Miracle-dz/UniFAR-FTUs huggingface dataset:
A RIGOROUS FREE-FORM LENS MODEL OF A2744 TO MEET THE HUBBLE FRONTIER FIELDS CHALLENGE

Hubble Frontier Fields (HFF) imaging of the most powerful lensing clusters provides access to the most magnified distant galaxies. The challenge is to construct lens models capable of describing these complex massive, merging clusters so that individual lensed systems can be reliably identified and their intrinsic properties accurately derived. We apply the free-form lensing method (WSLAP+) to A2744, providing a model independent map of the cluster mass, magnification, and geometric distance estimates to multiply lensed sources. We solve simultaneously for a smooth cluster component on a pixel grid, together with local deflections by the cluster member galaxies. Combining model prediction with photometric redshift measurements, we correct and complete several systems recently claimed and identify four new systems totaling 65 images of 21 systems spanning a redshift range of 1.4 < z < 9.8. The reconstructed mass shows small enhancements in the directions where significant amounts of hot plasma can be seen in X-ray. We compare photometric redshifts with “geometric redshifts,” finding a high level of self-consistency. We find excellent agreement between predicted and observed fluxes with a best-fit slope of 0.999 ± 0.013 and an rms of ∼0.25 mag, demonstrating that our magnification correction of the lensed background galaxies is very reliable. Intriguingly, few multiply lensed galaxies are detected beyond z ≃ 7.0, despite the high magnification and the limiting redshift of z ≃ 11.5 permitted by the HFF filters. With the additional HFF clusters, we can better examine the plausibility of any pronounced high-z deficit with potentially important implications for the reionization epoch and the nature of dark matter.
Challenges of business models for sustainability in startups

Purpose This study aims to analyze the challenges startups face in implementing business models for sustainability. In particular, the research question of this study is: How do the challenges faced by startups affect business models for sustainability in the context of an emerging country? Design/methodology/approach Startups are increasingly incorporating ways to thrive in a competitive environment with innovative sustainable business models, a key factor for competitive advantage and corporate sustainability. This paper analyses startups’ challenges in adopting business models for sustainability through a case study in two startups, using the sustainable value exchange matrix (SVEM) tool through workshops, to carry out the diagnosis of these challenges. Findings The barriers and challenges of business models for sustainability in startups were found in different categories, where the main barriers are linked to the institutional category, the organizational and the market and sales culture. Thus, the authors concluded that there is a need to reformulate public policies and to have greater participation of the actors involved. Research limitations/implications The main limitation of the research is the number of case studies (only two), which makes it difficult to generalize the results. Practical implications The research presents two major contributions. First, through the case studies, it is possible to verify that the barriers and challenges in business models for sustainability have relevance for startups. The second contribution is the adaptation of SVEM in conducting the debate by incorporating the barriers and challenges in value creation and delivery system. Social implications This study contributes to the business models for sustainability literature to better understand the challenges startups face in practice and can serve as insights to help overcome them. As this is an empirical study, the information gathered can help create metrics and public policies to achieve the United Nations sustainable development goals. Originality/value The present research has as originality the analysis of the challenges in startups in implementing business models for sustainability and their relationships with the value proposition, capture and creation, as well as and delivery (adapted to the challenges found in the literature) applying the SVEM tool proposed by Morioka et al. (2018).
Old and New No Go Theorems on Interacting Massless Particles in Flat Space

We review model independent arguments showing that massless particles interacting with gravity in a Minkowski background space can have at most spin two. These arguments include a classic theorem due to Weinberg, as well as a more recent extension of the Weinberg-Witten theorem. A puzzle arising from an apparent counterexample to these theorems is examined and resolved.
Quantum Energy Current Induced Coherence in a Spin Chain under Non-Markovian Environments

We investigate the time-dependent behaviour of the energy current between a quantum spin chain and its surrounding non-Markovian and finite temperature baths, together with its relationship to the coherence dynamics of the system. To be specific, both the system and the baths are assumed to be initially in thermal equilibrium at temperature Ts and Tb, respectively. This model plays a fundamental role in study of quantum system evolution towards thermal equilibrium in an open system. The non-Markovian quantum state diffusion (NMQSD) equation approach is used to calculate the dynamics of the spin chain. The effects of non-Markovianity, temperature difference and system-bath interaction strength on the energy current and the corresponding coherence in cold and warm baths are analyzed, respectively. We show that the strong non-Markovianity, weak system-bath interaction and low temperature difference will help to maintain the system coherence and correspond to a weaker energy current. Interestingly, the warm baths destroy the coherence while the cold baths help to build coherence. Furthermore, the effects of the Dzyaloshinskii–Moriya (DM) interaction and the external magnetic field on the energy current and coherence are analyzed. Both energy current and coherence will change due to the increase of the system energy induced by the DM interaction and magnetic field. Significantly, the minimal coherence corresponds to the critical magnetic field which causes the first order phase transition.
Altered Capacity for H2S Production during the Spontaneous Differentiation of Caco-2 Cells to Colonocytes Due to Reciprocal Regulation of CBS and SELENBP1

