Miracle-dz/UniFAR-FTUs huggingface dataset:
OceanSODA-ETHZ: a global gridded data set of the surface ocean carbonate system for seasonal to decadal studies of ocean acidification

Abstract. Ocean acidification has profoundly altered the ocean's carbonate chemistry since preindustrial times, with potentially serious consequences for marine life. Yet, no long-term, global observation-based data set exists that allows us to study changes in ocean acidification for all carbonate system parameters over the last few decades. Here, we fill this gap and present a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO2 (pCO2), pH, and the saturation state with respect to mineral CaCO3 (Ω) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1∘×1∘. This data set, named OceanSODA-ETHZ, was created by extrapolating in time and space the surface ocean observations of pCO2 (from the Surface Ocean CO2 Atlas, SOCAT) and total alkalinity (TA; from the Global Ocean Data Analysis Project, GLODAP) using the newly developed Geospatial Random Cluster Ensemble Regression (GRaCER) method (code available at https://doi.org/10.5281/zenodo.4455354, Gregor, 2021). This method is based on a two-step (cluster-regression) approach but extends it by considering an ensemble of such cluster regressions, leading to improved robustness. Surface ocean DIC, pH, and Ω were then computed from the globally mapped pCO2 and TA using the thermodynamic equations of the carbonate system. For the open ocean, the cluster-regression method estimates pCO2 and TA with global near-zero biases and root mean squared errors of 12 µatm and 13 µmol kg−1, respectively. Taking into account also the measurement and representation errors, the total uncertainty increases to 14 µatm and 21 µmol kg−1, respectively. We assess the fidelity of the computed parameters by comparing them to direct observations from GLODAP, finding surface ocean pH and DIC global biases of near zero, as well as root mean squared errors of 0.023 and 16 µmol kg−1, respectively. These uncertainties are very comparable to those expected by propagating the total uncertainty from pCO2 and TA through the thermodynamic computations, indicating a robust and conservative assessment of the uncertainties. We illustrate the potential of this new data set by analyzing the climatological mean seasonal cycles of the different parameters of the surface ocean carbonate system, highlighting their commonalities and differences. Further, this data set provides a novel constraint on the global- and basin-scale trends in ocean acidification for all parameters. Concretely, we find for the period 1990 through 2018 global mean trends of 8.6 ± 0.1 µmol kg−1 per decade for DIC, −0.016 ± 0.000 per decade for pH, 16.5 ± 0.1 µatm per decade for pCO2, and −0.07 ± 0.00 per decade for Ω. The OceanSODA-ETHZ data can be downloaded from https://doi.org/10.25921/m5wx-ja34 (Gregor and Gruber, 2020).
An epidemiological model for predicting students’ mathematics anxiety

Abstract Mathematics anxiety is one of the influential factors on students’ mathematical performance as a negative relationship between students’ mathematics anxiety and mathematical performances has been frequently reported. Predicting students’ mathematics anxiety can help educators to have a better understanding of students, and potentially could help them to know if specific interventions are needed to design to control students’ anxiety level. Epidemiology is the study of the distribution of diseases and other health matters. Recently, epidemiological modeling has been widely used as a tool to predict future behaviors in social sciences, economics, and disease progression. In the present study, an epidemiological model is designed to predict undergraduate students’ mathematics anxiety level. The findings show that a high percentage of students, approximately 70%, will suffer from high mathematics anxiety in near future.
Comparison of a Barium Chloride Test with ELISA for Pregnancy Detection in Cows

Abstract Introduction Early detection of pregnancy is vital for appropriate reproductive management programmes to facilitate the rapid re-insemination of non-pregnant females and reduce the calving interval. Material and Methods A barium chloride test was compared with a commercial progesterone ELISA to detect pregnancy in non-descriptive cows and investigate if it could be applied as an alternative to ELISA in the field. Blood and urine samples were collected from 74 cows with recorded insemination dates. The progesterone ELISA and barium chloride assay were implemented to detect progesterone (P4) in blood and urine specimens, respectively. The cows' reproductive systems were examined after they were slaughtered to determine the uterus's status. Macroscopic examination of the uterus was used as a reference standard for both tests. Results The sensitivity rates of the P4 ELISA and barium chloride test to detect pregnant cows were 100.0% and 79.4%, and to detect the corpus luteum (CL) were 83.0% and 87.0%, respectively, their sensitivity increasing in the presence of the CL. The ELISA and barium chloride tests were 79.7% and 52.7% accurate in the diagnosis of pregnancy. The accuracy of the barium chloride test in CL detection increased to 81.0%, and that of the ELISA to 86.4%. There were no significant differences (P = 0.052) between the barium chloride assay and ELISA when they were utilised for the identification of the CL. Conclusion The barium chloride test can be an inexpensive and time-saving alternative to ELISA in pregnancy diagnosis when the insemination date is known.
“I Live in Constant Fear of Highs,” the Daily Impact of Type 1 Diabetes

