bluuebunny/crossref_metadata_2025 huggingface dataset:
Atmosphere-Dependent Electron Relaxation of the Ag-Decorated TiO2 and the Relations with Photocatalytic Properties
Authors: Zhao Wenhao , Wen Liping , Liu Baoshun
Published: June 2023

In the current research, the atmosphere effects on the photoinduced electron relaxations of the undecorated TiO2 and Ag-decorated TiO2 (Ag/TiO2) were carefully studied by means of the in situ photoconductance and diffuse reflection measurements. In pure N2 atmosphere, the results showed that the electron relaxation mainly occurs through the transfer to the residual O2, and the Ag nanoparticles form a fast electron transfer pathway. It was seen that the apparent activation energy of the electron transfer to O2 was greatly reduced by the Ag decoration. In the methanol-containing N2 atmosphere, the electron relaxation can still occur via the transfer to residual O2 in the case of the undecorated TiO2, while the relaxation mechanism changes for the Ag/TiO2 as the relaxations are decreased with the temperatures. It is possible that the methanol molecule adsorbed on the Ag/TiO2 perimeters could act as the bridge for the recombination of the holes and the electrons stored in the Ag nanoparticles. Reducing the Ag nanoparticle size from 15 nm to 3 nm can greatly increase the electron relaxations due to the increase in Ag dispersion and Ag/TiO2 interconnection. Although the electron transfer to O2 was increased, both the photocatalytic oxidations of acetone and isopropanol showed a decrease after the Ag decoration. The results indicated that the photocatalytic oxidation was not limited by the electron transfer to O2. The increased electron transfer to O2 contributed to the recombination around the Ag/TiO2 perimeters, and the photocatalytic activities were decreased.
Clinics and Practice: Consolidating Best Practices in Periodontal Management
Authors: D’Ambrosio Francesco
Published: June 2023

Periodontitis is a multifactorial inflammatory disease [...]
Microstructure and Properties of Phosphorus Bronze/Brass Joints Produced by Resistance Projection Welding
Authors: Lai Ruilin , Zhang Weijun , Sheng Xiaofei , Ye Xianjue , Cai Yingfeng , Zhang Xiwei , Luo Temiao , Chen Pinghu , Lei Qian , Li Yunping
Published: June 2023

In this work, we fabricated lap joints between embossed projection phosphorus bronze and flat brass through resistance projection welding (RPW). The experimental results indicated that the bronze projection moves into the softer brass without being deformed during the welding process. The tensile shear loads of the joint reached a maximum value of 273.6 N at a welding current of 5.5 kA. Under this circumstance, a reaction layer, including a columnar crystal solidification layer and a diffusion layer, is formed at the interface beside the boundary of bronze. The EDS line scan shows an elemental transition diffusion layer of about 1.5 μm between the H62 brass columnar crystal and XYK-6 phosphorus bronze. The fracture occurred on the XYK-6 side, passing through the bump instead of the welding interface, resulting in intactness of the welding interface. The results revealed that resistance projection welding is an effective method for welding copper alloys, suggesting the bright prospects of this technology in welding electrical parts.
Research on Flexural and Freeze–Thaw Properties of Polypropylene-Fiber-Reinforced Pavement Concrete Containing Waterborne Epoxy
Authors: Wang Li , Zhang Bing , Zhang Hongliang , Zhao Bendong , Wang Biao , Zhao Quanman , Sun Min
Published: June 2023

