Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve Contextual IoT service discovery with semantic models

The Internet of things (IoT) is an ecosystem of smart connected devices that exchange data over a communication network. By integrating these devices into different vertical applications, the IoT has the potential to have a major impact on both the economy and society. However, the plethora of heterogeneous devices with varying ways of describing the information raise interoperability issues. In this context, the development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring interoperability among different devices and applications. In previous work, we focused on surpassing the IoT semantics barriers while exploring novel networking approaches. To this end, we proposed a service discovery mechanism, realised on top of Named Data Networking (NDN), that relied on a semantic matching mechanism for achieving a flexible discovery process. Since the initial work, several improvements were made to the semantic similarity model at the basis of the semantic matching algorithm. This work replicates the scenario proposed on the former contribution and assesses the impact of the improved semantic model. Results show that while the previous semantic model achieves a mean Average Precision of 0.29, the best performing current solution achieves 0.68.
Quantum noise limit for force sensitivity of linear detectors

We prove that the force sensitivity of the conventional optomechanical detector associated with the optical quadrature measurement of the output beam is lower bounded by the so-called ultimate quantum limit (UQL), i.e., the absolute value of the imaginary part of the inverse mechanical susceptibility. Through the linear response theory, we find that the force sensitivity of any linear detector is lower bounded by a generalized UQL, which might beat the usual UQL by properly tailoring the detector-oscillator interaction. We believe that our results open a new direction for improving the performance of high-sensitivity detection schemes.
Gamma-ray novae as probes of relativistic particle acceleration at non-relativistic shocks

The Fermi LAT discovery that classical novae produce >100 MeV gamma-rays establishes that shocks and relativistic particle acceleration are key features of these events. These shocks are likely to be radiative due to the high densities of the nova ejecta at early times coincident with the gamma-ray emission. Thermal X-rays radiated behind the shock are absorbed by neutral gas and reprocessed into optical emission, similar to Type IIn (interacting) supernovae. Gamma-rays are produced by collisions between relativistic protons with the nova ejecta (hadronic scenario) or Inverse Compton/bremsstrahlung emission from relativistic electrons (leptonic scenario), where in both scenarios the efficiency for converting relativistic particle energy into LAT gamma-rays is at most a few tens of per cent. The ratio of gamma-ray and optical luminosities, L_gam/L_opt, thus sets a lower limit on the fraction of the shock power used to accelerate relativistic particles, e_nth. The measured values of L_gam/L_opt for two classical novae, V1324 Sco and V339 Del, constrains e_nth > 1e-2 and > 1e-3, respectively. Inverse Compton models for the gamma-ray emission are disfavored given the low electron acceleration efficiency, e_nth ~ 1e-4-1e-3, inferred from observations of Galactic cosmic rays and particle-in-cell (PIC) numerical simulations. A fraction > 100(0.01/e_nth) and > 10(0.01/e_nth) per cent of the optical luminosity is powered by shocks in V1324 Sco and V339 Del, respectively. Such high fractions challenge standard models that instead attribute all nova optical emission to the direct outwards transport of thermal energy released near the white dwarf surface.
Unequal age-based household carbon footprint in China

ABSTRACT Controlling household consumption is an essential means to achieve carbon neutrality in China, and population ageing has an important impact on its structure. Since older people exhibit different consumption patterns than younger people, an increase in the proportion of aged people affects overall consumption patterns. This paper adopts an input–output model to reflect the heterogeneity in the consumption structure and household carbon footprint of different age groups, followed by a simulation of the future household carbon footprint. The results find that in China, the total household carbon footprint shows an inverted U-shape with age, with the lowest total carbon footprint coming from aged households (age of household head 65 and above) and the highest total carbon footprint from middle-aged households (age of household head 45–54). The average household carbon footprint decreases with age, with aged households remaining the lowest. Aged households, however, have the highest share of the direct carbon footprint. Interestingly, urban households of all ages have a higher carbon footprint than rural households, with the largest difference being among aged households. The projection results show that based on demographic changes, although the average household carbon footprint of elderly households in China is low, as the number of elderly households increases, the total carbon footprint of elderly households will be sizable and need to be taken seriously. Key policy insights The total carbon footprint of elderly households will become more significant and sizable as the number of elderly households increases. Urban life is more carbon-intensive, and China’s urbanization is leading to an increase in the carbon footprint. Aged households require more attention in future climate policies. Targeted, consumption-based climate policies are necessary for carbon reduction in China and will have relevance in other countries with similar consumption structure and demographic trends, e.g. with ageing or rapidly urbanizing populations.
ALMA observations of the variable 12 CO/ 13 CO ratio around the asymptotic giant branch star R Sculptoris ,

