Miracle-dz/UniFAR-FTUs huggingface dataset:
FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation

Knowledge graphs (KGs) use resource description framework (RDF) triples to model various crisp and static resources in the world. Meanwhile, knowledge embedded into vector space can imply more meanings. Much real-world information, however, is often uncertain and dynamic. Existing KG embedding (KGE) models are insufficient to deal with uncertain dynamic knowledge in vector spaces. To overcome this drawback, this article concentrates on an embedding module for the distributed representation of uncertain dynamic knowledge and proposes a strongly adaptive fuzzy spatiotemporal RDF embedding model (FSTRE). Specifically, we first propose a fine-grained fuzzy spatiotemporal RDF model, which provides the underlying representation framework for FSTRE. Then, within the complex vector space, spatial and temporal information is embedded by projection and rotation, respectively. Fine-grained fuzziness penetrates each element of the spatiotemporal five-tuples by a modal length of the anisotropic vectors. By using geometric operations as its transformation operator, FSTRE can capture the rich interaction between crisp and static knowledge and fuzzy spatiotemporal knowledge. We performed an experimental evaluation of FSTRE based on the built fuzzy spatiotemporal KG. It was shown that our FSTRE model is superior to state-of-the-art methods and can handle complex fuzzy spatiotemporal knowledge.
Phytochemical Profiling and Antiviral Activity of Green Sustainable Nanoparticles Derived from Maesa indica (Roxb.) Sweet against Human Coronavirus 229E

Plant secondary metabolites are key components for new, safe and effective drugs. Ethanolic extract of Maesa indica Roxb. Sweet (ME) aerial parts were used for biosynthesis of sustainable green zinc oxide nanoparticles (ZnO NPs) with an average particle size 6.80 ± 1.47 nm and zeta potential −19.7 mV. Both transmission electron microscopy and X-ray diffraction assay confirmed the hexagonal shape of ZnO NPs. Phenolic ingredients in ME were identified using LC-ESI-MS/MS-MRM revealing the identification of chlorogenic acid, gallic acid, caffeic acid, rutin, coumaric acid, vanillin, naringenin, quercetin, ellagic acid, 3.4-dihydroxybenzoic acid, methyl gallate, kaempferol, ferulic acid, syringic acid, and luteolin. The major compound was chlorogenic acid at concentration of 1803.84 μg/g. The antiviral activity of ME, ZnO NPs, and combination of ME with ZnO NPs against coronavirus 229E were investigated. ZnO NPs had superior antiviral effect against coronavirus 229E than ME while their combination showed the highest anti-coronavirus 229E effect, with 50% inhibition concentration (IC50) of 5.23 ± 0.18 µg/mL and 50% cytotoxic concentration (CC50) of 138.49 ± 0.26 µg/mL while the selectivity index (SI) was 26.47. The current study highlighted the possible novel anti-coronavirus 229E activity of green ZnO NPs synthesized from Maesa indica. More studies are needed to further investigate this antiviral activity to be utilized in future biomedical and environmental applications.
Hospitality Education and Industry Linkages: A Systematic Literature Network Analysis

The purpose of the study was to identify key themes, research gaps, and influential authors, as well as to examine the evolution of research and the quality and rigor of existing literature on hospitality education and its industry linkage. For this, a systematic literature network analysis, comprising a systematic literature review using the PRISMA framework, bibliometric analysis using R programming, network analysis, and thematic analysis was performed. The findings revealed that the most important clusters of research in this area are related to curriculum development, work-integrated learning, and industry partnerships. The review explored the importance of industry-academic collaboration, challenges, curriculum development, and employability as four main themes in the area of industrial linkage in hospitality education. As indicated by researchers, the review suggests the effectiveness of different approaches to industry-academia collaboration in hospitality education, the impact of industrial linkage on the quality of hospitality education in several regions, the role of technologies in enhancing the collaboration between academia and industry in hospitality education, and the development of a sustainable curriculum that incorporates environmental, social, and economic sustainability in hospitality education as future research areas.
Uniaxial Cyclic Cell Stretching Device for Accelerating Cellular Studies

