulfide ions on steel corrosion was more pronounced than the influence of temperature.
A Low Frequency Noise Source Localization and Identification Method Based on a Virtual Open Spherical Vector Microphone Array
Authors: Yang Boquan , Gao Yuan , Guo Qiang , Shi Shengguo
Published: March 2023

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA (‘p+v’ joint processing method of pressure and velocity). Firstly, a virtual open SMA with a larger aperture is obtained using a virtual array extrapolation method. In this method, the virtual SMA and the actual SMA are regarded as a dual-radius SMA, and velocity information is obtained using finite difference elements of the same direction (azimuth and elevation) array of the virtual and actual SMA. At the same time, the sound pressure at the velocity position is obtained using the virtual SMA extrapolation method and the virtual vector array element SMA, whereby both velocity and sound pressure information is obtained. Finally, the vector signal processing technology is introduced into the generalized inverse beamforming algorithm (GIB). After determining the vector transfer function of the ‘p+v’ joint processing mode, a low-frequency-noise-source localization and identification method based on the vector signal processing GIB is proposed. The simulation and experiment results show that a virtual SMA with a large aperture can be obtained using a virtual array extrapolation method, and the GIB with sound pressure and velocity joint processing has a better spatial resolution.
MRONJ Treatment Strategies: A Systematic Review and Two Case Reports
Authors: Inchingolo Angelo Michele , Malcangi Giuseppina , Ferrara Irene , Patano Assunta , Viapiano Fabio , Netti Anna , Azzollini Daniela , Ciocia Anna Maria , de Ruvo Elisabetta , Campanelli Merigrazia , Avantario Pasquale , Mancini Antonio , Inchingolo Francesco , Isacco Ciro Gargiulo , Corriero Alberto , Inchingolo Alessio Danilo , Dipalma Gianna
Published: March 2023

MRONJ is a serious drug-related side effect that is most common in people using antiresorptive and/or angiogenic medications. Therapy options for this condition include conservative treatments, surgical procedures with varied degrees of invasiveness, and adjuvant therapies. The aim of the present study is to identify the most successful and promising therapy alternatives available to clinicians. PubMed, Cochrane, Scopus, Web of Science, and Embase were searched for works on our topic published between 8 January 2006 and 8 January 2023. The search was restricted to randomized clinical trials, retrospective studies, clinical studies, and case series involving human subjects with at least five cases and no age restriction on participants. A total of 2657 was found. After the selection process, the review included 32 publications for qualitative analysis. Although conservative treatments (pharmacological, laser, and minimally invasive surgery) are effective in the early stages of MRONJs or as a supplement to traditional surgical resection therapy, most studies emphasize the importance of surgical treatment for the resolution or downstaging of advanced lesions. Fluorescence-guided surgery, PRP, PRF, CGF, piezosurgery, VEGF, hyaluronic acid, and ozone therapy all show significant potential for improving treatment outcomes.
Special Issue on Intelligent Systems Applications to Multiple Domains Based on Innovative Signal and Image Processing
Authors: Renò Vito , Maglietta Rosalia , Dimauro Giovanni
Published: March 2023

Nowadays, intelligent systems are largely applied in multiple domains (e [...]
Mechanism Analysis of Surrounding Rock Mass Failure Induced by the Multi-Cavern Effect in a Large-Scale Underground Powerhouse
Authors: Wang Meng , Shi An-Chi , Li Hai-Bo , Yan Hong-Chuan , Fan Gang , Zhou Jia-Wen
Published: March 2023