The 12CO/13CO ratio is often used as a measure of the 12C/13C ratio in the circumstellar environment, carrying important information about the stellar nucleosynthesis. External processes can change the 12CO and 13CO abundances, and spatially resolved studies of the 12CO/13CO ratio are needed to quantify the effect of these processes on the globally determined values. Additionally, such studies provide important information on the conditions in the circumstellar environment. The detached-shell source R Scl, displaying CO emission from recent mass loss, in a binary-induced spiral structure as well as in a clumpy shell produced during a thermal pulse, provides a unique laboratory for studying the differences in CO isotope abundances throughout its recent evolution. We observed both the 12CO(J = 3 → 2) and the 13CO(J = 3 → 2) line using ALMA. We find significant variations in the 12CO/13CO intensity ratios and consequently in the abundance ratios. The average CO isotope abundance ratio is at least a factor three lower in the shell (~19) than that in the present-day (≤300 years) mass loss (>60). Additionally, variations in the ratio of more than an order of magnitude are found in the shell itself. We attribute these variations to the competition between selective dissociation and isotope fractionation in the shell, of which large parts cannot be warmer than ~35 K. However, we also find that the 12CO/13CO ratio in the present-day mass loss is significantly higher than the 12C/13C ratio determined in the stellar photosphere from molecular tracers (~19). The origin of this discrepancy is still unclear, but we speculate that it is due to an embedded source of UV-radiation that is primarily photo-dissociating 13CO. This radiation source could be the hitherto hidden companion. Alternatively, the UV-radiation could originate from an active chromosphere of R Scl itself. Our results indicate that caution should be taken when directly relating the 12CO/13CO intensity and 12C/13C abundance ratios for specific asymptotic giant branch stars, in particular binaries or stars that display signs of chromospheric stellar activity. © ESO, 2013.
Explainable and Personalized Medical Cost Prediction Based on Multitask Learning over Mobile Devices

Currently, the forecasting of healthcare costs is of significant importance for the finance management of both government and individual citizens. However, the existence of dramatic individual diversity in health status, as well as the extensive complexity of the factors influencing the cost, has made the prediction a challenging task. Thanks to the unprecedented adoption of mobile devices, regular individuals may contribute diverse dimensions of data for the medical cost prediction. Hospitals and healthcare service providers are all setting up their own mobile services and collect user data for analysis. Previous methods usually employed traditional machine learning or simple neural network methods, which are difficult to be applied to the nonlinear medical cost and diverse dimensions of data. Therefore, this paper proposes a multitask learning-based framework for interpretable medical cost interval prediction to address these issues. The framework proposed in this paper first predicts subcost intervals by applying the multidimensional data collected from mobile ends and following the multitask learning paradigm. The total cost interval is then predicted based on this prediction. Simultaneously, the framework derives a decision tree from the parameters of the multitask learning network and calculates the importance of each feature in predicting the cost intervals. This paper demonstrates the method's effectiveness using real-world data experiments.
Synthesis, Electrochemical and Fluorescence Properties of the First Fluorescent Member of the Ferrocifen Family and of Its Oxidized Derivatives

The first fluorescent ferrociphenol derivative (P797) has been synthesized via McMurry cross-coupling followed by copper-catalyzed [3 + 2] azide-alkyne cycloaddition of the fluorescent group coumarin. Cyclic voltammograms of P797 exhibit either a monoelectronic oxidation wave ascribed to the ferrocene Fe(II) → Fe(III) conversion or a three-electron oxidation process in the presence of a base, leading to a Fe(III) quinone methide adduct. This general sequence is consistent with those previously described for non-fluorescent ferrociphenols. Furthermore, the fluorescence properties of P797 and its oxidized intermediates appear to strongly depend on the redox state of the ferrocene group. Indeed, electrochemical generation of Fe(III) (ferrocenium) states markedly increases the fluorescence emission intensity. In contrast, the emission of the Fe(II) (ferrocene) states is partially quenched by photoinduced electron transfer (PET) from the Fe(II) donor to the coumarin acceptor and by concentration-dependent self-quenching. Owing to its switchable fluorescence properties, complex P797 could represent an innovative and useful tool to study the biodistribution and the redox state of ferrocifens in cancer cells.
Unlocking the full potential of wave-matter nonlinear coupling in the epsilon-near-zero regime

In recent years, unconventional metamaterial properties have triggered a revolution of electromagnetic research which has unveiled novel scenarios of wave-matter interaction. A very small dielectric permittivity is a leading example of such unusual features, since it produces an exotic static-like regime where the electromagnetic field is spatially slowly-varying over a physically large region. The so-called epsilon-near-zero metamaterials thus offer an ideal platform where to manipulate the inner details of the "stretched" field. Here we theoretically prove that a standard nonlinearity is able to operate such a manipulation to the point that even a thin slab produces a dramatic nonlinear pulse transformation, if the dielectric permittivity is very small within the field bandwidth. The predicted non-resonant releasing of full nonlinear coupling produced by the epsilon-near-zero condition does not resort to any field enhancement mechanisms and opens novel routes to exploiting matter nonlinearity for steering the radiation by means of ultra-compact structures.
Naturalness and the Status of Supersymmetry