Hydrogen sulfide (H2S) has been proposed to promote tumor growth. Elevated H2S levels have been detected in human colorectal cancer (CRC) biopsies, resulting from the selective upregulation of cystathionine β-synthase (CBS). In contrast, the recently identified novel H2S-generating enzyme, selenium-binding protein 1 (SELENBP1), is largely suppressed in tumors. Here, we provide the first comparative analysis of the four human H2S-producing enzymes and the key H2S-catabolizing enzyme, sulfide:quinone oxidoreductase (SQOR), in Caco-2 human colorectal adenocarcinoma cells. The gene expression pattern of proliferating Caco-2 cells parallels that of CRC, while confluent cells undergo spontaneous differentiation to a colonocyte-like phenotype. SELENBP1 and SQOR were strongly upregulated during spontaneous differentiation, whereas CBS was downregulated. Cystathionine γ-lyase and 3-mercaptopyruvate sulfurtransferase remained unaffected. Terminally differentiated cells showed an enhanced capacity to produce H2S from methanethiol and homocysteine. Differentiation induced by exposure to butyrate also resulted in the upregulation of SELENBP1, accompanied by increased SELENBP1 promoter activity. In contrast to spontaneous differentiation, however, butyrate did not cause downregulation of CBS. In summary, SELENBP1 and CBS are reciprocally regulated during the spontaneous differentiation of Caco-2 cells, thus paralleling their opposing regulation in CRC. Butyrate exposure, while imitating some aspects of spontaneous differentiation, does not elicit the same expression patterns of genes encoding H2S-modulating enzymes.
An optimized ion trap geometry to measure quadrupole shifts of 171Yb+ clocks

We propose a new ion-trap geometry to carry out accurate measurements of the quadrupole shifts in the 171Yb ion. This trap will minimize the quadrupole shift due to the harmonic component of the confining potential by an order of magnitude. This will be useful to reduce the uncertainties in the clock frequency measurements of the 6s 2S1/2 → 4f136s2 2F7/2 and 6s 2S1/2 → 5d 2D3/2 transitions, from which we can deduce the precise values of the quadrupole moments (Θs) of the 4f136s2 2F7/2 and 5d 2D3/2 states. Moreover, it may be able to affirm the validity of the measured Θ value of the 4f136s2 2F7/2 state, for which three independent theoretical studies defer almost by one order of magnitude from the measurement. We also calculate Θs using the relativistic coupled-cluster (RCC) method. We use these Θ values to estimate the quadrupole shift that can be measured in our proposed ion trap experiment.
Optimization of quantum state tomography in the presence of experimental constraints

In the absence of experimental constraints, optimal measurement schemes for quantum state tomography are well understood. We consider the scenario where the experimenter doesn't have arbitrary freedom to construct their measurement set, and may therefore not be able to implement a known optimal scheme. We introduce a simple procedure for minimizing the uncertainty in the reconstructed quantum state for an arbitrary tomographic scheme. We do this by defining a figure of merit based on the equally-weighted variance of the measurement statistics. This figure of merit is straightforwardly based on the singular value decomposition of the measurement matrix, making it well-suited for optimization.
Recovery of Antioxidants from Tomato Seed Industrial Wastes by Microwave-Assisted and Ultrasound-Assisted Extraction

Tomato seed (TS) wastes are obtained in large amounts from the tomato processing industry. In this work, microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) of antioxidant compounds from TS were optimized by using response surface methodology. The effect of MAE and UAE main extraction parameters was studied on total phenolic content (TPC) and antioxidant activity (DPPH) responses. Antioxidant, structural, morphological, and thermal properties of MAE and UAE extracts were evaluated. A great influence of ethanol concentration was observed in both extraction methods. Optimal MAE conditions were determined as 15 min, 80 °C, 63% ethanol and 80 mL, with a desirability value of 0.914, whereas 15 min, 61% ethanol and 85% amplitude (desirability = 0.952) were found as optimal conditions for UAE. MAE extracts exhibited higher TPC and antioxidant activity values compared to UAE (1.72 ± 0.04 and 1.61 ± 0.03 mg GAE g TS−1 for MAE and UAE, respectively). Thermogravimetric analysis (TGA) results suggested the presence of some high molecular weight compounds in UAE extracts. Chlorogenic acid, rutin and naringenin were identified and quantified by HPLC-DAD-MS as the main polyphenols found by MAE and UAE, showing MAE extracts higher individual phenolics content (1.11–2.99 mg 100 g TS−1). MAE and UAE have shown as effective green techniques for extracting bioactive molecules with high antioxidant activity from TS with high potential to be scaled-up for valorizing of TS industrial wastes.
The Improvement of Porcine In Vitro Embryo Development through Regulating Autophagy by miRNA-143 Inhibition

