Unparticle effects at the MUonE experiment

We investigate possible effects of unparticles at the MUonE experiment by considering a general model for unparticle with broken scale invariance, characterized by the scaling dimension $d$ and the energy scale $\mu$ at which the scale invariance is broken. Taking into account available relevant constraints on the couplings of the unparticles with the Standard Model (SM) leptons, we found that the MUonE experiment at the level of 10 ppm systematic accuracy is sensitive to such effects if $1<d\lesssim 1.4$ and $1\le \mu \lesssim 12$ GeV for vector unparticles. The effects of scalar unparticles are too feeble to be detected. The vector unparticles can induce a significant shift on the best-fit value of $a_\mu^\text{had}$ at the MUonE, thereby providing an opportunity to detect unparticles or to obtain a new bound on the unparticle-SM couplings in the case of no anomaly.Comment: 15 pages, 5 figures, 3 table

VeML: An End-to-End Machine Learning Lifecycle for Large-scale and High-dimensional Data

An end-to-end machine learning (ML) lifecycle consists of many iterative processes, from data preparation and ML model design to model training and then deploying the trained model for inference. When building an end-to-end lifecycle for an ML problem, many ML pipelines must be designed and executed that produce a huge number of lifecycle versions. Therefore, this paper introduces VeML, a Version management system dedicated to end-to-end ML Lifecycle. Our system tackles several crucial problems that other systems have not solved. First, we address the high cost of building an ML lifecycle, especially for large-scale and high-dimensional dataset. We solve this problem by proposing to transfer the lifecycle of similar datasets managed in our system to the new training data. We design an algorithm based on the core set to compute similarity for large-scale, high-dimensional data efficiently. Another critical issue is the model accuracy degradation by the difference between training data and testing data during the ML lifetime, which leads to lifecycle rebuild. Our system helps to detect this mismatch without getting labeled data from testing data and rebuild the ML lifecycle for a new data version. To demonstrate our contributions, we conduct experiments on real-world, large-scale datasets of driving images and spatiotemporal sensor data and show promising results

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Smart Cities, Healthy Citizens: Integrating Urban Public Health in Urban Planning

Urban planning that incorporates public health considerations is crucial for the development of smart cities that prioritize the well-being and health of their citizens. This study presents key findings on integrating urban public health into urban planning to create environments that promote physical and mental well-being. The study identifies and explores several crucial considerations for achieving this integration.The first consideration is healthy urban design, which involves designing urban spaces and infrastructure that promote physical activity, accessibility, and safety. Walkable neighborhoods, well-connected sidewalks, bike lanes, and efficient public transit systems encourage active transportation. Incorporating parks, green spaces, and recreational facilities provide opportunities for exercise and outdoor activities, while inclusive and accessible public spaces reduce pollution and noise.Air quality and pollution control emerge as another vital consideration. The study highlights the importance of implementing policies to mitigate air pollution, reduce emissions, and promote clean energy sources. Designing urban areas to minimize exposure to pollution sources, increasing green spaces and urban forests, and utilizing smart technologies for monitoring air quality are key strategies for improving air quality and mitigating the heat island effect.Ensuring accessible healthcare and services is essential for equitable public health. The research emphasizes the need to strategically locate healthcare facilities to serve both urban and underserved areas. Attention should be given to the needs of vulnerable populations, such as the elderly, low-income communities, and individuals with disabilities. The integration of telemedicine and digital health solutions can enhance access to healthcare services.Promoting active transportation and safety is crucial in urban planning. The study highlights the importance of pedestrian and cyclist safety through well-designed crosswalks, traffic calming measures, and lighting systems. Dedicated cycling infrastructure, traffic management strategies, and smart traffic systems contribute to reducing accidents and improving road safety.Noise pollution management is an often overlooked aspect of urban planning. The research emphasizes the significance of designing buildings with sound insulation and implementing zoning regulations to separate noise-sensitive areas from noise-generating activities. Green buffers and sound barriers are effective in mitigating noise impacts, while monitoring noise levels and enforcing regulations minimize excessive noise.The study also underscores the importance of integrating elements that promote mental health and social well-being into urban planning. Creating inclusive and socially connected neighborhoods, designing public spaces that encourage socialization and relaxation, and prioritizing the provision of community centers and social services all contribute to mental health and well-being.Data and technology integration play a crucial role in informing urban planning decisions and improving public health outcomes. The study highlights the value of collecting and analyzing health-related data to identify health disparities, understand the impact of the built environment on health, and guide decision-making processes. Utilizing smart technologies, such as wearable devices and health monitoring systems, promotes individual health awareness and facilitates targeted interventions.Evaluation and monitoring are essential components of successful urban planning. Continuously monitoring and evaluating the impact of urban planning decisions on public health outcomes, collecting data on health indicators, and using this information to assess intervention effectiveness and inform future planning efforts are critical for sustainable development.Integrating urban public health considerations into urban planning enables the creation of smart and healthy environments that support the well-being of citizens. This holistic approach ensures that urban development fosters economic growth, technological advancement, and the health and happiness of the people who live and work in these cities

Study on Designing and Manufacturing a Radio-Frequency Generator Used in Drying Technology and Efficiency of a Radio Frequency-Assisted Heat Pump Dryer in Drying of <em>Ganoderma lucidum</em>

A radio-frequency (RF) generator applied in drying technology was designed and manufactured for drying Ganoderma lucidum. The drying experiments were conducted by drying method of RF-assisted heat pump in order to inspect the operating parameters of the RF generator and investigate the effects of the input drying parameters on drying rate in the RF-assisted heat pump drying of Ganoderma lucidum. The results have shown that the RF generator achieved the required operating parameters as design such as RF power of 3 kW and operating frequency of 27 MHz. In RF-assisted heat pump drying, increase in RF power and drying air temperature increases the drying rate. Meanwhile, drying air velocity does not significantly affect the drying rate. At RF power of 1.95 kW, the drying time reduces by 9, 17, and 33% in comparison with RF power of 1.3, 0.65, and 0 kW (heat pump drying). At drying air temperature of 50°C, the drying time reduces by 10% and 21% in comparison with drying air temperature of 40 and 45°C. Besides, increasing RF power retains the higher content of polysaccharide in Ganoderma lucidum, and the Ganoderma lucidum samples retain the color better after drying

Social Justice Innatural Resources Explosion in Vietnam Currently Looked at the Aspect of Benefits

Social justice is always a lofty goal which humanity targets at. However, for the sake of dominance, the issue of social justice is causing a wide range of challenges. Therefore, in addition to generalizing some general theoretical issues about social justice, this article raises a number of issues of social justice of the natural resources exploitation in Vietnam and the meaning of studying this issue for Vietnam's socio-economic development now. Keywords: justice, equality, benefits, natural resources DOI: 10.7176/RHSS/9-12-03 Publication date:June 30th 201

Performance analysis for three cases of outage probability in one-way DF full-duplex relaying network with presence of direct link

In this paper, the one-way decode-and-forward (DF) full-duplex relaying network system with presence of direct link is investigated. In the analysis section, we derived the exact, lower, and upper bound for outage probability (OP) with maximal ratio combining (MRC) at the receiver. Furthermore, the system performance's analytical expressions are verified by using the Monte Carlo simulation. In addition, we investigated the effect of the main parameters on the OP of the proposed system. Finally, we can sate that the simulation curves overlap the analytical curves to convince the analysis section. This research can provide a novel recommendation for the communication network