The underground powerhouse of a hydropower station, in the form of a cavern group, is generally characterized by a large scale and complicated spatial structure. During the construction phase, extensive excavation in limited underground space may cause a multi-cavern effect between adjacent caverns and thus lead to deformation and failure of the surrounding rock mass, which undoubtedly compromises cavern stability and construction safety. This paper takes the drainage gallery LPL5-1 in the Baihetan underground powerhouse (adjacent to the main powerhouse) as a case study. During the excavation of the main powerhouse, the shotcrete at the upstream arch of LPL5-1 cracked, ballooned and peeled off. After field investigation and numerical simulations, the stress evolution induced by excavation is studied and the failure mechanism is analyzed. The results indicate that the multi-cavern effect led to the surrounding rock mass failures in LPL5-1, which is related to the continuous excavation of the main powerhouse and the resultant extensive stress adjustment. During the main powerhouse excavation, a stress concentration zone was generated at the upstream arch and was intensified with the excavation progressed. The expanded stress concentration zone affected LPL5-1 and made its surrounding rock mass split, thus causing the shotcrete cracking.
Microbiota Alterations in Patients with Mucous Membrane Pemphigoid and Pemphigus Vulgaris: A Systematic Review
Authors: Santoro Rossella , Romano Antonio , Morcaldi Maria Cristina , Fiori Fausto , Di Spirito Federica
Published: March 2023

The human oral cavity comprises an extensive range of microorganisms, viruses, bacteria, fungi, archaea, and protozoa, each having a particular role and interacting with each other and with the host in sickness or health. Changes in the microbiome composition can be crucial in balancing health and disease, locally and systemically. Various microbial species in commensal relationships form the oral microbiota, and when this commensalism undergoes variations the immune system can be pushed towards the activation of inflammatory and autoimmune processes. Through a systematic review of the literature, we set out to investigate the role that the oral microbiota can play in the development and evolution of pemphigus vulgaris and mucous membrane pemphigoid. We performed our systematic review by searching “microbiome OR microbiota” AND “pemphigus OR pemphigoid” on Medline, ISI Web of science and Embase, and we included randomized controlled trials (RCTs), prospective comparison studies, retrospective cohort studies, case–control studies, and case series. These autoimmune diseases need a genetic basis to develop, but as multifactorial pathologies they are influenced by environmental factors and the dysbiosis of the oral microbiota can be a trigger. If the human microbiome plays a critical role in the pathogenesis and manifestation of oral autoimmune diseases, the next step could be new and promising therapeutic approaches such as probiotics or prebiotics.
Studies on the Synthesis and Application Properties of a Betaine Surfactant with a Benzene Ring Structure
Authors: Zuo Qi , Wang Zhihui , Li Peng , Yang Luyuan , Song Zhaozheng
Published: March 2023

Two novel betaine surfactants with distinct hydrophilic headgroups were synthesized, including carboxybetaine surfactant (DCB) and sulfobetaine surfactant (DSB). Their properties of reducing the interfacial tension (IFT) of Xinjiang crude oil/water were studied under alkaline-free conditions, as were their thermal stability, wettability, and emulsification properties. The chemical structures of the target products were characterized and analyzed by using 1H-NMR and 13C-NMR. The experimental results indicate that the introduction of a benzene ring to the hydrophobic group can improve the solubility and high-temperature resistance of the betaine surfactant. Thermogravimetric analysis showed that the degradation temperature of the synthesized betaine was above 190 °C. As the concentration of the betaine solution increased, DSB (0.0750 mmol/L) showed a lower critical micelle concentration (CMC) than DCB (0.1852 mmol/L). The wetting ability of DCB was significantly higher than that of DSB, and their contact angles on paraffin film decreased to 28.36° and 35.26°. In addition, both DCB and DSB can reduce the IFT of Xinjiang crude oil/water to ultra-low levels (10−3 mN/m) in the absence of alkali. The appropriate ion concentration has a synergistic effect on the surfactant to reduce the interfacial tension of oil/water and the effect of the three ions on the interfacial tension was as follows: Na+ < Ca2+ < Mg2+.
Design of an S/X-Band Single-Layer Shared-Aperture Array Antenna Using a Mutual Complementary Configuration
Authors: Yim En-Yeal , Jang Doyoung , Lee Chang-Hyun , Choo Hosung
Published: March 2023

