Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Nutri-Score and Health Star Rating Nutrient Profiling Models Using Large Branded Foods Composition Database and Sales Data

Front-of-package nutrition labelling (FOPNL) is known as an effective tool that can encourage healthier food choices and food reformulation. A very interesting type of FOPNL is grading schemes. Our objective was to compare two market-implemented grading schemes—European Nutri-Score (NS) and Australian Health Star Rating (HSR), using large Slovenian branded foods database. NS and HSR were used for profiling 17,226 pre-packed foods and drinks, available in Slovenian food supply dataset (2020). Alignment between models was evaluated with agreement (% of agreement and Cohen’s Kappa) and correlation (Spearman rho). The 12-month nationwide sales-data were used for sale-weighing, to address market-share differences. Study results indicated that both models have good discriminatory ability between products based on their nutritional composition. NS and HSR ranked 22% and 33% of Slovenian food supply as healthy, respectively. Agreement between NS and HSR was strong (70%, κ = 0.62) with a very strong correlation (rho = 0.87). Observed profiling models were most aligned within food categories Beverages and Bread and bakery products, while less aligned for Dairy and imitates and Edible oils and emulsions. Notable disagreements were particularly observed in subcategories of Cheese and processed cheeses (8%, κ = 0.01, rho = 0.38) and Cooking oils (27%, κ = 0.11, rho = 0.40). Further analysis showed that the main differences in Cooking oils were due to olive oil and walnut oil, which are favoured by NS and grapeseed, flaxseed and sunflower oil that are favoured by HSR. For Cheeses and cheese products, we observed that HSR graded products across the whole scale, with majority (63%) being classified as healthy (≥3.5 *), while NS mostly graded lower scores. Sale-weighting analyses showed that offer in the food supply does not always reflect the sales. Sale-weighting increased overall agreement between profiles from 70% to 81%, with notable differences between food categories. In conclusion, NS and HSR were shown as highly compliant FOPNLs with few divergences in some subcategories. Even these models do not always grade products equally high, very similar ranking trends were observed. However, the observed differences highlight the challenges of FOPNL ranking schemes, which are tailored to address somewhat different public health priorities in different countries. International harmonization can support further development of grading type nutrient profiling models for the use in FOPNL, and make those acceptable for more stake-holders, which will be crucial for their successful regulatory implementation.
Model Checks for Generalized Linear Models

In this paper we propose and study non-parametric tests for the validity of (composite) Generalized Linear Models with a given parametric link structure, which are based on certain empirical processes marked by the residuals. When properly transformed to their innovation part the resulting test statistics are distribution-free. The method perfectly adapts to a situation, when also the input vector follows a dimension reducing model.
Radar image study of ocean breaking waves

In this paper a radar image study of electromagnetic scattering from one dimensional ocean breaking waves is described. Backscatter results are considered for 1-D surfaces which satisfy an impedance boundary condition (IBC). Radar images of single ocean breaking wave profiles are formed through back-projection tomography. Detailed investigations of the images are also provided to clarify major and secondary scattering events as well as the polarization dependence. A ray tracing analysis is performed to predict possible multiple scattering images.
Accounting outsourcing and its relationship with financial performance of SMEs: Manager and employee perspectives

There is a gap in the literature related to understanding the relationship between outsourcing of accounting functions and financial performance (productivity and cost efficiency) of SMEs. This study examines the relationship between accounting outsourcing and financial performance in terms of cost efficiency and profitability in SMEs operating in Greater Sydney, Australia. An online survey of employees and managers (N = 80) of Australian SMEs using snowball sampling assessed the demographics, accounting outsourcing, organisational profitability, and cost-effectiveness of these SMEs. Data were analysed using linear regression and correlation analyses. The empirical analysis revealed that the outsourcing of tax services was related to a better financial performance of SMEs in terms of both cost efficiency and profitability. The outsourcing of management accounting functions was beneficial to the productivity of SMEs, whereas the outsourcing of general accounting was not related to higher productivity. Collectively, the findings of this study will assist SMEs in identifying resource gaps and the reasons for turning to external accountancy services to close the gaps and improve their financial performance.
Compartmentalization engineering of yeasts to overcome precursor limitations and cytotoxicity in terpenoid production

Metabolic engineering strategies for terpenoid production have mainly focused on bottlenecks in the supply of precursor molecules and cytotoxicity to terpenoids. In recent years, the strategies involving compartmentalization in eukaryotic cells has rapidly developed and have provided several advantages in the supply of precursors, cofactors and a suitable physiochemical environment for product storage. In this review, we provide a comprehensive analysis of organelle compartmentalization for terpenoid production, which can guide the rewiring of subcellular metabolism to make full use of precursors, reduce metabolite toxicity, as well as provide suitable storage capacity and environment. Additionally, the strategies that can enhance the efficiency of a relocated pathway by increasing the number and size of organelles, expanding the cell membrane and targeting metabolic pathways in several organelles are also discussed. Finally, the challenges and future perspectives of this approach for the terpenoid biosynthesis are also discussed.
(Mis-)use of standard Autopilot and Full Self-Driving (FSD) Beta: Results from interviews with users of Tesla's FSD Beta