Cellular response to mechanical stimuli is a crucial factor for maintaining cell homeostasis. The interaction between the extracellular matrix and mechanical stress plays a significant role in organizing the cytoskeleton and aligning cells. Tools that apply mechanical forces to cells and tissues, as well as those capable of measuring the mechanical properties of biological cells, have greatly contributed to our understanding of fundamental mechanobiology. These tools have been extensively employed to unveil the substantial influence of mechanical cues on the development and progression of various diseases. In this report, we present an economical and high-performance uniaxial cell stretching device. This paper reports the detailed operation concept of the device, experimental design, and characterization. The device was tested with MDA-MB-231 breast cancer cells. The experimental results agree well with previously documented morphological changes resulting from stretching forces on cancer cells. Remarkably, our new device demonstrates comparable cellular changes within 30 min compared with the previous 2 h stretching duration. This third-generation device significantly improved the stretching capabilities compared with its previous counterparts, resulting in a remarkable reduction in stretching time and a substantial increase in overall efficiency. Moreover, the device design incorporates an open-source software interface, facilitating convenient parameter adjustments such as strain, stretching speed, frequency, and duration. Its versatility enables seamless integration with various optical microscopes, thereby yielding novel insights into the realm of mechanobiology.
SOCIAL CAPITAL AS KNOWLEDGE ABSORPTIVE CAPACITY AND FIRM INNOVATION

The objective of the study is to investigate relationships amongst social capital, potential absorptive capacity, realized absorptive capacity, and firm innovation. The data were collected by using a questionnaire from 89 leather product exporting firms from leather products businesses in Thailand. The hypothesized relationships among variables are examined by using ordinary least square (OLS) regression analysis. The results indicate that social capital have are significant positive impact on potential absorptive capacity and realized absorptive capacity. Potential absorptive capacity and realized absorptive capacity have are significant positive impact on firm innovation. This study might be useful to scholars and those who share an interest in the subject. Moreover, theoretical and managerial contributions, conclusion, and suggestions for future research are also interesting to be discussed.
Interdecadal variation and possible causes of summer extreme precipitation over northern Xinjiang province, northwestern China

Using daily precipitation data from 25 stations, NCEP/NCAR reanalysis data, and 12 climate indices for boreal summers from 1963 to 2017, we investigate the interdecadal variation and causes of extreme precipitation in northern Xinjiang Province (NXJ), northwestern China. The summer frequency of daily precipitation extremes (DPEs), summer precipitation from DPEs, and contribution of precipitation from DPEs to total summer precipitation have increased significantly since the early 1990s, and this increase in extreme precipitation is more intense at stations in the eastern part of NXJ. Comparing the periods 1963–1988 and 1992–2017, the anomalous circulation over the Mongolian Plateau changed from cyclonic to anti-cyclonic, and the center of the anomalous cyclonic circulation over Central Asia shifted northward from the Iranian Plateau to the Aral Sea. These changes in anomalous circulation pattern induced changes in the paths of water vapor transport and the distribution of moisture convergence, which explain well the interdecadal increase in extreme precipitation and its spatial variability. Further analysis confirms evident impacts of the Indian Ocean basin mode (IOBM) and Scandinavia teleconnection pattern on the development of anomalous circulation related to the interdecadal increase in extreme precipitation. A positive IOBM favors the northward transport of moisture from the northern Indian Ocean to Xinjiang Province and the development of anomalous cyclonic circulation over Central Asia in the middle and upper troposphere. The negative phase of the Scandinavia pattern, which has prevailed since the late 1970s, and the enhanced out of phase connection between the anomalous circulation over Scandinavia Peninsula and the West Siberia during 1992–2017 favors the development of anomalous anti-cyclonic circulation over the Mongolian Plateau.
‘Many to One’ in the Articulation to Acoustics Map