In order to further enhance the reinforcing effectiveness of polypropylene (PP) fibers on pavement concrete, waterborne epoxy (WBE) was introduced in this research and its effect on the flexural properties and freeze–thaw resistance of PP-fiber-reinforced concrete was evaluated. Compressive-strength tests, flexural-strength tests, three-point bending tests, freeze–thaw cycling tests and a scanning electron microscopic observation were carried out to analyze mainly the influence of WBE on the flexural properties and freeze–thaw resistance of PP-fiber-reinforced concrete. WBE contents of 0, 5%, 10%, 15% and 20% by weight of the cement were employed. The experimental results indicated that WBE was beneficial to improving the flexural properties of PP-fiber-reinforced concrete. With increasing content of WBE, the flexural strength and the peak load showed significant increases. Although a slight degradation in the abovementioned flexural parameters was observed when the WBE content was above 15%, the deflection at the peak, the fracture energy and the fracture toughness still showed an upward trend. In addition, the freeze–thaw resistance of PP-fiber-reinforced concrete was improved remarkably with the increasing addition of WBE content, leading to smaller mass loss and higher residual flexural strength. Moreover, microstructural images revealed that with the addition of WBE, the PP fiber/concrete interfacial bonding was effectively improved, and the concrete matrix tended to be denser as well, which provided higher resistance for crack initiation and propagation. In consideration of maximally improving the flexural properties of PP-fiber-reinforced pavement concrete, and while ensuring the compressive strength and meeting the freeze–thaw requirements, it was recommended that the content of WBE in PP-fiber-reinforced concrete should be 15%.
Polypyrrole Nanosheets Prepared by Rapid In Situ Polymerization for NIR-II Photoacoustic-Guided Photothermal Tumor Therapy
Authors: Xie Yixin , Xu Ji , Jin Hui , Yi Yunfeng , Shen Yuqing , Zhang Xiuming , Liu Xinxin , Sun Yanan , Shi Wei , He Yuan , Ge Dongtao
Published: June 2023

Recently, the near-infrared-II (NIR-II, 1000–1350 nm) region has been extensively applied in deep-tissue photothermal therapy (PTT) on account of it having stronger tissue penetration and a higher maximum permissible exposure (MPE) than the near-infrared-I (NIR-I, 650–950 nm) region. In this study, we developed a rapid and convenient in situ polymerization strategy to fabricate polypyrrole nanosheets (PPy NSs) within a few minutes using manganese dioxide nanosheets (MnO2 NSs) as both the oxidant and the self-sacrificed template. The fabricated PPy NSs exhibited excellent NIR-II absorption, which conferred its high photothermal conversion efficiency (66.01%) at 1064 nm and its photoacoustic (PA) imaging capability. Both in vivo and in vitro studies have shown that that PPy NSs possess good biological safety and excellent PTT efficacy and PA imaging performances. Thus, the as-synthesized PPy NSs could effectively achieve PA imaging-guided photothermal tumor ablation under 1064 nm excitation. Our work provides a novel and promising method for the rapid preparation of PPy NSs without the addition of exogenous oxidants and subsequent template removal, which could be regarded as potential photothermal agents (PTAs) to integrate the diagnosis and treatment of cancer.
Refined Grain Enhancing Lithium-Ion Diffusion of LiFePO4 via Air Oxidation
Authors: Shen Xinjie , Qin Zijun , He Peipei , Ren Xugang , Li Yunjiao , Wu Feixiang , Cheng Yi , He Zhenjiang
Published: June 2023

LiFePO4 is a type of cathode material with good safety and long service life. However, the problems of the low Li ion diffusion rate and low electron conductivity limit the application of LiFePO4 in the field of electric vehicles. In this paper, FePO4 with different grain sizes was prepared via the air oxidation precipitation method and then sintered to prepare LiFePO4. The refined grain can shorten the diffusion distance of Li+, accelerate the diffusion of Li+, and improve the diffusion coefficient of Li+. The results show that LiFePO4 with a smaller grain size has better electrochemical performance. The discharge capacity of the first cycle is 151.3 mAh g−1 at 1 C, and the capacity retention rate is 95.04% after 230 cycles. Its rate performance is more outstanding, not only at 0.2 C, where the discharge capacity is as high as 155 mAh g−1, but also at 10 C, the capacity fade is less, and it can still reach 131 mAh g−1. The air oxidation precipitation method reduces the production cost, shortens the production process, and prepares FePO4 with small grains, which provides a reference for further improving the properties of precursors and LiFePO4.
The Effects of Individuals’ Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain
Authors: Gezha Vladislav N. , Kozitsin Ivan V.
Published: June 2023