Tesla's Full Self-Driving Beta (FSD) program introduces technology that extends the operational design domain of standard Autopilot from highways to urban roads. This research conducted 103 in-depth semi-structured interviews with users of Tesla's FSD Beta and standard Autopilot to evaluate the impact on user behavior and perception. It was found that drivers became complacent over time with Autopilot engaged, failing to monitor the system, and engaging in safety-critical behaviors, such as hands-free driving, enabled by weights placed on the steering wheel, mind wandering, or sleeping behind the wheel. Drivers' movement of eyes, hands, and feet became more relaxed with experience with Autopilot engaged. FSD Beta required constant supervision as unfinished technology, which increased driver stress and mental and physical workload as drivers had to be constantly prepared for unsafe system behavior (doing the wrong thing at the worst time). The hands-on wheel check was not considered as being necessarily effective in driver monitoring and guaranteeing safe use. Drivers adapt to automation over time, engaging in potentially dangerous behaviors. Some behavior seems to be a knowing violation of intended use (e.g., weighting the steering wheel), and other behavior reflects a misunderstanding or lack of experience (e.g., using Autopilot on roads not designed for). As unfinished Beta technology, FSD Beta can introduce new forms of stress and can be inherently unsafe. We recommend future research to investigate to what extent these behavioral changes affect accident risk and can be alleviated through driver state monitoring and assistance.
Compressed sensing based on the improved wavelet transform for image processing

Compressed sensing theory is a new sampling theory that can sample signal in a below sampling rate than the traditional Nyquist sampling theory. Compressed sensing theory that has given a revolutionary solution is a novel sampling and processing theory under the condition that the signal is sparse or compressible. This paper investigates how to improve the theory of CS and its application in imaging system. According to the properties of wavelet transform sub-bands, an improved compressed sensing algorithm based on the single layer wavelet transform was proposed. Based on the feature that the most information was preserved on the low-pass layer after the wavelet transform, the improved compressed sensing algorithm only measured the low-pass wavelet coefficients of the image but preserving the high-pass wavelet coefficients. The signal can be restricted exactly by using the appropriate reconstruction algorithms. The reconstruction algorithm is the key point that most researchers focus on and significant progress has been made. For the reconstruction, in order to improve the orthogonal matching pursuit (OMP) algorithm, increased the iteration layers make sure low-pass wavelet coefficients could be recovered by measurements exactly. Then the image could be reconstructed by using the inverse wavelet transform. Compared the original compressed sensing algorithm, simulation results demonstrated that the proposed algorithm decreased the processed data, signal processed time decreased obviously and the recovered image quality improved to some extent. The PSNR of the proposed algorithm was improved about 2 to 3 dB. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
The Hawaii K-band galaxy survey. 3: Spectroscopy of K less than 20 galaxies

We present spectra and multicolor (B, I, K) data for near-infrared (K) selected spatially complete magnitude limited (K is less than 20) galaxy samples from the Hawaii Survey. The redshift indentification of the sample of 298 galaxies is substantially complete to a B magnitude of 26 and an I magnitude of 22.5, and identification of observed galaxies ranges from nearly 100% completeness at K is less than 18 to approximately 70% completeness at K = 19-20. We note that many of the unidentified objects appear to be red (I-K) objects which are flat in the optical and spectroscopically featureless. Strengths of spectral-line features and breaks are tabulated for the 262 galaxies with reasonably secure redshifts. The measured redshifts may nearly all fall at z is less than or approximately 1, with the exception of a compact absorption-line object at z = 2.35. At K is less than or equal to 18, the redshift distribution is well fitted by a model with no luminosity evolution, implying that from the K-band Hubble diagram, the Hubble constant can vary at most by 10% over the redshift range from z approximately 0.025 to 0.25, and that positive luminosity evolution at any significant level between z = 0 and z = 1 is ruled out. However, the evolution of both the emission-line strengths and the 4000 A break indicates that galaxies were undergoing significantly more star formation at z = 1 than at the present time.
First Measurements of Extragalactic CO(4--3)

We report the first measurements of extragalactic CO(4-3) toward a sample of prominent nearby galactic nuclei (NGC 253, IC 342, M82, and NGC 6946). With the J=4 level 55 K above ground, and critical densities for excitation of order ∼10 4 -10 5 cm −3 , detection of the CO(4-3) line provides direct evidence for large amounts of warm, dense molecular gas. A multitransition CO excitation analysis, based on all presently observed 12 CO and 13 CO transitions up to J=6-5, has been performed to confine the physical state of the gas
A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics

The recent literature on maximum likelihood cointegration theory studies Gaussian vector autoregression (VAR) models allowing for some deterministic components in the form of polynomials in time. An examination is presented of such models for variables integrated at most of order one, when tests for cointegration involve statistics with non-standard asymptotic distributions. The asymptotic distributions of these test statistics are known to be functions of the distribution of certain matrices of stochastic variables involving integrals of Brownian motions. In fact, conditional on which restrictions on the coefficients of the polynomial in time are valid, different asymptotic distributions are obtained. The cases examined exhaust the hypotheses relevant to the cointegration rank analysis of I(1) variables in models involving up to linear trends and possibly seasonal dummies. The examination solves the numerical problem in making most of the interesting quantiles of these asymptotic distributions available to the applied econometrician.
ON STATIONARY PROCESSES IN THE PLANE

The sampling theory of stationary processes in space is not completely analogous to that of stationary time series, due to the fact that the variate of a time series is influenced only by past values, while for a spatial process dependence extends in all directions. This point is elaborated in ? 2-4. The estimation and test theory developed in ? 7 is applied in ? 8 to uniformity data for wheat and oranges. The final section is devoted to an examination of some particular two-dimensional processes.
DISCRIMINANT ANALYSIS OF MULTI-DIMENSIONAL INTERVAL DATA AND ITS APPLICATION TO CHEMICAL SENSING

A conventional discriminant problem is to determine a discriminant function, which maps a point in a multi-dimensional feature space to a point in a one-dimensional decision space, using a set of labeled (known classification) samples. In many cases, attribute values of each sample are not constant but fluctuating with time. In this paper, we represent the fluctuating attribute values of each sample by an interval vector in the feature space, and propose a discriminant method for a set of interval vectors. The proposed method is based on a linear interval model which maps an interval vector in the feature space to an interval in the decision space. A mathematical programming problem is formulated to determine the coefficients of this model. We also propose a set of discriminant rules to discriminate unknown samples. The proposed method is applied to a smell sensing problem.
Muscular Damping Distribution Strategy for Bio-Inspired, Soft Motion Control at Variable Precision

Bio-inspired and compliant control approaches have been studied by roboticists for decades to achieve more natural robot motion. Independent of this, medical and biological researchers have discovered a wide variety of muscular properties and higher-level motion characteristics. Although both disciplines strive to better understand natural motion and muscle coordination, they have yet to meet. This work introduces a novel robotic control strategy that bridges the gap between these distinct areas. By applying biological characteristics to electrical series elastic actuators, we developed a simple yet efficient distributed damping control strategy. The presented control covers the entire robotic drive train, from abstract whole-body commands to the applied current. The functionality of this control is biologically motivated, theoretically discussed, and finally evaluated through experiments on the bipedal robot Carl. Together, these results demonstrate that the proposed strategy fulfills all requirements that are necessary to continue developing more complex robotic tasks based on this novel muscular control philosophy.
Modeling the Population Dynamics of Antibiotic-Resistant Bacteria:. AN Agent-Based Approach

The response of bacterial populations to antibiotic treatment is often a function of a diverse range of interacting factors. In order to develop strategies to minimize the spread of antibiotic resistance in pathogenic bacteria, a sound theoretical understanding of the systems of interactions taking place within a colony must be developed. The agent-based approach to modeling bacterial populations is a useful tool for relating data obtained at the molecular and cellular level with the overall population dynamics. Here we demonstrate an agent-based model, called Micro-Gen, which has been developed to simulate the growth and development of bacterial colonies in culture. The model also incorporates biochemical rules and parameters describing the kinetic interactions of bacterial cells with antibiotic molecules. Simulations were carried out to replicate the development of methicillin-resistant S. aureus (MRSA) colonies growing in the presence of antibiotics. The model was explored to see how the properties of the system emerge from the interactions of the individual bacterial agents in order to achieve a better mechanistic understanding of the population dynamics taking place. Micro-Gen provides a good theoretical framework for investigating the effects of local environmental conditions and cellular properties on the response of bacterial populations to antibiotic exposure in the context of a simulated environment.
Holistic Framework to Contextualize Dietary Quality Assessment: A Critical Review