This paper proposes an S/X-band single-layer shared-aperture array antenna for the multifunction radars of military ships. A unit cell of the proposed antenna consists of one S-band element and four X-band elements. The S- and X-band elements are printed on the same layer to prevent a blockage effect by upper elements in the stacked shared-aperture antenna. Herein, the S-band element has a mutual complementary configuration for the X-band elements. In addition, the unit cell of the proposed antenna is designed in a symmetrical structure, which can be flexibly extended to a full array configuration. To verify the antenna feasibility, antenna performances are measured in a full anechoic chamber. The fractional bandwidths of the S- and X-band elements are 13.6% and 13.4%, respectively. Moreover, in the 2 × 2 array configuration, the S-band array gain in the bore-sight direction varies from 5.4 dBi to 3.5 dBi when the main beam is steered from 0° to 45°. Under the same conditions, the measured X-band array gain in the bore-sight direction decreases from 13.4 dBi to 11.6 dBi.
Wideband, High-Gain, and Compact Four-Port MIMO Antenna for Future 5G Devices Operating over Ka-Band Spectrum
Authors: Hussain Sayed Aqib , Taher Fatma , Alzaidi Mohammed S. , Hussain Irshad , Ghoniem Rania M. , Sree Mohamed Fathy Abo , Lalbakhsh Ali
Published: March 2023

In this article, the compact, ultra-wideband and high-gain MIMO antenna is presented for future 5G devices operating over 28 GHz and 38 GHz. The presented antenna is designed over substrate material Roger RT/Duroid 6002 with a thickness of 1.52 mm. The suggested design has dimensions of 15 mm × 10 mm and consists of stubs with loaded rectangular patch. The various stubs are loaded to antenna to improve impedance bandwidth and obtain ultra-wideband. The resultant antenna operates over a broadband of 26.5–43.7 GHz, with a peak value of gain >8 dBi. A four-port MIMO configuration is achieved to present the proposed antenna for future high data rate devices. The MIMO antenna offers isolation <−30 dB with ECC of <0.0001. The antenna offers good results in terms of gain, radiation efficiency, envelop correlation coefficient (ECC), mean effective gain (MEG), diversity gain (DG), channel capacity loss (CCL), and isolation. The antenna hardware prototype is fabricated to validate the performance of the suggested design of the antenna achieved from software tools, and good correlation between measured and simulated results is observed. Moreover, the proposed work performance is also differentiated with literature work, which verifies that the suggested work is a potential applicant for future 5G compact devices operating over wideband and high gain.
Adapting the GACT-X Aligner to Accelerate Minimap2 in an FPGA Cloud Instance
Authors: Teng Carolina , Achjian Renan Weege , Wang Jiang Chau , Fonseca Fernando Josepetti
Published: March 2023

In genomic analysis, long reads are an emerging type of data processed by assembly algorithms to recover the complete genome sample. They are, on average, one or two orders of magnitude longer than short reads from the previous generation, which provides important advantages in information quality. However, longer sequences bring new challenges to computer processing, undermining the performance of assembly algorithms developed for short reads. This issue is amplified by the exponential growth of genetic data generation and by the slowdown of transistor technology progress, illustrated by Moore’s Law. Minimap2 is the current state-of-the-art long-read assembler and takes dozens of CPU hours to assemble a human genome with clinical standard coverage. One of its bottlenecks, the alignment stage, has not been successfully accelerated on FPGAs in the literature. GACT-X is an alignment algorithm developed for FPGA implementation, suitable for any size input sequence. In this work, GACT-X was adapted to work as the aligner of Minimap2, and these are integrated and implemented in an FPGA cloud platform. The measurements for accuracy and speed-up are presented for three different datasets in different combinations of numbers of kernels and threads. The integrated solution’s performance limitations due to data transfer are also analyzed and discussed.
Physical and Mechanical Characteristics of Variotropic Concrete during Cyclic and Continuous Sulfate Attack
Authors: Shcherban’ Evgenii , Stel’makh Sergey , Beskopylny Alexey , Mailyan Levon , Meskhi Besarion , Elshaeva Diana , Chernil’nik Andrei
Published: March 2023