The opinion dynamics literature argues that the way people perceive social influence depends not only on the opinions of interacting individuals, but also on the individuals’ non-opinion characteristics, such as age, education, gender, or place of residence. The current paper advances this line of research by studying longitudinal data that describe the opinion dynamics of a large sample (~30,000) of online social network users, all citizens of one city. Using these data, we systematically investigate the effects of users’ demographic (age, gender) and structural (degree centrality, the number of common friends) properties on opinion formation processes. We revealed that females are less easily influenced than males. Next, we found that individuals that are characterized by similar ages have more chances to reach a consensus. Additionally, we report that individuals who have many common peers find an agreement more often. We also demonstrated that the impacts of these effects are virtually the same, and despite being statistically significant, are far less strong than that of opinion-related features: knowing the current opinion of an individual and, what is even more important, the distance in opinions between this individual and the person that attempts to influence the individual is much more valuable. Next, after conducting a series of simulations with an agent-based model, we revealed that accounting for non-opinion characteristics may lead to not very sound but statistically significant changes in the macroscopic predictions of the populations of opinion camps, primarily among the agents with radical opinions (≈3% of all votes). In turn, predictions for the populations of neutral individuals are virtually the same. In addition, we demonstrated that the accumulative effect of non-opinion features on opinion dynamics is seriously moderated by whether the underlying social network correlates with the agents’ characteristics. After applying the procedure of random shuffling (in which the agents and their characteristics were randomly scattered over the network), the macroscopic predictions have changed by ≈9% of all votes. What is interesting is that the population of neutral agents was again not affected by this intervention.
Combining MAS-GiG Model and Related Problems to Optimization in Emergency Evacuation
Authors: Huyen Dinh Thi Hong , Ha Hoang Thi Thanh , Occello Michel
Published: June 2023

Emergency evacuation is of paramount importance in protecting human lives and property while enhancing the effectiveness and preparedness of organizations and management agencies in responding to emergencies. In this paper, we propose a method for evacuating passengers to safe places with the shortest possible evacuation time. The proposed method is based on a multi-level multi-agent MAS-GiG model combined with three problems. First, constructing a path map to select the shortest path; second, dividing the space of the experimental environment into smaller areas for efficient management, monitoring, and guiding evacuation; the third, adjusting the speed to handle collision issues and maintain distance between two or more groups of evacuees while moving. We extend our previous study by establishing groups based on the location of passengers and using a MAS-GiG model to guide evacuation. We compare the proposed method with our previous method to provide specific evaluations for the research and research in the future. We tested two methods in the departure hall, first floor, Danang International Airport, Vietnam.
Electrostimulation and Nanomanipulation of Two-Dimensional MoO3-x Layers Grown on Graphite
Authors: Nadolska Aleksandra , Kowalczyk Dorota A. , Lutsyk Iaroslav , Piskorski Michał , Krukowski Paweł , Dąbrowski Paweł , Le Ster Maxime , Kozłowski Witold , Dunal Rafał , Przybysz Przemysław , Ryś Wojciech , Toczek Klaudia , Kowalczyk Paweł J. , Rogala Maciej
Published: June 2023