Simple Summary The improvement of in vitro embryo development is an important factor for the advancement of reproductive technology. Recently, studying microRNA related to early embryos is a strategic approach. This research applies miR-143 (mimics and inhibitors) to the porcine parthenogenetically activated embryos. Interesting results are revealed in this study: the miR-143 inhibitor improves the embryo development to the blastocyst. In addition, our research reveals the enhancement of autophagy and ER-phagy associated markers with higher levels of gene expression. Abstract In vitro embryo research is an important stage for the advancement of many reproductive technologies in research and agriculture. For this reason, the improvement of in vitro embryo development is a strategic field worthy of investigation. Relatively little is known about miR-143 and its effects on autophagy associated with embryo development and in vitro embryo culture. In this study, we examined the effect of miR-143 (via mimics and inhibitors) on embryonic development threatened by microinjection after parthenogenetic activation. We evaluated rates of cleavage, blastocyst, and total cell number of blastocyst; additionally, we performed LC3 immunofluorescence analysis and mRNA expression analyses of genes associated with autophagy, endoplasmic reticulum (ER)-phagy, ER stress, embryo quality, and apoptosis. The inhibition of miR-143 positively influenced embryo development by increasing the activity of autophagy and ER-phagy and the expression of embryo quality-related genes, while reducing apoptosis. In contrast, treatment with miR-143 mimics increased ER stress-related gene expression and apoptosis, and reduced embryo development. Together, our findings indicate that miR-143 plays a role in the interplay between autophagy, ER-phagy, and embryo quality during early porcine embryo development.
A simple model to interpret the ultraviolet, optical and infrared SEDs of galaxies

Abstract We present a simple, largely empirical but physically motivated model, which is designed to interpret consistently multi-wavelength observations from large samples of galaxies in terms of physical parameters, such as star formation rate, stellar mass and dust content. Our model is both simple and versatile enough to allow the derivation of statistical constraints on the star formation histories and dust contents of large samples of galaxies using a wide range of ultraviolet, optical and infrared observations. We illustrate this by deriving median-likelihood estimates of a set of physical parameters describing the stellar and dust contents of local star-forming galaxies from the Spitzer Infrared Nearby Galaxy Sample (SINGS) and from a newly-matched sample of SDSS galaxies observed with GALEX, 2MASS, and IRAS. The model reproduces well the observed spectral energy distributions of these galaxies across the entire wavelength range from the far-ultraviolet to the far-infrared. We find important correlations between the physical parameters of galaxies which are useful to investigate the star formation activity and dust properties of galaxies. Our model can be straightforwardly applied to interpret observed ultraviolet-to-infrared spectral energy distributions (SEDs) from any galaxy sample.
Modelling of Heat Transfer in Single Crystal Growth

An attempt is made to review the heat transfer and the related problems encountered in the simulation of single crystal growth. The peculiarities of conductive, convective and radiative heat transfer in the different melt, solution, and vapour growth methods are discussed. The importance of the adequate description of the optical crystal properties (semitransparency, specular reflecting surfaces) and their effect on the heat transfer is stresses. Treatment of the unknown phase boundary fluid/crystal as well as problems related to the assessment of the quality of the grown crystals (composition, thermal stresses, point defects, disclocations etc.) and their coupling to the heat transfer/fluid flow problems is considered. Differences between the crystal growth simulation codes intended for the research and for the industrial applications are indicated. The problems of the code verification and validation are discussed; a brief review of the experimental techniques for the study of heat transfer and flow structure in crystal growth is presented. The state of the art of the optimization of the growth facilities and technological processes is discussed. An example of the computations of the heat transfer and crystal growth of bulk SiC crystals by the sublimation method is presented.
A Regularized Real-Time Integrator for Data-Driven Control of Heating Channels

In many contexts of scientific computing and engineering science, phenomena are monitored over time and data are collected as time-series. Plenty of algorithms have been proposed in the field of time-series data mining, many of them based on deep learning techniques. High-fidelity simulations of complex scenarios are truly computationally expensive and a real-time monitoring and control could be efficiently achieved by the use of artificial intelligence. In this work we build accurate data-driven models of a two-phase transient flow in a heated channel, as usually encountered in heat exchangers. The proposed methods combine several artificial neural networks architectures, involving standard and transposed deep convolutions. In particular, a very accurate real-time integrator of the system has been developed.
Optically controlled phase gate and teleportation of a controlled-not gate for spin qubits in a quantum-dot–microcavity coupled system