Author(s): Johnson, Keith; Bakst, Sarah | Abstract: The “many to one” problem arises when trying to map inversely from acoustic patterns tovocal tract configurations. In one famous demonstration, Atal et al. (1978) searched through theacoustic outputs of a synthetic vocal tract for vowels that matched each other exactly on the frequenciesof the first three resonances (F1-3) and found that for each vowel tested [i], [a] and [u] there wereseveral vocal tract configurations that gave the same formant frequencies. This result has been used toshow that one (whether speech technologist or listener) cannot inversely map from acoustics to derive aunique possible vocal tract shape. We synthesized vowels using the formant frequencies reported byAtal et al. and show that listeners can detect differences between them even though the vowels areidentical in the first three formants. Our conclusion is that listeners may not be as troubled by a manyto-oneproblem as has been assumed before.
LQ Secure Control for Cyber-Physical Systems Against Sparse Sensor and Actuator Attacks

This paper investigates the problem of secure control for cyber-physical systems (CPSs) under sensor and actuator attacks. The attacks are assumed to be sparse but arbitrary. The strong detectability under the sparse attacks is characterized by computing the rank of the so-called structural matrix pencil. An adaptive hybrid control strategy is proposed by automatically excluding the tampered measurement and input data. First, a hybrid system consisting of all possible combinations of models is implemented. Then, combining classical linear quadratic control theory, an adaptive switching logic is designed to online locate the proper system mode by observing the optimal performance index. As a result, the closed-loop system is ensured to be asymptotically stable in the presence of the attacks, while the performance loss can be mitigated. Also, the performance degradation under attacks is also analyzed quantificationally based on the quadratic cost function. Simulation results on an aircraft system are given to substantiate the proposed method.
A Novel Machine Learning Approach for Intelligent Spectrum Management in Cognitive Radio Networks

This letter proposes a novel hybrid spectrum management scheme combining transfer actor-critic learning (TACT) and Q-learning algorithms to improve the cognitive radio access network’s spectrum efficiency. The TACT algorithm improves its mean opinion score over time, while the Q-learning achieves faster convergence during spectral management. Thus, this letter seeks to alleviate resource constraints by better exploiting unused communication channels. Computer simulations are carried out compared to reinforcement learning and conventional TACT algorithms. The results evidence the efficiency of our approach for intelligent spectrum management in cognitive radio networks.
Whole Transcriptome Analysis of Substantia Nigra in Mice with MPTP-Induced Parkinsonism Bearing Defective Glucocerebrosidase Activity

Mutations in the GBA1 gene represent the major genetic risk factor for Parkinson’s disease (PD). The lysosomal enzyme beta-glucocerebrosidase (GCase) encoded by the GBA1 gene participates in both the endolysosomal pathway and the immune response. Disruption of these mechanisms is involved in PD pathogenesis. However, molecular mechanisms of PD associated with GBA1 mutations (GBA-PD) are unknown today in particular due to the partial penetrance of GBA1 variants in PD. The modifiers of GBA1 penetrance have not been elucidated. We characterized the transcriptomic profiles of cells from the substantia nigra (SN) of mice with co-injection with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and selective inhibitor of GCase activity (conduritol-β-epoxide, (CBE)) to mimic PD bearing GCase dysfunction (MPTP+CBE), mice treated with MPTP, mice treated with CBE and control mice treated with injection of sodium chloride (NaCl) (vehicle). Differential expression analysis, pathway enrichment analysis, and outlier detection were performed. Functional clustering of differentially represented transcripts revealed more processes associated with the functioning of neurogenesis, inflammation, apoptosis and autophagy in MPTP+CBE and MPTP mice than in vehicle mice, with a more pronounced alteration of autophagy processes in MPTP+CBE mice than in MPTP mice. The PI3K-Akt-mTOR signaling pathway may be considered a potential target for therapy in PD with GCase dysfunction.
Road crack detection based on improved U-Net