For decades, the unnaturalness of the weak scale has been the dominant problem motivating new particle physics, and weak-scale supersymmetry has been the dominant proposed solution. This paradigm is now being challenged by a wealth of experimental data. In this review, we begin by recalling the theoretical motivations for weak-scale supersymmetry, including the gauge hierarchy problem, grand unification, and WIMP (weakly interacting massive particle) dark matter, and their implications for superpartner masses. These are set against the leading constraints on supersymmetry from collider searches, the Higgs boson mass, and low-energy constraints on flavor and CP violation. We then critically examine attempts to quantify naturalness in supersymmetry, stressing the many subjective choices that influence the results both quantitatively and qualitatively. Finally, we survey various proposals for natural supersymmetric models, including effective supersymmetry, focus point supersymmetry, compressed supersymmetry.
Forest school

This review aims to summarise existing research on the forest-based pedagogical approach known as forest school, as developed in the UK. Modelled on the nature kindergartens of northern Europe, forest school is popular in the UK and is now being practiced or explored in other countries around the world. Drawing on papers specifically researching forest school, identified through the Scopus database, it identifies and reviews key themes emerging from the literature: research on its development, relationship to classroom teaching and the national curriculum, impact on children’s development, and their relationship to the environment and environmental behaviour. It identifies the challenges and tensions emerging in the practice of forest school, between the performative agenda of schools and the alternative learning approaches embedded in forest school praxis. It summarises the attempts by several authors to develop theoretical models of forest school. It discusses the transferability of this forest education practice to new cultures, environments and educational systems. Finally, it concludes by identifying challenges for further research.
Effect of Plant Preservative MixtureTM on Endophytic Bacteria Eradication from In Vitro-Grown Apple Shoots

Endophytic contaminants are a common problem for the in vitro propagation of woody plants and have significant economic repercussions for the conservation of plant genetic resources and commercial micropropagation. In this study, first, the microbial contamination that appeared around the base of in vitro-grown apple shoots was identified as Bacillus megaterium. Then, plant preservative mixture (PPMTM) was used as a bactericidal agent in plant tissue culture. Its efficacy for eradicating endophytic B. megaterium in in vitro cultures of apple was tested. In vitro-contaminated shoots were grown in tissue culture medium supplemented with 0.2% v/v PPMTM for 12 weeks and then transferred to medium without any PPMTM and cultured for 24 weeks. This study showed that PPMTM is an effective agent for controlling the growth of B. megaterium. Our results highlight the species-specific response of apple shoots to PPMTM. PPMTM was effective in controlling endogenous microbial contaminations from apple varieties ‘Golden Delicious’, ‘Landsberger Renette’, ‘Suislepper’, and ‘Aport krovavo-krasnyi’; meanwhile, in ‘KG 7’ and ‘Gold Rush’, all the plants grown in the absence of PPMTM were still bacterially contaminated, even though they were pre-treated for 12 weeks in PPMTM-supplemented medium. These results therefore suggest the essentiality of further testing of extended incubation of PPMTM in these cultivars that had outbreaks of bacterial contamination.
Investigation on the Temperature Distribution Uniformity of an Extrusion-Based 3D Print Head and Its Temperature Control Strategy

Extrusion-based 3D printing for thermoplastic polymers manifests potential for the fabrication of biocompatible and biodegradable scaffolds. However, the uncontrollable shape of printed filaments usually negatively impacts on the printing processes. Non-uniform temperature in the print head is a primary cause of inaccuracy in the diameter of filaments formed during the process of extruding thermoplastic polymers. Therefore, the temperature distribution inside the print head must be controlled accurately. This study developed a novel print head configuration with two groups of controllable heat sources for extrusion-based printing of thermoplastic polymers. Subsequently, a numerical thermal analysis based on the finite element method (FEM) was conducted to investigate the temperature field in the print head during the heating process. Moreover, a temperature control strategy is proposed under which the temperature distribution of the print head can be regulated. The temperature uniformity can be improved with the proposed temperature control strategy. Lastly, groups of printing trials were implemented, and the printed filaments showed excellent uniformity of diameter when temperature distribution uniformity was controlled in the print head.
Machine Learning for Medical and Healthcare Data Analysis and Modelling: Case Studies and Performance Comparisons of Different Methods