The concrete of numerous buildings and structures is at increased risk due to various kinds of aggressive pollutants. In this regard, it is necessary to implement and take additional actions, among which the so-called technological methods for concrete structure property modification are promising. These methods comprise improvement and modernization of existing technologies to produce the most effective concrete building structures before the introduction of steel reinforcement. One of the effective and proven technological and design solutions is the use of centrifuged and vibrocentrifuged concrete of an annular section with a variotropic concrete structure. The aim of the work was to study the physical and mechanical properties of variotropic concretes of annular structures when exposed to sulfate attack. As a result of the cyclic impact of sulfate attack, the mass loss of vibrocentrifuged concrete was the smallest in comparison with centrifuged (17% less) and vibrated concrete (37% less). The loss of cube and prism strength of vibrocentrifuged concrete was the smallest in comparison with centrifuged (20% and 18% less, respectively) and vibrated concrete (42% and 38% less, respectively). The sulfate attack rate, as a depth of penetration and concrete destruction, was 46% less for vibrocentrifuged concrete than for centrifuged concrete and 65% less than for vibrated concrete.
Overview of the Effects of Moringa oleifera Leaf Extract on Oxidative Stress and Male Infertility: A Review
Authors: Mohlala Kutullo , Offor Ugochukwu , Monageng Elizabeth , Takalani Ndivhuho B. , Opuwari Chinyerum S.
Published: March 2023

According to a recent report by the WHO, 50–80 million people suffer with infertility. Amongst these populations, male counterparts account for 20–50% of infertility cases. The aetiology of infertility in men includes many factors such as psychological issues, lifestyle and environmental factors, hormonal disorders and chromosomal abnormalities. The pathophysiology of these aetiologies may be initiated by a local inflammatory reaction increasing reactive oxygen species (ROS) production, which can negatively affect the male reproductive system by altering the hypothalamic–pituitary–gonadotropin axis (HPG axis). Alteration of the HPG axis may affect testicular steroidogenesis, spermatogenesis, the Leydig cells and Sertoli cells, leading to poor semen quality. The prevalence of male infertility underscores the need for a thorough scientific investigation to identify treatable or reversible factors using plant adjuvants with antioxidative properties. Therefore, this review aims to provide an overview of the currently available knowledge on the aetiologies of male reproductive dysfunction, emphasising infertility risk factors, as well as elucidating the possible ways by which readily available alternatives, such as Moringa oleifera leaves, may mitigate male infertility by highlighting its role on the oxidative stress parameters, reproductive hormonal levels, testicular steroidogenesis and spermatogenesis, gene expression, weight and morphology of the testis and sperm parameters.
Influence of Thermal and Mechanical Load Cycling on Fracture Resistance of Premolars Filled with Calcium Silicate Sealer
Authors: Smran Ahlam , Abdullah Mariam , Ahmad Norasmatul Akma , AL-Maflehi Nassr , Samran Abdulaziz
Published: March 2023

The aim of this study was to evaluate the aging effect of thermomechanical cyclic load on fracture resistance of lower premolars obturated with AH Plus and BioRoot RCS root canal sealers. Forty-eight single-rooted premolars teeth were instrumented with REVO-S files up to SU/0.06 taper. The teeth were randomly assigned into 2 main groups (n = 24) according to the selected two root canal sealers (AH Plus or BioRoot RCS). All teeth were obturated using matching gutta-percha. Each main group were further divided into 3 subgroups (A, B and C) (n = 8). Group A acted as the negative control group (non-Thermomechanical aging). Whereas Group B and C were subjected first to thermal variations in a thermal cycling machine (7500 and 15,000 thermal cycles), then two different dynamic loading periods namely 3 × 105 and 6 × 105 in a masticatory simulator with a nominal load of 5 kg at 1.2 Hz which simulate approximately 1 ½ and 3 years of clinical function respectively. The roots were decoronated and fracture resistance were measured using a universal testing machine. After thermal-mechanical aging, BioRoot RCS showed significantly higher fracture resistance (p < 0.05) than AH Plus. As the thermal-mechanical cycles increased both AH Plus and BioRoot RCS exhibited a significant decrease in fracture resistance (p < 0.05). It could be concluded that thermomechanical aging had a significant impact on the outcome of the fracture resistance of AH Plus and BioRoot RCS.
The Project-Based Learning Study of Insurance Information Courses to Simulate the Application of Online Analytical Processing
Authors: Liao Yung-Cheng , Chen Mei-Su
Published: March 2023