Assisted with linear optical manipulation, single photon, entangled photon pairs, photon measurement, and classical communication, a scheme for two-spin qubits phase gate and teleportation of a CNOT gate between two electron spins from acting on local qubits to acting on remote qubits using quantum dots in optical microcavities is proposed. The scheme is based on spin selective photon reflection from the cavity and is achieved in a deterministic way by the sequential detection of photons and the single-qubit rotations of a single electron spin in a self-assembled GaAs/InAs quantum dot. The feasibility of the scheme is assessed showing that high average fidelities of the gates are achievable in the weak-coupling regime when the side leakage and cavity loss are low. The scheme opens promising perspectives for long-distance quantum communication, distributed quantum computation, and constructing remote quantum information processing networks.
Coffee Yield Stability as a Factor of Food Security

Yield fluctuation is a major risk in all agricultural sectors, and it influences Goal 2 (food security) of the UN SDGs. Yield fluctuations are expected due to climate change, risking stable coffee supplies, and compromising coffee-exporting countries’ ability to earn revenue to pay for food imports. Technology minimizing yield fluctuations is crucial for food security and for coffee farmers to earn a stable income. Fluctuations are small if yields remain close to the mean yield trends. In this study, the coffee yields of major producers are analyzed, together with zonal temperature data, to see where coffee is grown with stable technology under rising temperatures; thus, we demonstrate the advantages of the Yield Stability Index (YSI) over traditional stability measurements in guiding policy formulation and managerial decisions. The Yield Stability Index (YSI) is applied for 1961–1994 and 1995–2020, for the world’s 12 major coffee-producing countries. The YSI indicates that of the 12 countries, only Indonesia, Honduras, and Mexico maintain stable yield levels, while Brazil and Vietnam considerably improve their yield stability, which traditional stability measures cannot grasp. Country-wise differences exist in environmental vulnerability and adaptability, with implications for food security. The novelty is the application of the YSI, and the connection between yield stability, climate change, and food security.
Road Scene Data Annotation with Semi-Automated Active Learning Framework for Convolutional Neural Networks

—Autonomous driving vehicles are considered the future of mobility as they can reduce the mortality rate owing to traffic accidents. This can also be achieved using cameras and a Convolutional Neural Network (CNN) to detect objects on the road and take necessary actions to prevent life-threatening occurrences. However, the current form of CNN needs to be trained using large amounts of annotated data, which is time consuming, expensive, and requires extensive manpower. These limitations can be overcome by using Active Learning (AL) systems, which only select a subset of informative data from the big data for annotation by humans. Although AL reduces the amount of data being used for CNN training, humans are still needed to annotate the data. This study proposes a Semi-Automated Active Learning system (SAAL) to further reduce the need for manpower for data annotation. SAAL uses AL and a new algorithm called Machine Teachers (MTs), which are stacked algorithms of pre-trained CNN and optical flow that use the temporal-spatial information video data from cameras on vehicles to help humans annotate images. This allows SAAL to be partially automated and further reduces human effort while roughly maintaining the accuracy of CNN to that of AL.
Ultrasound Radiomics Nomogram to Diagnose Sub-Centimeter Thyroid Nodules Based on ACR TI-RADS

Simple Summary Fine-needle aspiration or surgical resection is used to determine whether thyroid nodules (TNs) are benign or malignant, but there is a risk of overtreatment. Guidelines such as the American College of Radiology thyroid imaging reporting and data system consider follow-up for TNs < 1 cm, even with high-risk ultrasound features; therefore, for these TNs, an ultrasound radiomics nomogram was developed for noninvasive assessment of benign and malignant TNs. In addition, to assess the model’s diagnostic capabilities, the present study tested the model using an external validation group. The test results suggest that ultrasound radiomics is an effective, noninvasive method by which to diagnose noninvasive TNs. Abstract The aim of the present study was to develop a radiomics nomogram to assess whether thyroid nodules (TNs) < 1 cm are benign or malignant. From March 2021 to March 2022, 156 patients were admitted to the Affiliated Hospital of Nantong University, and from September 2017 to March 2022, 116 patients were retrospectively collected from the Jiangsu Provincial Hospital of Integrated Traditional Chinese and Western Medicine. These patients were divided into a training group and an external test group. A radiomics nomogram was established using multivariate logistics regression analysis using the radiomics score and clinical data, including the ultrasound feature scoring terms from the thyroid imaging reporting and data system (TI-RADS). The radiomics nomogram incorporated the correlated predictors, and compared with the clinical model (training set AUC: 0.795; test set AUC: 0.783) and radiomics model (training set AUC: 0.774; test set AUC: 0.740), had better discrimination performance and correction effects in both the training set (AUC: 0.866) and the test set (AUC: 0.866). Both the decision curve analysis and clinical impact curve showed that the nomogram had a high clinical application value. The nomogram constructed based on TI-RADS and radiomics features had good results in predicting and distinguishing benign and malignant TNs < 1 cm.