Miracle-dz/UniFAR-FTUs huggingface dataset:
Competing Retailers’ Environmental Investment: An Analysis under Different Power Structures

Sustainability issues in supply chains have received increasingly significant concern. Facing incentives such as environmental tax and consumer environmental awareness, firms and even retailers have started to make sustainability investments. To evaluate the retailer’s contribution to sustainability issues, we study a supply chain with one manufacturer and two symmetric competing retailers who have the option to make sustainable investment in their upstream members directly in green technology or clean production. We investigate the optimal sustainable investment and operation decisions under three power structures: (1) firms have the same power (Nash game); (2) the manufacturer is more powerful (Manufacturer-lead Stackelberg game) and (3) the retailers are more powerful (Retailer-lead Stackelberg game). By analyzing the optimal decisions and the economic performances, we show that the retailers always have incentives to make sustainable investment in all power structures. However, the retailers’ power affects firms’ decisions, the economic and the environmental performances. When the investment cost is low, the emission reduction due to investment is the most significant with less powerful retailers. With relatively high investment cost, whether the retailers having more power make more sustainable investment depends on the unit tax saving and effect factor of emission reduction on the demand. From the environmental perspective, simultaneous games may conduct the most significant total emission reduction in most cases. We also consider an asymmetric case and compare it with the symmetric one.
Profile variability of the H-alpha and H-beta broad emission lines in NGC5548

Between 1996 and 2002, we have carried out a spectral monitoring program for the Seyfert galaxy NGC 5548. High quality spectra (S/N>50), covering the spectral range (4000-7500)AA were obtained with the 6 m and 1 m telescopes of SAO (Russia) and with the 2.1 m telescope GHO (Mexico). We found that both the flux in the lines and the continuum gradually decreased, reaching minimum values during May-June 2002. The mean, rms, and the averaged over years, observed and difference line profiles of H-alpha and H-beta reveal the double peaked structure at the radial velocity ~+-1000km/s. The relative intensity of these peaks changes with time. During 1996, the red peak was the brightest, while in 1998 - 2002, the blue peak became the brighter one. In 2000-2002 a distinct third peak appeared in the red wing of H-alpha and H-beta line profiles. The radial velocity of this feature decreased between 2000 and 2002 from ~+2500 km/s to ~+2000 km/s. The fluxes of the various parts of the line profiles are well correlated with each other and also with the continuum flux. Shape changes of the different parts of the broad line are not correlated with continuum variations and, apparently, are not related to reverberation effects. Changes of the integral Balmer decrement are, on average, anticorrelated with the continuum flux variations. This is probably due to an increasing role of collisional excitation as the ionizing flux decreases. Our results favor the formation of the broad Balmer lines in a turbulent accretion disc with large and moving "optically thick" inhomogeneities, capable of reprocessing the central source continuum.
Face Inverse Rendering via Hierarchical Decoupling

Previous face inverse rendering methods often require synthetic data with ground truth and/or professional equipment like a lighting stage. However, a model trained on synthetic data or using pre-defined lighting priors is typically unable to generalize well for real-world situations, due to the gap between synthetic data/lighting priors and real data. Furthermore, for common users, the professional equipment and skill make the task expensive and complex. In this paper, we propose a deep learning framework to disentangle face images in the wild into their corresponding albedo, normal, and lighting components. Specifically, a decomposition network is built with a hierarchical subdivision strategy, which takes image pairs captured from arbitrary viewpoints as input. In this way, our approach can greatly mitigate the pressure from data preparation, and significantly broaden the applicability of face inverse rendering. Extensive experiments are conducted to demonstrate the efficacy of our design, and show its superior performance in face relighting over other state-of-the-art alternatives. Our code is available at https://github.com/AutoHDR/HD-Net.git.
Associating changes in the bacterial community of rumen and faeces and milk fatty acid profiles in dairy cows fed high-starch or starch and oil-supplemented diets