The use of data warehouses combined with online analytical processing (OLAP) platforms has become popular in Taiwan’s insurance market. However, most schools do not have an insurance data warehouse and OLAP platform for student learning in Taiwan. The researched courses are insurance information system courses for two university classes. Based on the teacher’s experience and innovativeness, those courses are integrated using the guided project-based learning approach. Students need to build a customer micro-database, analyze customer figures through pivot analysis charts, and plan marketing campaigns. The study finds a project-based learning approach is helpful to enhance students’ OLAP analysis abilities. Secondly, the research finds that the flexibility for students to choose the topic of their project is one of the key success factors. Thirdly, the evaluation share of the student’s learning scores is important for the completion of the project. Fourthly, the courses are accompanied with satisfaction questionnaires to monitor the learning results and analyze the learning satisfaction for students among course A, course B, and the college average. Those students in the two classes both have higher satisfaction scores than the college average, but there are still differences between the classes after the t-test.
Relationship between South China Sea Summer Monsoon and Western North Pacific Tropical Cyclones Linkages with the Interaction of Indo-Pacific Pattern
Authors: Liu Shengyuan , Xu Jianjun , Tu Shifei , Zheng Meiying , Chen Zhiqiang
Published: March 2023

The South China Sea (SCS) summer monsoon (SCSSM) and Western North Pacific tropical cyclones (TCs) are both tropical systems that interact with each other on multiple scales. This study examines the differences in TCs activity characteristics between anomalous strong and weak SCSSM years, and explores the possible mechanisms behind these differences through the coupling relationship between tropical atmospheric circulation and oceanic surface conditions. Results show that the destructiveness of TCs over the Western North Pacific is stronger during weak SCSSM years than in strong years, whereas the opposite occurs for TCs over the SCS. The interaction between the tropical Indo-Pacific ocean and atmosphere plays a key role in the relationship between SCSSM intensity and TCs activity. In strong (weak) SCSSM years, the sea surface temperature anomaly (SSTA) in the tropical Pacific Ocean tends to correspond to a La Niña-like (El Niño-like) distribution, whereas the tropical Indian Ocean shows an Indian Ocean dipole-negative (positive) phase distribution. Moreover, Walker circulations in both the Indian and Pacific Oceans are coupled during these years, which creates a seesaw-like relationship in the conditions for TCs formation between the SCS and the Western Pacific Ocean. During weak SCSSM years, the formation and activity of TCs over the SCS are suppressed due to the weakened water vapor transport caused by abnormal easterly winds from the eastern Indian Ocean to the southern SCS. Meanwhile, the higher SSTA in the Western Pacific Ocean enhances the TCs activity. In strong SCSSM years, the enhanced monsoon drives a stronger monsoon trough, improving the convective environment over the SCS, whereas in contrast, the Western Pacific Ocean is covered by colder water, resulting in poorer conditions for TCs genesis.
Sources Causing Long-Term and Seasonal Changes in Combustion-Derived Particulate Matter in the Urban Air of Sapporo, Japan, from 1990 to 2002
Authors: Hayakawa Kazuichi , Sakai Shigekatsu , Akutagawa Tomoko
Published: March 2023