Molybdenum trioxide shows many attractive properties, such as a wide electronic band gap and a high relative permittivity. Monolayers of this material are particularly important, as they offer new avenues in optoelectronic devices, e.g., to alter the properties of graphene electrodes. Nanoscale electrical characterization is essential for potential applications of monolayer molybdenum trioxide. We present a conductive atomic force microscopy study of an epitaxially grown 2D molybdenum oxide layer on a graphene-like substrate, such as highly oriented pyrolytic graphite (HOPG). Monolayers were also investigated using X-ray photoelectron spectroscopy, atomic force microscopy (semi-contact and contact mode), Kelvin probe force microscopy, and lateral force microscopy. We demonstrate mobility of the unpinned island under slight mechanical stress as well as shaping and detachment of the material with applied electrical stimulation. Non-stoichiometric MoO3-x monolayers show heterogeneous behavior in terms of electrical conductivity, which can be related to the crystalline domains and defects in the structure. Different regions show various I–V characteristics, which are correlated with their susceptibility to electrodegradation. In this work, we cover the existing gap regarding nanomanipulation and electrical nanocharacterization of the MoO3 monolayer.
Progress in the Copper-Based Diamond Composites for Thermal Conductivity Applications
Authors: Chen Kang , Leng Xuesong , Zhao Rui , Kang Yiyao , Chen Hongsheng
Published: June 2023

Copper-based diamond composites have been the focus of many investigations for higher thermal conductivity applications. However, the natural non-wetting behavior between diamond particles and copper matrix makes it difficult to fabricate copper-based diamond composites with high thermal conductivity. Thus, to promote wettability between copper and diamond particles, the copper/diamond interface must be modified by coating alloying elements on the diamond surface or by adding active alloying elements with carbon in the copper matrix. In this paper, we review the research progress on copper-based diamond composites, including theoretical models for calculating the thermal conductivity and the effect of process parameters on the thermal conductivity of copper-based diamond composites. The factors that affect interfacial thermal conductivity are emphatically analyzed in this review. Finally, the current problems of copper-based diamond composites and future research trends are recommended.
Impact of Dams on Stream Fish Diversity: A Different Result
Authors: Faucheux Nicky M. , Miranda Leandro E. , Taylor Jason M. , Farris Jerry
Published: June 2023

Impoundments can drastically change the physical and biological characteristics of fluvial systems. Changes in the physical characteristics, such as reductions in flow, increased sediment deposition, and increased surface area, often influence the system’s biological components, including plant, macroinvertebrate, and fish assemblages. In addition to having direct effects on impounded waterbodies, impoundments can also have wide-ranging effects at the watershed scale, particularly on upstream tributary streams. The purpose of this study was to assess the magnitude of these effects. We analyzed historical data from 26 streams distributed across five sub-basins in the Bluff Hills region of the Yazoo Basin, MS, USA. All five major tributary rivers in this region are impounded by large (11,240–26,143 hectares) reservoirs for flood control. We compared fish assemblages in streams located upstream and downstream of the four reservoirs using PERMANOVA, and contrary to expectations, we found no significant differences between the upstream and downstream assemblages. We explore several possible explanations for this discrepancy and suggest that stream assemblage response to impoundment may be nuanced by the regional species pool, the history of stream conditions in the watershed, and the resistance of the streams to periodic disturbances.
In Vitro Seed Germination and Seedling Development of Dracula felix (Luer) Luer—An Orchid Native to Ecuador
Authors: Quijia-Lamiña Paulina H. , Baquero Luis E. , Kane Michael E. , Zettler Lawrence W.
Published: June 2023

Effects of daily temperature fluctuations that mimic on-site environmental conditions were tested on seed germination and development in Dracula felix, a native epiphytic orchid from the neotropics. Mature seeds collected from a native population lost their viability from 60% to 37.78% and 0% after 8 and 16 weeks., respectively, under 22 ± 2 °C. Seed viability was completely lost when seeds were maintained at −10 °C in the dark. Less than 50% germination was observed in D. felix seed across all treatments. Seed germinated regardless of the light or temperature treatment. However, significant improvement in germination was observed at 17/22 °C compared to constant temperature treatments. Early seedling development stages were observed only on 1/2XMS and VW media at 17 °C or 17 °C/22 °C under a 12 h light photoperiod. Neither germination nor seedling development were improved by any fungal strain tested using standard symbiotic germination protocols. Information obtained from this study is critical to ensure the ex-situ conservation of this and other rare Dracula species under current and future climate change scenarios.
Stratigraphic and Paleoecological Significance of the Early/Middle Pleistocene Vertebrate Fauna of the Süttő 21 Site
Authors: Pazonyi Piroska , Szentesi Zoltán , Mészáros Lukács , Hír János , Gasparik Mihály
Published: June 2023