Abstract The experiment reported in this research paper aimed to evaluate the effects of high-starch or starch and oil-supplemented diets on rumen and faecal bacteria, and explore links between the structure of bacterial communities and milk fatty acid (FA) profiles. We used four Holstein dairy cows in a 4 × 4 Latin square design. Cows were fed a diet rich in cereals (high-starch diet with 23% starch content on dry matter (DM) basis), a diet supplemented with saturated FA from Ca salts of palm oil + 18% DM starch, a diet with high content of monounsaturated FA (from extruded rapeseeds) + 18% DM starch or a diet rich in polyunsaturated FA (from extruded sunflower seeds) + 17% DM starch. At the end of each experimental period, cows were sampled for rumen and faecal contents, which were used for DNA extraction and amplicon sequencing. Partial least squares (PLS) regression analysis highlighted diet-related changes in both rumen and faecal bacterial structures. Sparse PLS discriminant analysis was further employed to identify biologically relevant operational taxonomical units (OTUs) driving these differences. Our results show that Butyrivibrio discriminated the high-starch diet and linked positively with higher concentrations of milk odd- and branched-chain FA. YS2-related OTUs were key taxa distinguishing diets supplemented with Ca salts of palm oil or sunflower seeds and correlated positively with linoleic acid in milk. Similarly, diets modulated faecal bacterial composition. However, correlations between changes in faecal and rumen bacteria were poor. With this work, we demonstrated that high-starch or lipid-supplemented diets affect rumen and faecal bacterial community structure, and these changes could have a knock-on effect on milk FA profiles.
Ligand diversity contributes to the full activation of the jasmonate pathway in Marchantia polymorpha

Significance Plant survival in nature relies on activation of adaptive mechanisms, which largely depends on phytohormones, including jasmonates. Identification of bioactive jasmonates, their biosynthesis, and regulatory mechanisms is a fundamental scientific question. In vascular plants, jasmonates derive from polyunsaturated fatty acids containing 16- or 18-carbons. In contrast, bryophytes are also abundant in C20 and C22 long-chain polyunsaturated fatty acids (LCPUFAs), which are also found in animals. Here, we found that LCPUFAs are also a source of bioactive jasmonates. Using the liverwort Marchantia polymorpha (Mp), we discovered a ligand of Coronatine Insensitive 1 (MpCOI1) receptor, investigated its biosynthetic pathway, and showed that the discovered molecule cooperates with dinor-12-oxo-10,15(Z)-phytodienoic acid (dn-OPDA), the known MpCOI1 ligand, in the full activation of jasmonate responses in Marchantia.
How Does Carbon Dioxide–Induced Acidification Affect Postecdysial Exoskeletal Mineralization in the Blue Crab (Callinectes sapidus)?

Carbon dioxide (CO2) enrichment in seawater because of increased use of fossil fuels can possibly cause detrimental effects on the physiological processes of marine life, especially shell builders, due to CO2‐induced ocean acidification. We investigated, for the first time, specifically the effect of CO2 enrichment on postecdysial shell mineralization in Crustacea using the blue crab, Callinectes sapidus, as the model crustacean. It was hypothesized that CO2 enrichment of seawater would adversely affect exoskeletal mineralization in the blue crab. We used two groups of postecdysial crabs, with one group exposed to seawater at a pH of 8.20 and the other group treated with CO2‐acidified seawater with a pH of 7.80–7.90. After a period of 7 days, samples of exoskeleton and hemolymph were collected from the survivors. Enrichment was found to significantly increase exoskeletal magnesium content by 104% relative to control, whereas a statistically nonsignificant elevation of 31% in exoskeletal calcium was registered. Because CO2 treatment did not change the content of magnesium and calcium in the hemolymph, we postulate that increased exoskeletal mineralization in postecdysial blue crabs must stem from an increased influx of bicarbonate ions from the medium through the gill, to the hemolymph, and across the epidermis. In addition, the observed significant increase in the mass of exoskeleton following CO2 treatment must be at least partly accounted for by enhanced postmolt carbonate salt deposition to the shell. Environ Toxicol Chem 2022;41:2950–2954. © 2022 SETAC
5153 - NEAR-TIP ASYMPTOTIC ANALYSIS OF A PKN FLUID-DRIVEN FRACTURE WITH NON-LOCAL ELASTICITY EQUATION

