ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Research on Art Teaching Practice Supported by Virtual Reality (VR) Technology in the Primary Schools

Nowadays, teaching and learning methods are constantly changing with the development and popularization of information technology. Many teaching activities are exploring the integration of virtual technology. However, the specific effects of VR are challenging to verify. In this paper, "teaching in VR environment" and "traditional teaching" were designed to carry out a series of teaching comparison practices between two groups of a primary school. By analyzing the experimental data of the experimental group and the control group, the research found that it is easier to enter mental flow in virtual reality, and the introduction of virtual reality technology is positively correlated with learning engagement. What is more, compared with traditional teaching and learning methods, virtual reality technology and related software can help individuals give full play to their creativity.
An innovative computational approach based on a particle swarm strategy for adaptive phased-arrays control

In this paper a new approach to the control of phased arrays is presented and assessed. Starting from the adaptive array theory, a particle swarm strategy is used to tune the phase coefficients of the array in order to adaptively minimize/avoid the effects of interfering signals at the receiver. To show the effectiveness of the proposed approach, a selected set of numerical examples, concerned. with linear as well as planar arrays, is presented. Furthermore, to evaluate the advantages of the particle swarm optimizer (PSO)-based strategy over state-of-art methods, a comparative study is carried out by analyzing the performance of the method in terms of both the signal-to-interference-plus-noise-ratio and resulting beam pattern. The achieved results, even though preliminary, seem to confirm that the PSO-based approach satisfactorily works and it generally outperforms previously proposed/state-of-art phase-only adaptive control strategies.
Accurate Modeling of the Microwave Treatment of Works of Art

The microwave heating treatment is a useful methodology and the disinfestation of works of art can also benefit from this approach. However, even if the microwave treatment is able to eliminate the pests that could damage the works of arts, it may nevertheless present some unexpected effects such as the presence of highly heated areas (hot spots) or areas with poor radiation due to particular shapes. To overcome this issue, we developed a mathematical model allowing predicting and monitoring tasks about the heating process. The prediction model has been developed into a software solution able to predict the distribution of heating power in objects to be treated, even of complex shapes, in order to define the exposure conditions, the time necessary to the processing, the power to be transmitted in the chamber and any repair or protection to cover the most sensitive areas. It can also predict the behaviour of irradiation in the presence of other entities such as nails or pests. The data to be provided for performing a simulation are: the geometry of the object, the shape of the infesting agent and their dielectric characteristics. As a result, we obtain the distribution of heating power and a software tool able to model and predict activities for cultural heritage treatments.
EFFECTS OF ECOLOGICAL ENVIRONMENT INTEGRATED ART EDUCATION ON STUDENTS' LEARNING OUTCOME AND ENVIRONMENTAL BEHAVIOUR

Ecological environment is the basis of social development. The destruction of natural environment and ecology, on the contrary, would restrict social development. Environmental issues therefore have become the common concerns globally. To promote the knowledge and perception of environmental problems through education and to understand the correlations among living environment, resources, and humans are urgent for educators. Art is originated from life and is integrated into life, and life is the source of culture. Integrating ecological environment into art teaching allows students being close to the environment, reflecting the correlation of 'human-environment-society', potentially acquiring sense of accomplishment and potential inspiration, as well as developing sound personality. Taking a university in Guilin, Guangxi Zhuang autonomous region, as the research object, total 196 students are preceded the ecological environment integrated art education experiment in this study. The 15-week experimental teaching is preceded for 3 h per week (total 45 h). According to the results, suggestions are proposed, expecting to promote students' learning interests in art and emphasis on environmental conservation to achieve the effect of ecological art education and cultivate students' responsible environmental behaviour.
Efficient large-scale face clustering using an online Mixture of Gaussians