The Süttő 21 site is a fissure fill of the freshwater limestone of the Gazda quarry in Süttő. The material was collected between 2017 and 2019, and the results are summarised in this article, with a special focus on the small vertebrate fauna of the site and its stratigraphic and paleoecological significance. The fissure fill can be placed around the Early/Middle Pleistocene boundary (ca. 1.1 and 0.77 Ma). The paleoecological analysis of the herpeto- and mammal fauna of the sequence indicates the proximity of a permanent water body. The lower part of the sequence is dominated by open habitat indicator taxa indicating a cool, dry climate. Towards the upper part of the sequence, the climate remained cool, but became wetter, and the vegetation gradually changed to forest-steppe/open forest. The fauna of the Süttő 21 site can be compared with the material of sites that are of a similar age, thus revealing taxonomic and paleoecological differences between different areas of the country. While a warm, dry climate and open vegetation can be reconstructed in the Villány Hills around the Early/Middle Pleistocene boundary, the Northern Hungarian areas had a cooler, wetter climate and a slightly more closed (sparse forest, forest-steppe) vegetation during this period.
Role of Point-of-Care Diagnostics in Lower- and Middle-Income Countries and Austere Environments
Authors: Ganchi Feroz Abubaker , Hardcastle Timothy Craig
Published: June 2023

Introduction: Austere environments include the wilderness and many lower- and middle-income countries, with many of these countries facing unrest and war. The access to advanced diagnostic equipment is often unaffordable, even if available, and the equipment is often liable to break down. Methods: A short review paper examining the options available to medical professionals to undertake clinical and point-of-care diagnostic testing in resource-constrained environments that also illustrates the development of mobile advanced diagnostic equipment. The aim is to provide an overview of the spectrum and functionality of these devices beyond clinical acumen. Results: Details and examples of products covering all aspects of diagnostic testing are provided. Where relevant, reliability and cost implications are discussed. Conclusions: The review highlights the need for more cost-effective accessible and utilitarian products and devices that will bring cost-effective health care to many in lower- and middle-income or austere environments.
Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction
Authors: Venkatachala Appa Swamy Mareeswari , Periyasamy Jayalakshmi , Thangavel Muthamilselvan , Khan Surbhi B. , Almusharraf Ahlam , Santhanam Prasanna , Ramaraj Vijayan , Elsisi Mahmoud
Published: June 2023

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.
Analysis of the Effectiveness of Metaheuristic Methods on Bayesian Optimization in the Classification of Visual Field Defects
Authors: Abu Masyitah , Zahri Nik Adilah Hanin , Amir Amiza , Ismail Muhammad Izham , Yaakub Azhany , Fukumoto Fumiyo , Suzuki Yoshimi
Published: June 2023

Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model’s performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
Authors: Shi Peilun , Qiu Jianing , Abaxi Sai Mu Dalike , Wei Hao , Lo Frank P.-W. , Yuan Wu
Published: June 2023

Medical image analysis plays an important role in clinical diagnosis. In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology. Those benchmarks are representative and commonly used in model development. Our experimental results indicate that while SAM presents remarkable segmentation performance on images from the general domain, its zero-shot segmentation ability remains restricted for out-of-distribution images, e.g., medical images. In addition, SAM exhibits inconsistent zero-shot segmentation performance across different unseen medical domains. For certain structured targets, e.g., blood vessels, the zero-shot segmentation of SAM completely failed. In contrast, a simple fine-tuning of it with a small amount of data could lead to remarkable improvement of the segmentation quality, showing the great potential and feasibility of using fine-tuned SAM to achieve accurate medical image segmentation for a precision diagnostics. Our study indicates the versatility of generalist vision foundation models on medical imaging, and their great potential to achieve desired performance through fine-turning and eventually address the challenges associated with accessing large and diverse medical datasets in support of clinical diagnostics.
New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer
Authors: El Hejjioui Brahim , Lamrabet Salma , Amrani Joutei Sarah , Senhaji Nadia , Bouhafa Touria , Malhouf Moulay Abdelilah , Bennis Sanae , Bouguenouch Laila
Published: June 2023