One of the most widely used hydraulic fracturing models in the oil and gas industry is the socalled PKN model (named after Perkins, Kern and Nordgren). Based on certain geometrical assumptions, in the PKN model the elasticity equation (which relates the fracture width with the internal fluid pressure) is reduced to a local operator. This local operator imposes a zero pressure boundary condition at the fracture tips, which is incompatible with the classical square-root tip asymptote. This limitation reduces the applicability of the PKN model to situations in which the rock toughness is negligible. In this paper, we propose an alternative, non-local formulation for the elasticity equation of a PKN-type fracture. We also perform a near-tip asymptotic analysis of the new equation, and show the presence of a “plane-strain” zone near the advancing tip, in which the appeareance of fluid pressure singularities (similar to those obtained in pure plane-strain fracture models) is predicted.
Photon antibunching and nonlinear effects for a quantum dot coupled to a semiconductor cavity

Our model comprehensively simulates modern nanoscale semiconductor microcavities incorporating cavity quantum electrodynamics within both the weak and strong coupling regimes, using on-resonant laser excitation and nonresonant excitation due to a wetting layer. For weak coupling, the most significant effect is photon antibunching with nonresonant emission. We investigate how the antibunching characteristics change as the cavity finesse is increased towards the strong coupling regime. Antibunching can also be observed in a strongly coupled system with resonant excitation, using the photon blockade mechanism which has been demonstrated in atom systems. We calculate what cavity parameters are required to observe this effect. Experimentally these studies are equivalent to nonlinear pump probe measurements, where a strong pump, either resonant or nonresonant, is used to excite the coupled system, and the resulting state is characterized using a weak, resonant probe beam.
BROKEN BAR DETECTION BASED ON MEASUREMENT OF INDUCTION MOTOR ONE PHASE START-UP CURRENT JEDAN METOD DETEKCIJE OTKAZA TIPA SLOMLJENE ŠIPKE BAZIRAN NA MERENJU STRUJE ZALETA MOTORA U JEDNOJ FAZI

The paper presents the method of the broken bar detection based on analysis of an induction motor start-up current for no load case. Motor Current Signature Analysis (MCSA) is far the most popular approach to detect induction motor failures these days due to its benefits. The transient analysis is conducted by calculating power spectrum density (PSD) via Sort Time Fourier Transformation (STFT). Discriminative feature is obtained by calculating skenwness statistical parameter for given PSD for total amount of power per segment. It is shown that values for chosen features differ for healthy and broken bar rotor condition for a wide load range. Based on the proposed features estimation of rotor condition can be made. Study is done on real motor data collected from labora-tory and industrial environment.
Wine Yeast Cells Acquire Resistance to Severe Ethanol Stress and Suppress Insoluble Protein Accumulation during Alcoholic Fermentation