In smart healthcare, binary classification is one of the most important tasks for disease or clinical outcome prediction. Machine learning (ML) methods have great potential to discover knowledge from data. While there are a wide range of ML methods that are potentially useful, the selection, design and implementation of an appropriate method from a variety of ML models for specific prediction tasks is still quite challenging in many medical and healthcare data modelling tasks. This paper investigates, evaluate and compares nine typical representative ML methods using four clinical and healthcare datasets. The main attention is paid to the strengths and weaknesses of these methods and provide suggestions to researchers in related fields. Results demonstrate that ensemble learning (such as Adaboost and random forest) outperform the other approaches in prediction accuracy and interpretability.
Improved method for phase wraps reduction in profilometry

In order to completely eliminate, or greatly reduce the number of phase wraps in 2D wrapped phase map, Gdeisat et al. proposed an algorithm, which uses shifting the spectrum towards the origin. But the spectrum can be shifted only by an integer number, meaning that the phase wraps reduction is often not optimal. In addition, Gdeisat's method will take much time to make the Fourier transform, inverse Fourier transform, select and shift the spectral components. In view of the above problems, we proposed an improved method for phase wraps elimination or reduction. First, the wrapped phase map is padded with zeros, the carrier frequency of the projected fringe is determined by high resolution, which can be used as the moving distance of the spectrum. And then realize frequency shift in spatial domain. So it not only can enable the spectrum to be shifted by a rational number when the carrier frequency is not an integer number, but also reduce the execution time. Finally, the experimental results demonstrated that the proposed method is feasible.
Selection Game for Consensus-Based Decentralized Aggregators of Distributed Energy Resources in a Blockchain Ecosystem

Aggregators can be effective in organizing distributed energy resources (DERs) for smart grids and electricity markets. The recent development of blockchain and peer-to-peer (P2P) networks provides a new ecosystem for aggregating DERs. Initial studies have mainly used off-the-shelf consensuses, which may struggle to balance node sizes and computational intensities. Moreover, the dynamics of DERs changing their selection among multiple aggregators over time are rarely considered in most related literature. This freedom of selection, which is encouraged by the electricity market, can be better activated by a blockchain. In this study, a game-dynamic-based selection framework for multiple aggregators of DERs is proposed in a decentralized blockchain ecosystem. First, a proof-of-dual-credibility (Po2C) protocol is established so that DERs in such an aggregator can reach consensus. At the same time, for one DER node, both an unavoidable objective credit and a malicious subjective credit constitute its credibility with different weights. Then, a function with triple payoffs motivates DERs in terms of both the physical characteristics of being power supply devices and P2P nodes, the latter including consensus winning and data propagation. Third, the selection game of DERs among aggregators is modeled as an evolutionary game under replicator dynamics to find equilibrium. Numerical simulations with two and four aggregators show general stability in the selection game of DERs. Performance achieved with different consensuses and incentives are compared as well. The framework shows its great potential to organize DERs in a decentralized but aggregated mechanism in open electricity markets.
Controllable Preparation and Strengthening Strategies towards High-Strength Carbon Nanotube Fibers

Carbon nanotubes (CNTs) with superior mechanical properties are expected to play a role in the next generation of critical engineering mechanical materials. Crucial advances have been made in CNTs, as it has been reported that the tensile strength of defect-free CNTs and carbon nanotube bundles can approach the theoretical limit. However, the tensile strength of macro carbon nanotube fibers (CNTFs) is far lower than the theoretical level. Although some reviews have summarized the development of such fiber materials, few of them have focused on the controllable preparation and performance optimization of high-strength CNTFs at different scales. Therefore, in this review, we will analyze the characteristics and latest challenges of multiscale CNTFs in preparation and strength optimization. First, the structure and preparation of CNTs are introduced. Then, the preparation methods and tensile strength characteristics of CNTFs at different scales are discussed. Based on the analysis of tensile fracture, we summarize some typical strategies for optimizing tensile performance around defect and tube–tube interaction control. Finally, we introduce some emerging applications for CNTFs in mechanics. This review aims to provide insights and prospects for the controllable preparation of CNTFs with ultra-high tensile strength for emerging cutting-edge applications.
False Discovery Rate (FDR) and Familywise Error Rate (FER) Rules for Model Selection in Signal Processing Applications

Model selection is an omnipresent problem in signal processing applications. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) are the most commonly used solutions to this problem. These criteria have been found to have satisfactory performance in many cases and had a dominant role in the model selection literature since their introduction several decades ago, despite numerous attempts to dethrone them. Model selection can be viewed as a multiple hypothesis testing problem. This simple observation makes it possible to use for model selection a number of powerful hypothesis testing procedures that control the false discovery rate (FDR) or the familywise error rate (FER). This is precisely what we do in this paper in which we follow the lead of the proposers of the said procedures and introduce two general rules for model selection based on FDR and FER, respectively. We show in a numerical performance study that the FDR and FER rules are serious competitors of AIC and BIC with significant performance gains in more demanding cases, essentially at the same computational effort.