The construction of road infrastructure equipment is being vigorously developed throughout the country and an improved UNet network model is After a long period of use, the pavement problems become more and more obvious, and the emergence of neural networks has substantially reduced labour costs and improved detection efficiency. After a long period of use, the pavement problems become more and more obvious, and the emergence of neural networks has substantially reduced labour costs and improved detection efficiency. One of them is UNet, an end-to-end deep learning network with excellent recognition capability for school object detection, so it can be applied to road crack defect detection. The paper improves the coding part on the traditional UNet network structure into a residual network ResNet50 and removes the connection layer and average pooling layer, using image greyscaling The paper improves the coding part on the traditional UNet network structure into a residual network ResNet50 and removes the connection layer and average pooling layer, using image greyscaling, image denoising and image enhancement to improve the recognition accuracy. The experimental results show that the proposed algorithm improves the accuracy by 2.1% and the IOU by 5.7% compared with the original UNet network.
Analysis of the resonance effect on the orbital motion of a spacecraft around Mercury

The double-averaged method should be used with caution in some situations where the averaging is applied at different timescales. In this work, a study is presented considering this observation for orbits around Mercury. When the average anomaly of the Sun is eliminated, the idea is that all effects whose periods below 88 days are neglected. As the rotation of Mercury is about 58.6 days, this means that the perturbation due to the C22 term must also be neglected. However, since the C22 term is important and should be taken into account, then terms longer than 58.6 days should also be preserved. In other words, keeping the C22 term with a period of 58.6 days means that the solar terms with the longest period (88 days) must also be maintained. Therefore, in this preliminary work, the second average over the mean anomaly of the Sun (or Mercury) is not applied. An analysis of the orbital motion of artificial satellites around Mercury is presented taking into account its non-sphericity (J2 , C22 ) and the perturbation of the third body. The single averaged method is applied to eliminate only the mean anomaly of the spacecraft. In this work, is presented an approach considering the Kozai resonance.
Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions

An individualized dynamic risk prediction model that incorporates all available information collected over the follow‐up can be used to choose an optimal treatment strategy in realtime, although existing methods have not been designed to handle competing risks. In this study, we developed a landmark proportional subdistribution hazard (PSH) model and a comprehensive supermodel for dynamic risk prediction with competing risks. Simulations showed that our proposed models perform satisfactorily (assessed by the time‐dependent relative difference, Brier score and area under the receiver operating characteristics curve) under PSH or non‐PSH settings. The models were used to predict the probabilities of developing a distant metastasis among breast cancer patients where death was treated as a competing risk. Prediction can be estimated by using standard statistical packages.
Recent Progress in Printed Photonic Devices: A Brief Review of Materials, Devices, and Applications

Printing electronics incorporates several significant technologies, such as semiconductor devices produced by various printing techniques on flexible substrates. With the growing interest in printed electronic devices, new technologies have been developed to make novel devices with inexpensive and large-area printing techniques. This review article focuses on the most recent developments in printed photonic devices. Photonics and optoelectronic systems may now be built utilizing materials with specific optical properties and 3D designs achieved through additive printing. Optical and architected materials that can be printed in their entirety are among the most promising future research topics, as are platforms for multi-material processing and printing technologies that can print enormous volumes at a high resolution while also maintaining a high throughput. Significant advances in innovative printable materials create new opportunities for functional devices to act efficiently, such as wearable sensors, integrated optoelectronics, and consumer electronics. This article provides an overview of printable materials, printing methods, and the uses of printed electronic devices.
Geographical Map Registration and Fusion of Lidar-Aerial Orthoimagery in GIS