In recent years, the number of applications demanding real-time face clustering algorithms has increased, especially for security and surveillance purposes. However, state-of-the-art face clustering methods are offline, they need to repeat the whole clustering process every time new data arrives, and thus, they are not suitable for real-time applications. On the other hand, online clustering methods are highly dependent on the order and the size of the data, and they are less accurate than offline methods. To overcome these limitations, we present an online gaussian mixture-based clustering method (OGMC). The key idea of this method is the proposal that an identity can be represented by more than just one distribution or cluster. Using feature vectors extracted from the incoming faces, OGMC generates clusters that may be connected to others depending on their proximity and their robustness, and updates their connections every time their parameters are updated. With this approach, we reduce the dependency of the clustering process on the order and the size of the data and we are able to deal with complex data distributions. Experimental results show that OGMC outperforms state-of-the-art clustering methods on large-scale face clustering benchmarks not only in accuracy, but also in efficiency and scalability.
Does new-type urbanization curb haze pollution? A case study from China

The rapid urbanization process has led to a high concentration of population and economic activities in urban space, thus leading to severe environmental pollution. The concept of new-type urbanization has been proposed in China to combat the pollution associated with urbanization. This study analyzes the interaction effect of new-type urbanization with land, industry, and technology on haze pollution, using Chinese provincial-level panel data, and employs a STIRPAT model with interaction terms for empirical testing. The results find that new-type urbanization can significantly reduce the national haze pollution level; meanwhile, the optimization of intensive urban land use level, industrial structure, and technological innovation can interact with it to promote haze reduction, and there is regional heterogeneity. The improvement of intensive urban land use and industrial structure in the central region will significantly enhance the haze reduction effect of new-type urbanization, while the improvement of technological innovation in the west will instead weaken its haze reduction effect, and the interaction in the eastern region is not significant. This research provides a theoretical basis for better implementation of new-type urbanization construction and effective promotion of green and sustainable urban development.
Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single-and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data-taken from digital acquisition of the Ghent Altarpiece (1432)-confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.
STATE-OF-THE-ART CAPABILITIES OF COMPUTER GRAPHICS IN MUSEUM LIGHTING MODELLING

Computer graphics and technologies have currently come a long way from engineering methods of environment and space parameter calculation. In the spheres of light engineering and lighting design, it is time to switch to modelling of illumination using mainly graphic lighting effects and imagery rather than digital values. Such transfer is impossible without trying to form the methodology and recommendations for 3D modelling of illumination. The authors of this article attempt to describe the key approaches to 3D modelling of museum illumination using contemporary software. The operating lighting installation of one of the halls of the Pushkin State Museum of Fine Arts is used as an example. Based on the achieved results of the research, we tried to describe the methodology and to provide recommendations for quality design and modelling of illumination in any exhibition spaces. The described methodology may be useful both for lighting engineers, architects, designers, and for curators and museum employees. The former may use the methodology for technical approach and implementation of museum illumination while the latter may use it to find a common language with the former and to compile more accurate terms of reference for them.
Research on the development trend and application of digital media art in graphic design education

The rapid development of new media technology has a significant impact on the traditional graphic design art. It not only promotes the rapid development of graphic design field, but also accelerates the transformation and upgrading of the whole visual communication design education concept. The traditional teaching concept of graphic design cannot meet the needs of students, but the education of graphic design under digital information can improve the satisfaction of students with gorgeous visual effects. Therefore, it is of great significance to study the reform of modern graphic design education mode in the digital media environment. This paper first analyzes the development of digital media art and graphic design education, then studies the relationship between graphic design teaching and mobile digital media, and finally discusses the value of digital media art in graphic design teaching and the significance of reform.
Progression of the pluripotent epiblast depends upon the NMD factor UPF2

Nonsense-mediated RNA decay (NMD) is a highly conserved RNA turnover pathway that degrades RNAs harboring in-frame stop codons in specific contexts. Loss of NMD factors leads to embryonic lethality in organisms spanning the phylogenetic scale, but the mechanism remains unknown. Here, we report that the core NMD factor, UPF2, is required for expansion of epiblast cells within the inner cell mass of mice in vivo. We identify NMD target mRNAs in mouse blastocysts - both canonical and alternatively processed mRNAs - including those encoding cell cycle arrest and apoptosis factors, raising the possibility that NMD is essential for embryonic cell proliferation and survival. In support, the inner cell mass of Upf2-null blastocysts rapidly regresses with outgrowth and is incompetent for embryonic stem cell derivation in vitro. In addition, we uncovered concordant temporal- and lineage-specific regulation of NMD factors and mRNA targets, indicative of a shift in NMD magnitude during peri-implantation development. Together, our results reveal developmental and molecular functions of the NMD pathway in the early embryo.
Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.
Elimination by Linear Association: An Effective and Efficient Static Mirror Adaptive Random Testing