Fifty-one samples were collected seasonally to estimate the amounts of total suspended particulate (TSP) in Sapporo, Japan, from 1990 to 2002. The atmospheric concentration of combustion-derived particulate (Pc) was calculated based on the NP method using 1-nitropyrene and pyrene. The atmospheric TSP and Pc concentration ranges were between 31–121 µg m−3 of air (Mean ± standard deviation (SD) = 58.2 ± 20.2 µg m−3) and 31–121 µg m−3 (Mean ± SD = 8.2 ± 6.0 µg m−3), respectively. First-order linear regression equations suggested that the Pc fraction decreased faster than TSP. The highest and lowest Pc concentrations were observed in winter and summer, respectively, whereas the highest and lowest TSP concentrations were observed in spring and winter, respectively. The largest and smallest Pc/TSP concentration ratios were observed in winter (0.324) and summer (0.075), respectively. The seasonal fractions of high-temperature combustion-derived particulate (Ph) in Pc ranged from 0.56 (winter) to 0.75 (summer), suggesting that the contribution of vehicle emissions to Pc was always larger than those of coal and biomass combustion. The sources of long-term and seasonal change in Pc were elucidated by analyzing organic source markers. Atmospheric concentrations of polycyclic aromatic hydrocarbons (PAHs), nitropolycyclic aromatic hydrocarbons (NPAHs) and hopanes showed long-term and seasonal changes similar to those of Pc, although biomarkers of biomass and coal combustion, such as levoglucosan, mannosan, and galactosan were not as strongly correlated. These results suggest that the change in the Pc concentration was mainly affected by vehicle emissions rather than by coal and biomass combustion or secondary pollutant formation. The decrease in the Pc over the study period was mainly a result of the Japanese particulate matter/NOx regulations on vehicle exhaust.
Estrogenicity of Major Organic Chemicals in Cigarette Sidestream Smoke Particulate Matter
Authors: Lin Chun-Ju , Li Lih-Ann
Published: March 2023

We previously found that cigarette sidestream smoke particulate matter (CSSP) could activate estrogen receptor ERα to generate estrogen-like tumor-promoting effects. This study sought to identify the compounds responsible for CSSP estrogenicity. We first identified the component compounds using a combination of GC-MS and mass spectral matching. Based on computational estrogenicity prediction, nine potential estrogenic compounds were selected for second GC-MS identification and quantification. Their estrogenic activities at levels detected in the CSSP were verified using an estrogen-responsive reporter assay. Only catechol, a possible human carcinogen, showed significant estrogenic activity, but the activity was too low to justify CSSP estrogenicity. Even so, the mixture of these compounds reconstituted according to their contents in CSSP produced almost one third of the estrogenic activity of CSSP. These compounds acted synergistically to induce greater estrogenic effects at levels without apparent estrogenic activities. Nicotine accounted for approximately 16% of the total CSSP mass. The high abundance raises concerns about nicotine toxicity, including potentially working together with estrogenic chemicals to promote tumor growth. In summary, this study presents a tiered testing approach to identify estrogenic chemicals. Although no individual components are accountable for CSSP estrogenicity, the low-dose mixture effects of CSSP components warrant public health concerns.
Mathematical Model to Calculate Heat Transfer in Cylindrical Vessels with Temperature-Dependent Materials
Authors: Fernández-Gracía Martina , Sánchez-Pérez Juan Francisco , del Cerro Francisco , Conesa Manuel
Published: March 2023

In this article, a mathematical model capable of simulating the heat transfer of cylindrical vessels whose properties are dependent on temperature is proposed. As a case study, it compares, from an approach of their heat transfer and chemical migration characteristics as a function of the temperature reached, different materials commonly used for the manufacture of water bottles. More specifically, the materials studied were aluminium, polyethylene terephthalate, and polypropylene. The validation of the model consists of an experiment carried out in the laboratory with three water bottles of each of the materials under study, as well as simulations using the Network Simulation Method to recreate the heat transfer that occurs through the walls of the bottles. On the other hand, the nondimensionalization technique is also applied, which allows us to obtain the weight of each of the variables on the problem, as well as the existing relationship between them. Finally, an outside temperature of 30 °C to 50 °C is simulated, which is a common temperature range in southern Europe during the summer season, and an initial temperature of 20 °C for the water contained in the bottle to know the behaviour of the materials and what the final temperature of the water would be after one hour.