Centimeter level globally accurate and consistent maps for autonomous vehicles navigation has long been achieved by on board real-time kinematic(RTK)-GPS in open areas. However when dealing with urban environments, GPS frequently experiences multipath and blockage in urban canyon, under bridges, inside tunnels and in underground environments. In this paper we present strategies to efficiently register local maps in geographical coordinate systems through the tactical integration of GPS and information extracted from precisely geo-referenced high resolution aerial orthogonal imagery. Dense lidar point clouds obtained from moving vehicles are projected down to horizontal plane, accurately registered and overlaid on aerial orthoimagery. Sparse, robust and long-term pole-like landmarks are used as anchor points to link lidar and aerial image sensing, and constrain the spatial uncertainties of remaining lidar points that cannot be directly measured and identified. We achieved 15-75cm absolute average global accuracy using precisely geo-referenced aerial imagery as ground truth. This is valuable in enabling the fusion of ground vehicle on-board sensor features with features extracted from aerial images such as traffic and lane markings. It is also useful for cooperative sensing to have an unbiased and accurate global reference. Experimental results are presented demonstrating the accuracy and consistency of the maps when operating in large urban areas.
Tangible interventions for office work well-being: approaches, classification, and design considerations

O ﬃ ce well-being aims to explore and support a healthy, balanced and active work style in o ﬃ ce environments. Recent work on tangible user interfaces has started to explore the role of physical, tangible interfaces as active interventions to explore how to tackle problems such as inactive work and lifestyles, and increasingly sedentary behaviours. We identify a fragmented research landscape on tangible O ﬃ ce well-being interventions, missing the relationship between interventions, data, design strategies, and outcomes, and behaviour change techniques. Based on the analysis of 40 papers, we identify 7 classi ﬁ cations in tangible O ﬃ ce well-being interventions and analyse the intervention based on their role and foundation in behaviour change. Based on the analysis, we present design considerations for the development of future tangible O ﬃ ce well-being design interventions and present an overview of the current ﬁ eld and future research into tangible O ﬃ ce well-being interventions to design for a healthier and active o ﬃ ce environment.
Motor rolling bearing fault diagnosis based on MVMD energy entropy and GWO-SVM

For motor rolling bearing fault diagnosis, vibration signal analysis is a common method to extract sensitive fault characteristics. In this paper, a newly signal processing method, multivariate variational mode decomposition (MVMD), is proposed to extract features from motor rolling bearings. The MVMD was carried out on the motor rolling bearings state signals of different categories, and the prior parameter K value which had a great influence on the decomposition effect was analyzed. Each component obtained by decomposition was measured in the form of energy entropy (EE), and the measured feature information was classified and identified by support vector machine (SVM) classifier. Meanwhile, the grey wolf optimization (GWO) was used to optimize the parameters of the classifier network to further improve the recognition accuracy. Through the simulation results, it is found that the scheme can achieve 100 % effect on the diagnosis rate of normal working condition, outer ring fault, inner ring fault and rolling element fault under the condition of different load and speed of the motor rolling bearing.
Associations between Nutrigenomic Effects and Incidences of Microbial Resistance against Novel Antibiotics

Nutrigenomics is the study of the impact of diets or nutrients on gene expression and phenotypes using high-throughput technologies such as transcriptomics, proteomics, metabolomics, etc. The bioactive components of diets and nutrients, as an environmental factor, transmit information through altered gene expression and hence the overall function and traits of the organism. Dietary components and nutrients not only serve as a source of energy but also, through their interactions with genes, regulate gut microbiome composition, the production of metabolites, various biological processes, and finally, health and disease. Antimicrobial resistance in pathogenic and probiotic microorganisms has emerged as a major public health concern due to the presence of antimicrobial resistance genes in various food products. Recent evidence suggests a correlation between the regulation of genes and two-component and other signaling systems that drive antibiotic resistance in response to diets and nutrients. Therefore, diets and nutrients may be alternatively used to overcome antibiotic resistance against novel antibiotics. However, little progress has been made in this direction. In this review, we discuss the possible implementations of nutrigenomics in antibiotic resistance against novel antibiotics.