Adaptive random testing (ART) is a software testing method which combines randomness with even distribution of test cases within the input domain of a program with the aim of improving the effectiveness of random testing (RT). It was established right from the onset that, ART is considerably less efficient compared to RT due to the overhead cost involved in filtering randomly generated test cases in order to achieve the even spread objective. Again, it has been observed that over-concentration on achieving better effectiveness at the expense of efficiency will make ART advantage over RT a superficial one. Besides, the ART is close to its theoretical bound in terms of effectiveness. Various algorithms have therefore emerged that seeks to minimize the efficiency deficit incurred by the ART. One of such strategies is mirror adaptive random testing (MART). Unfortunately, the MART's performance is generally unstable due to the lack of diversity in mirror generated test cases. The culprit has been identified as the mirroring functions used in place of complex ART computations. In this paper, we present elimination (E) by linear association (E-MART) as a solution to the problem of the MART that guarantees diversity in all dimension(s) of mirror test cases. By partitioning the source domain into multiple subdomains, we systematically isolate mirror partitions which are linearly associated with the source domains. The source domain is then iteratively partitioned whiles forgetting strategy is applied to select test cases. The simulations and experimental studies conducted indicate that the EMART has a more stable performance compared to the MART and compares favorably in terms of efficiency by reducing the quadratic time of the MART to linear.
Tabdoc Approach: An Information Fusion Method to Implement Semantic Interoperability Between IoT Devices and Users

Services generated by various Internet of Things (IoT) devices are in heterogeneous contexts, hindering users from efficient consumption of those services and hence development of IoT. This paper addresses the problems in user-device interaction. A user-device interoperability framework, Tabdoc approach, is hereby established to implement semantic interoperability between IoT devices and users. In this approach, a divide-and-conquer strategy is used; a semantic document representation method for message modeling is devised to guarantee consistent message understanding among different contexts, and a semantic extraction algorithm and a semantic interpretation algorithm are proposed to implement automatic cross-context message processing. The experimental results show promising performance improvements over state-of-the-art methods.
Pseudomonas aeruginosa Pangenome: Core and Accessory Genes of a Highly Resourceful Opportunistic Pathogen

In this chapter, we leverage a novel approach to assess the seamless population structure of Pseudomonas aeruginosa, using the full repertoire of genomes sequenced to date (GenBank, April 6, 2020). In order to assess the set of core functions that represents the species as well as the differences in these core functions among the phylogroups observed in the population structure analysis, we performed pangenome analyses at the species level and at the phylogroup level. The existence of the phylogroups described in the population structure analyses was supported by their different profiles of antibiotic-resistant determinants. Finally, we utilized a presence/absence matrix of protein families from the entire species to evaluate if P. aeruginosa phylogroups can be differentiated according to their accessory genomic content. Our analysis shows that the core genome of P. aeruginosa is approximately 62% of the average gene content for the species, and it is highly enriched with pathways related to the metabolism of carbohydrates and amino acids as well as cellular processes and cell maintenance. The analysis of the accessory genome of P. aeruginosa performed in this chapter confirmed not only the existence of the three phylogroups previously described in the population structure analysis, but also of 29 genetic substructures (subgroups) within the main phylogroups. Our work illustrates the utility of populations genomics pipelines to better understand highly complex bacterial species such as P. aeruginosa.
Experimental comparison of state-of-the-art methods for digital optimum filter synthesis with arbitrary constraints and noise