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC.
Quantifying Information of Dynamical Biochemical Reaction Networks
Authors: Jiang Zhiyuan , Su You-Hui , Yin Hongwei
Published: June 2023

A large number of complex biochemical reaction networks are included in the gene expression, cell development, and cell differentiation of in vivo cells, among other processes. Biochemical reaction-underlying processes are the ones transmitting information from cellular internal or external signaling. However, how this information is measured remains an open question. In this paper, we apply the method of information length, based on the combination of Fisher information and information geometry, to study linear and nonlinear biochemical reaction chains, respectively. Through a lot of random simulations, we find that the amount of information does not always increase with the length of the linear reaction chain; instead, the amount of information varies significantly when this length is not very large. When the length of the linear reaction chain reaches a certain value, the amount of information hardly changes. For nonlinear reaction chains, the amount of information changes not only with the length of this chain, but also with reaction coefficients and rates, and this amount also increases with the length of the nonlinear reaction chain. Our results will help to understand the role of the biochemical reaction networks in cells.
A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT
Authors: Wang Yaping , Zhang Sheng , Cao Ruofan , Xu Di , Fan Yuqi
Published: June 2023

In complex industrial environments, the vibration signal of the rolling bearing is covered by noise, which makes fault diagnosis inaccurate. In order to overcome the effect of noise on the signal, a rolling bearing fault diagnosis method based on the WOA-VMD (Whale Optimization Algorithm-Variational Mode Decomposition) and the GAT (Graph Attention network) is proposed to deal with end effect and mode mixing issues in signal decomposition. Firstly, the WOA is used to adaptively determine the penalty factor and decomposition layers in the VMD algorithm. Meanwhile, the optimal combination is determined and input into the VMD, which is used to decompose the original signal. Then, the Pearson correlation coefficient method is used to select IMF (Intrinsic Mode Function) components that have a high correlation with the original signal, and selected IMF components are reconstructed to remove the noise in the original signal. Finally, the KNN (K-Nearest Neighbor) method is used to construct the graph structure data. The multi-headed attention mechanism is used to construct the fault diagnosis model of the GAT rolling bearing in order to classify the signal. The results show an obvious noise reduction effect in the high-frequency part of the signal after the application of the proposed method, where a large amount of noise was removed. In the diagnosis of rolling bearing faults, the accuracy of the test set diagnosis in this study was 100%, which is higher than that of the four other compared methods, and the diagnosis accuracy rate of various faults reached 100%.
Is a Whole School Approach to Inclusion Really Meeting the Needs of All Learners? Home-Schooling Parents’ Perceptions
Authors: Forlin Chris , Chambers Dianne
Published: June 2023

This paper examines issues impacting parental choice for home-schooling in one state in Australia. Data were collected from 99 parents home-schooling at least one child during the 2022 academic year. Utilizing the newly developed Parent Perceptions of Home-Schooling scale, consideration was given to proactive and reactive reasons perceived by parents as impacting their decision-making. Proactive reasons consisting of philosophical beliefs and needs of the child were cited more frequently by parents who had always home-schooled their child. Reactive reasons were mostly employed to confirm parent decisions to remove a child from school due to concerns regarding the school, the child, and the needs of parents. A range of school, child, and parental issues, and different child needs were identified as impetus for home-schooling learners with and without a diagnosed disability or an additional learning need. Discussion centers around whether current whole school approaches to inclusion are addressing the needs of all learners and parents, as perceived by this cohort. By gaining a better understanding of parental reasons for withdrawing their children with specific educational needs from regular schooling, this study is significant as it highlights a range of pertinent school-based issues to be considered to ensure effective inclusion for all.
Entrepreneurship Education with Purpose: Active Ageing for 50+ Entrepreneurs and Sustainable Development for Rural Areas
Authors: Römer-Paakkanen Tarja , Suonpää Maija
Published: June 2023