Winemaking is a dynamic and complex process in which ethanol concentrations gradually increase to reach >10% (vol/vol) through alcoholic fermentation. However, there is little information on protein damage in wine yeast during winemaking. ABSTRACT Under laboratory conditions, acute 10% (vol/vol) ethanol stress causes protein denaturation and accumulation of insoluble proteins in yeast cells. However, yeast cells can acquire resistance to severe ethanol stress by pretreatment with mild ethanol stress (6% vol/vol) and mitigate insoluble protein accumulation under subsequent exposure to 10% (vol/vol) ethanol. On the other hand, protein quality control (PQC) of yeast cells during winemaking remains poorly understood. Ethanol concentrations in the grape must increase gradually, rather than acutely, to more than 10% (vol/vol) during the winemaking process. Gradual increases in ethanol evoke two possibilities for yeast PQC under high ethanol concentrations in the must: suppression of insoluble protein accumulation through the acquisition of resistance or the accumulation of denatured insoluble proteins. We examined these two possibilities by conducting alcoholic fermentation tests at 15°C that mimic white winemaking using synthetic grape must (SGM). The results obtained revealed the negligible accumulation of insoluble proteins in wine yeast cells throughout the fermentation process. Furthermore, wine yeast cells in fermenting SGM did not accumulate insoluble proteins when transferred to synthetic defined (SD) medium containing 10% (vol/vol) ethanol. Conversely, yeast cells cultured in SD medium accumulated insoluble proteins when transferred to fermented SGM containing 9.8% (vol/vol) ethanol. Thus, wine yeast cells acquire resistance to the cellular impact of severe ethanol stress during fermentation and mitigate the accumulation of insoluble proteins. This study provides novel insights into the PQC and robustness of wine yeast during winemaking. IMPORTANCE Winemaking is a dynamic and complex process in which ethanol concentrations gradually increase to reach >10% (vol/vol) through alcoholic fermentation. However, there is little information on protein damage in wine yeast during winemaking. We investigated the insoluble protein levels of wine yeast under laboratory conditions in SD medium and during fermentation in SGM. Under laboratory conditions, wine yeast cells, as well as laboratory strain cells, accumulated insoluble proteins under acute 10% (vol/vol) ethanol stress, and this accumulation was suppressed by pretreatment with 6% (vol/vol) ethanol. During the fermentation process, insoluble protein levels were maintained at low levels in wine yeast even when the SGM ethanol concentration exceeded 10% (vol/vol). These results indicate that the progression of wine yeast through fermentation in SGM results in stress tolerance, similar to the pretreatment of cells with mild ethanol stress. These findings further the understanding of yeast cell physiology during winemaking.
Validation of the Thumbs food classification system as a tool to accurately identify the healthiness of foods

Abstract The Thumbs food classification system was developed to assist remote Australian communities to identify food healthiness. This study aimed to assess: (1) the Thumbs system’s alignment to two other food classification systems, the Health Star Rating (HSR) and the Northern Territory School Canteens Guidelines (NTSCG); (2) its accuracy in classifying ‘unhealthy’ (contributing to discretionary energy and added sugars) and ‘healthy’ products against HSR and NTSCG; (3) areas for optimisation. Food and beverage products sold between 05/2018 and 05/2019 in fifty-one remote stores were classified in each system. System alignment was assessed by cross-tabulating percentages of products, discretionary energy and added sugars sold assigned to the same healthiness levels across the systems. The system/s capturing the highest percentage of discretionary energy and added sugars sold in ‘unhealthy’ products and the lowest levels in ‘healthy’ products were considered the best performing. Cohen’s κ was used to assess agreement between the Thumbs system and the NTSCG for classifying products as healthy. The Thumbs system classified product healthiness in line with the HSR and NTSCG, with Cohen’s κ showing moderate agreement between the Thumbs system and the NTSCG (κ = 0·60). The Thumbs system captured the most discretionary energy sold (92·2 %) and added sugar sold (90·6 %) in unhealthy products and the least discretionary energy sold (0 %) in healthy products. Modifications to optimise the Thumbs system include aligning several food categories to the NTSCG criteria and addressing core/discretionary classification discrepancies of fruit juice/drinks. The Thumbs system offers a classification algorithm that could strengthen the HSR system.
Puerarin alleviates depressive-like behaviors in high-fat diet-induced diabetic mice via modulating hippocampal GLP-1R/BDNF/TrkB signaling