Numerous dietary quality indices exist to help quantify overall dietary intake and behaviors associated with positive health outcomes. Most indices focus solely on biomedical factors and nutrient or food intake, and exclude the influence of important social and environmental factors associated with dietary intake. Using the Diet Quality Index- International as one sample index to illustrate our proposed holistic conceptual framework, this critical review seeks to elucidate potential adaptations to dietary quality assessment by considering—in parallel—biomedical, environmental, and social factors. Considering these factors would add context to dietary quality assessment, influencing post-assessment recommendations for use across various populations and circumstances. Additionally, individual and population-level evidence-based practices could be informed by contextual social and environmental factors that influence dietary quality to provide more relevant, reasonable, and beneficial nutritional recommendations.
An Optimization Strategy for MADM Framework with Confidence Level Aggregation Operators under Probabilistic Neutrosophic Hesitant Fuzzy Rough Environment

In this research, we first offer unique notions of averaging and geometric aggregation operators with confidence level by employing a probabilistic neutrosophic hesitant fuzzy rough framework. Then, we look into other descriptions of the suggested operators, such as idempotency, boundedness, and monotonicity. Additionally, for the derived operators, we establish the score and accuracy functions. We also provide a novel approach to assessing the selection procedure for smart medical devices (SMDs). The selection criteria for SMDs are quite complex, which is the most noteworthy feature of this investigation. It is suggested that these processes be simulated using a method utilizing a hesitant fuzzy set, a rough set, and a probabilistic single-valued neutrosophics set. The proposed approach is employed in the decision-making process, while taking into consideration the decision-makers’ (DMs’) level of confidence in the data they have obtained in order to deal with ambiguity, incomplete data, and uncertainty in lower and upper approximations. The major goal was to outline the issue’s complexities in order to pique interest among experts in the health care sector and encourage them to evaluate SMDs using various evaluation standards. The analysis of the technique’s outcomes demonstrated that the rankings and the results themselves were adequate and trustworthy. The effectiveness of our suggested improvements is also demonstrated through a symmetrical analysis. The symmetry behavior shows that the current techniques address more complex and advanced data.
SPECTRAL MORPHOLOGY IN TRUMPLER 16.

The results of classifying 32 stars in the Trumpler 16 field from spectrograms are presented. The stars are identified and their unreddened visual magnitudes and MK types are given. The distance to the cluster was computed at 2.3 kpc, using normal stars on the main sequence without any suspicion of duplicity and excluding O3 stars. The H-R diagram for Trumpler 16 is presented, identifying the evolved stars, the abnormal ones, and the suspected binaries. A WR star of magnitude -6.6 is present, and the minimum percentage of stars with 'b' characteristics in the range B0-B2 is around 30 percent, in fairly good agreement with the correlation found by Abt (1979) between age and percentage of 'b' stars. Also found were one star with a weak shell, two stars with possible spectroscopic companions, one composite spectrum, and one object whose spectrum is not normal but cannot be interpreted convincingly at present.
Loop closure detection using local Zernike moment patterns

This paper introduces a novel image description technique that aims at appearance based loop closure detection for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure is achieved by simply comparing the most recent image with the images in the past trajectory. The technique has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms of computation efficiency and loop closure precision.
Optical Studies of NaCl Single Crystals in 10 eV Region II. The Spectra of Conductivity at Low Temperatures, Absorption Constant and Energy Loss

Kramers-Kronig analysis was made on the reflectivity spectra of NaCl single crystals in the region 6eV to 12eV for temperature range from 10°K to 573°K. The spectra of absorption constant and energy loss are reported for the temperature range from 10°K to 573°K and compared with those obtained on thin film specimens. The spectra of complex dielectric constant, complex refractive index and conductivity are presented at temperatures of 10°K, 78°K and 298°K. The analysis of σ-spectrum in the region of chlorine doublet and the plateau near the step leads to the conclusion that the spectrum in this region consists of absorptions due to hydrogen like series of exciton. Each of the chlorine doublet standing for n =1 state exciton lines can be fitted with a suitable asymmetric Lorentzian function having a positive degree of asymmetry. The oscillator strengths of (3/2, 1/2)- and (1/2, 1/2)-components are estimated at 0.212 and 0.252 at 10°K, respectively. The exchange interaction between electron and hole of an ex.
Reverse Inference in Neuropsychology

Reverse inference in cognitive neuropsychology has been characterized as inference to ‘psychological processes’ from ‘patterns of activation’ revealed by functional magnetic resonance or other scanning techniques. Several arguments have been provided against the possibility. Focusing on Machery’s ([2014]) presentation, we attempt to clarify the issues, rebut the impossibility arguments, and propose and illustrate a strategy for reverse inference. 1. The Problem of Reverse Inference in Cognitive Neuropsychology2. The Arguments2.1. The anti-Bayesian argument3. Patterns of Activation4. Reverse Inference Practiced5. Seek and Ye Shall Find, Maybe6. Conclusion The Problem of Reverse Inference in Cognitive Neuropsychology The Arguments The anti-Bayesian argument Patterns of Activation Reverse Inference Practiced Seek and Ye Shall Find, Maybe Conclusion