As older people have accumulated more developed networks, stronger financial positions and a greater ability to leverage resources and create more credible businesses, they are better placed to start new and more successful businesses than the younger generation. This paper presents the research that has been conducted for the ENTRUST project, which aims at designing an entrepreneurship training programme for people aged 50+ who are interested in creating new businesses to foster the sustainable development of rural areas and to provide services to tourists or other businesses that support tourism in rural areas. The results of the surveys for potential 50+ entrepreneurs (72 respondents) and experts in rural development and tourism organizations (100 respondents) show that there are perceived business opportunities in rural areas for experienced 50+ professionals. There is also a high demand for training targeted at rural tourism entrepreneurs. The interviews of experienced 50+ entrepreneurs (8) who work in cooperation with the rural community show that 50+ entrepreneurs find their work meaningful and that they want to continue working “as long as their health allows”. They greatly value the opportunity to develop the rural area and to be involved in preserving its historical and cultural heritage.
Nursing Students’ Evolving Perceptions of Online Learning: A Hierarchy of Curricula
Authors: Nwamu Henrietta , Ni Anna Ya
Published: June 2023

Many nursing programs had relatively light online learning components before the COVID-19 pandemic. The transition to fully online courses without preliminary planning represented a problematic shock to most nursing students and faculty. To understand students’ perceptions of the critical success of online learning factors, an empirical comparison was conducted of nursing (n = 126) and non-nursing students (n = 1766) with similar demographic characteristics, but in different timeframes. A two-sample t-test was conducted for each question to ascertain significant differences in student perceptions between nursing and non-nursing students. While the ranking of critical success factors was similar for nursing and other students, the perceptions of nursing students were generally far more critical of the capability of online learning due, partially due to the perception that the online medium is less well suited to nursing education, and partially to instructional challenges caused by the rapid transition and chaos caused by the pandemic. The pandemic provided an inferior initial exposure to online nursing education in programs that had not previously prepared for online instruction, but had an abnormally increased demand for more online education. Without aggressive efforts to improve online teaching in programs with similar circumstances, students’ perceptions of overall quality may not improve significantly in the long term.
A Scoping Review of School-Based Strategies for Addressing Anxiety, Intolerance of Uncertainty and Prediction in Autistic Pupils
Authors: Emerson Anne , Costley Debra
Published: June 2023

In a typical school day, young people need to do many tasks which rely on the ability to predict. Since prediction underpins cognitive and social skills, difficulties with prediction lead to multiple challenges to learning. In this review, we consider the evidence that autistic people often have difficulty making predictions about other people’s behaviour, or understanding what they are required to do, contributing to high rates of anxiety and intolerance of uncertainty. The focus of the review is to consider what we already know about effective strategies used by schools to support learning and social inclusion and to consider how we might build on these approaches. We propose a number of so far unexplored ideas with the potential to build predictive skills and which require evaluation.
Learning Strategies for Sensitive Content Detection
Authors: Povedano Álvarez Daniel , Sandoval Orozco Ana Lucila , García-Miguel Javier Portela , García Villalba Luis Javier
Published: June 2023

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.
A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance
Authors: Lee Sang-Hyuk , Choi So-Won , Lee Eul-Bum
Published: June 2023

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.
A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble
Authors: Wang Zhendong , Wang Jianlan , Li Dahai , Zhu Donglin
Published: June 2023

Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.
Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification
Authors: Park Seunghyun , Park Daejin
Published: June 2023

Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer.
Speech Emotion Recognition Based on Deep Residual Shrinkage Network
Authors: Han Tian , Zhang Zhu , Ren Mingyuan , Dong Changchun , Jiang Xiaolin , Zhuang Quansheng
Published: June 2023

Speech emotion recognition (SER) technology is significant for human–computer interaction, and this paper studies the features and modeling of SER. Mel-spectrogram is introduced and utilized as the feature of speech, and the theory and extraction process of mel-spectrogram are presented in detail. A deep residual shrinkage network with bi-directional gated recurrent unit (DRSN-BiGRU) is proposed in this paper, which is composed of convolution network, residual shrinkage network, bi-directional recurrent unit, and fully-connected network. Through the self-attention mechanism, DRSN-BiGRU can automatically ignore noisy information and improve the ability to learn effective features. Network optimization, verification experiment is carried out in three emotional datasets (CASIA, IEMOCAP, and MELD), and the accuracy of DRSN-BiGRU are 86.03%, 86.07%, and 70.57%, respectively. The results are also analyzed and compared with DCNN-LSTM, CNN-BiLSTM, and DRN-BiGRU, which verified the superior performance of DRSN-BiGRU.
Control Strategy for Improving the Voltage Regulation Ability of Low-Carbon Energy Systems with High Proportion of Renewable Energy Integration
Authors: Liu Fei , Mu Yunfei , Chen Zhe
Published: June 2023

In low-carbon energy systems, due to the high proportion of renewable energy access, the voltage regulation capacity of the system will decrease. Therefore, in the event of voltage violation, it is easy to cause large-scale renewable energy off-grid and power outages. In order to improve the voltage regulation ability of low-carbon energy system, this paper proposes a two-stage overvoltage suppression strategy for sending-end power grid. Firstly, the principle of overvoltage phenomenon in the sending end power grid of low-carbon energy system with high proportion of renewable energy access is studied, and an overvoltage control strategy composed of two stages of centralized control of rectifier station and flexible resource control of distributed power grid is proposed. Then, the PSO algorithm and consensus algorithm are used to solve the established control model. Finally, a simulation system is established based on actual operating power grid data to verify the proposed control strategy through simulation. The results indicate that the control strategy proposed in this paper can effectively suppress transient overvoltage of AC buses and improve the operational stability of the high proportion of renewable energy sending-end power grid under various operating conditions. In addition, during the daytime overvoltage regulation process, the potential of flexible regulation equipment can be fully utilized. Shortening the duration of voltage exceeding the limit and reducing the peak voltage exceeding the limit can help reduce the renewable energy waste rate of the power grid.
Enhanced Information Graph Recursive Network for Traffic Forecasting
Authors: Ma Cheng , Sun Kai , Chang Lei , Qu Zhijian
Published: June 2023

Accurate traffic forecasting is crucial for the advancement of smart cities. Although there have been many studies on traffic forecasting, the accurate forecasting of traffic volume is still a challenge. To effectively capture the spatio-temporal correlations of traffic data, a deep learning-based traffic volume forecasting model called the Enhanced Information Graph Recursive Network (EIGRN) is presented in this paper. The model consists of three main parts: a Graph Embedding Adaptive Graph Convolution Network (GE-AGCN), a Modified Gated Recursive Unit (MGRU), and a local information enhancement module. The local information enhancement module is composed of a convolutional neural network (CNN), a transposed convolutional neural network, and an attention mechanism. In the EIGRN, the GE-AGCN is used to capture the spatial correlation of the traffic network by adaptively learning the hidden information of the complex topology, the MGRU is employed to capture the temporal correlation by learning the time change of the traffic volume, and the local information enhancement module is employed to capture the global and local correlations of the traffic volume. The EIGRN was evaluated using the real datasets PEMS-BAY and PeMSD7(M) to assess its predictive performance The results indicate that the forecasting performance of the EIGRN is better than the comparison models.