We present the experimental application and comparison of two methods for the synthesis of digital filters, which represent the state-of-the-art of optimum digital processing of shaped signals with arbitrary constraints in time and frequency domain, and any kind of stationary noise power spectral density, The methods are implemented in experimental measurement setups, and optimum filters are synthesized with regard to assigned constraints (e.g., finite duration, flat top, peaking time, zero area, etc.) and by taking into account the real environmental noise or disturbance present in the system, identified from datasets of simple signal experimental acquisitions. Implementation issues are detailed and basic design rules for digital signal processors based on these techniques are derived.
Direct Analysis of Soil Composition for Source Apportionment by Laser Ablation Single-Particle Aerosol Mass Spectrometry

Soil has always been the most complex biomaterial on the planet. The rapid determination of the components in the soil and their original source is a prerequisite for soil quality, environmental, and human health risk assessments. In this study, the chemical compositions and source apportionment of surface soil samples collected from five sites in Shanghai, China, were successfully investigated using a laboratory-developed laser ablation single-particle aerosol mass spectrometry (LA-SPAMS) instrument combined with an adaptive resonance theory-based neural network algorithm (ART-2a) data-processing method for the first time. In total, more than 35,000 particles, ranging from 200 to 2000 nm, were sized, and around 15-20% of the particles were chemically analyzed by LA-SPAMS to generate both positive and negative mass spectra. The results show that there are significant differences in particle size distribution among the five samples, with peaks of various sizes and different profiles, while all five soil samples contain crustal elements, heavy metals, organic and inorganic components, and so forth. The chemical composition of each sample varied considerably, so different classes of SPAMS particle classes were identified, which were later grouped into seven general categories: EC-rich (containing elemental carbon), secondary components, organic nitrogen, crust, HM (containing heavy metal), PAH (containing polycyclic aromatic hydrocarbons), and NaK-rich particles, based on the dominant marked ions. The composition analysis and source apportionment showed that soil components in different areas have been affected by the local environment, such as local industrial emissions and automobile exhaust, which are usually characterized by varying degrees of mixing between the crust and environmental aerosols. In combination with the ART-2a method, LA-SPAMS enables rapid and direct analysis of soil samples based on real-time single-particle measurements, which will help in understanding the distribution, transport, and fate of the soil components, thus providing new insights into soil-quality assessment. Moreover, the established LA-SPAMS can also be practically applied to other daily inspection tasks, such as rocks, minerals, metals, ceramics, polymers, and other solid materials for ingredient analysis and quality evaluation.
Investigation of Asymptomatic Infection of Phellinus noxius in Herbaceous Plants

The white-rot fungus Phellinus noxius is known to cause brown root rot disease (BRRD) in woody trees and shrubs. To understand the pathogenicity of P. noxius in herbaceous plants, we investigated 23 herbaceous weed and turfgrass species in 32 BRRD-infested sites in Taiwan and/or tested them by artificial inoculation. In the field survey, P. noxius was isolated from seven symptomless herbaceous species (i.e., Typhonium blumei, Paspalum conjugatum, Paspalum distichum, Oplismenus compositus, Bidens pilosa, Digitaria ciliaris, and Zoysia matrella). Potted plant inoculation assays suggested that P. noxius is able to infect Artemisia princeps, O. compositus, and Z. matrella but not Axonopus compressus, Eremochloa ophiuroides, Ophiopogon japonicus, or Cynodon dactylon. A. princeps plants wilted within 2 weeks postinoculation, but inoculated O. compositus and Z. matrella were asymptomatic, and P. noxius could be isolated from only inoculated sites. The colonization of P. noxius in the cortex and vascular cylinder of roots was visualized by paraffin sectioning and trypan blue staining of juvenile seedlings grown on water agar. To evaluate the effect of replantation for the remediation of BRRD-infested sites, P. noxius-inoculated wood strips were buried in soil with or without vegetation. After 4 weeks, P. noxius could be detected only in the bare soil group. For the control of BRRD, the herbaceous hosts should be removed around the diseased trees/stumps and non-host turfgrasses (e.g., A. compressus, E. ophiuroides, O. japonicus, or C. dactylon) planted to accelerate the degradation of P. noxius.