ABSTRACT Objective Depression is one of the most common complications in patients with diabetes. Our previous study demonstrated puerarin, a dietary isoflavone, improved glucose homeostasis and β-cell regeneration in high-fat diet (HFD)-induced diabetic mice. Here, we aim to evaluate the potential effect of puerarin on diabetes-induced depression. Methods The co-occurrence of diabetes and depression with related biochemical alterations were confirmed in HFD mice and db/db mice, respectively using behavioral analysis, ELISA and western blotting assay. Furthermore, impacts of puerarin on depression-related symptoms and pathological changes were investigated in HFD mice. Results The results showed that puerarin effectively alleviated the depression-like behaviors of HFD mice, down-regulated serum levels of corticosterone and IL-1β, while up-regulated the content of 5-hydroxytryptamine. Simultaneously, puerarin increased the number of hippocampal neurons in HFD mice, and suppressed the apoptosis of neurons to protect the hippocampal neuroplasticity. GLP-1R expression in hippocampus of HFD mice was enhanced by puerarin, which subsequently activated AMPK, CREB and BDNF/TrkB signaling to improve neuroplasticity. Importantly, our data indicated that puerarin had an advantage over fluoxetine or metformin in treating diabetes-induced depression. Conclusion Taken together, puerarin exerts anti-depressant-like effects on HFD diabetic mice, specifically by improving hippocampal neuroplasticity via GLP-1R/BDNF/TrkB signaling. Puerarin as a dietary supplement might be a potential candidate in intervention of diabetes with comorbid depression.
Knowledge-Based Representation Learning for Nucleus Instance Classification From Histopathological Images

The classification of nuclei in H&E-stained histopathological images is a fundamental step in the quantitative analysis of digital pathology. Most existing methods employ multi-class classification on the detected nucleus instances, while the annotation scale greatly limits their performance. Moreover, they often downplay the contextual information surrounding nucleus instances that is critical for classification. To explicitly provide contextual information to the classification model, we design a new structured input consisting of a content-rich image patch and a target instance mask. The image patch provides rich contextual information, while the target instance mask indicates the location of the instance to be classified and emphasizes its shape. Benefiting from our structured input format, we propose Structured Triplet for representation learning, a triplet learning framework on unlabelled nucleus instances with customized positive and negative sampling strategies. We pre-train a feature extraction model based on this framework with a large-scale unlabeled dataset, making it possible to train an effective classification model with limited annotated data. We also add two auxiliary branches, namely the attribute learning branch and the conventional self-supervised learning branch, to further improve its performance. As part of this work, we will release a new dataset of H&E-stained pathology images with nucleus instance masks, containing 20,187 patches of size 1024 $\times1024$ , where each patch comes from a different whole-slide image. The model pre-trained on this dataset with our framework significantly reduces the burden of extensive labeling. We show a substantial improvement in nucleus classification accuracy compared with the state-of-the-art methods.
On the Ultraviolet Divergence in QED

The well-known physical equivalence drawn from hole theory is applied in this article. The author suggests to replace, in the part of Feynman diagram which cannot be fixed by experiments, each fermion field operator, and hence fermion propagator, by pairs of equivalent fermion field operators and propagators. The formulation of this article thus yields additional terms which reveal characteristic effects that have not been explored previously; such characteristic effects lead to the appearence of logarithmic running terms and that finite radiative corrections are directly obtained in calculations.
State-level heterogeneity in associations between structural stigma and individual health care access: A multilevel analysis of transgender adults in the United States

Objective State-level variation in how restrictive policies affect health care access for transgender populations has not been widely studied. Therefore, we assessed the association between structural stigma and four measures of individual health care access among transgender people in the United States, and the extent to which structural stigma explains state-level variability. Methods Data were drawn from the 2015–2019 Behavioral Risk Factor Surveillance System and the Human Rights Campaign’s State Equality Index. We calculated weighted proportions and conducted multilevel logistic regression of individual heterogeneity and discriminatory accuracy. Results An increase in the structural stigma score by one standard deviation was associated with lower odds of health care coverage (OR = 0.80; 95% CI: 0.66, 0.96) after adjusting for individual-level confounders. Approximately 11% of the total variance for insurance coverage was attributable to the state level; however, only 18% of state-level variability was explained by structural stigma. Adding Medicaid expansion attenuated the structural stigma-insurance association and explained 22% of state-level variation in health insurance. For the remaining outcomes (usual source of care, routine medical check-up, and cost-related barriers), we found neither meaningful associations nor considerable between-state variability. Conclusions Our findings support the importance of Medicaid expansion and transgender-inclusive antidiscrimination protections to enhance health care insurance coverage. From a measurement perspective, however, additional research is needed to develop and validate measures of transgender-specific structural stigma to guide future policy interventions.