Limited research exists regarding the burdens associated with type 1 diabetes (T1D). The study’s objective was to understand the impact of T1D from people with T1D and caregivers of minors with T1D. Six focus groups were conducted, with a total of 31 participants. Participants included people with T1D, ages 23 to 72 (n = 17) and caregivers ages 34 to 55 (n = 14). Participants were recruited from T1D Exchange Glu. People with T1D reported time spent managing diabetes had greatest impact, while caregivers reported financial and employment sacrifices as most impactful. Our findings provide insight into the real-world daily impact of diabetes.
Early fungicide treatment reduces blackleg on canola but yield benefit is realized only on susceptible cultivars under high disease pressure

Abstract Blackleg [Leptosphaeria maculans (Desm.) Ces. & de Not. ] is the most widespread disease of canola (Brassica napus L.) on the Canadian prairies. It has a noticeably increased incidence in recent years possibly due to shifts in the pathogen population and erosion in cultivar resistance. This study was conducted to assess foliar-applied fungicides for mitigating the risk of blackleg. Field trials were conducted at five locations in the Black or Dark-Brown Soil Zones for four years using the susceptible canola ‘Westar’. The fungicides pyraclostrobin (Headline®), azoxystrobin (Quadris®), propiconazole (Tilt®), and azoxystrobin + propiconazole (Quilt®) were applied at the 2–4 leaf stage against early infection. For comparisons, pyraclostrobin was also applied at bolting of ‘Westar’ and at the 2–4 leaf stage of two resistant cultivars (‘43E01’, ‘45H29’). These early treatments, except propiconazole, significantly reduced the mean disease incidence (MDI) and disease severity index (DSI) on ‘Westar’, relative to untreated control, reducing the impact of disease on yield by 16.5–26.9%. Late application of pyraclostrobin at bolting was ineffective. Two-application treatments, with pyraclostrobin at the 2–4 leaf stage and propiconazole at the bolting stage, or vice versa, provided no further efficacy or yield benefit relative to the early application of pyraclostrobin alone. None of the treatments showed substantial disease reduction or yield benefit in two low-disease station years (MDI <30%). On the resistant cultivars, pyraclostrobin reduced MDI and DSI, but showed no yield benefit. As resistant canola cultivars are used commonly on the prairies, a routine application of foliar fungicide is not recommended.
Pan-cancer mutational landscape of the PPAR pathway reveals universal patterns of dysregulated metabolism and interactions with tumor immunity and hypoxia

Peroxisome proliferator-activated receptors (PPARs) are a family of nuclear receptors that regulate lipid metabolism and bioenergetic demands within living systems. Consequently, aberrant expression of PPAR genes could predispose individuals to diseases including cancer. PPAR signaling exerts pleiotropic functions in cancer, yet, little is known about the interactions between genetic and transcriptional events of pathway genes in a pan-cancer context. Employing multidimensional datasets of over 18,000 patients involving 21 cancers, we performed systematic characterization on copy number alteration and differential transcript expression of 74 PPAR pathway genes. We identified 18 putative driver candidates demonstrating mutually exclusive patterns of loss- and gain-of-function phenotypes. These driver genes successfully predicted patient survival rates in bladder, renal, glioma, liver and stomach/esophageal cancers. Dysregulated PPAR signaling in these cancers converged on common downstream pathways associated with multiple metabolic processes. Moreover, clinically-relevant relationships between PPARs and hypoxia were observed where hypoxia further aggravates disease phenotypes in tumor subtypes with aberrant PPAR signaling. In glioma samples, including astrocytoma and oligoastrocytoma, PPAR hyperactivation is associated with immunosuppression through increased regulatory T cell expression. Our analysis reveals underappreciated levels of diversity and conservation in PPAR genes that could lay the groundwork for therapeutic strategies targeting tumor metabolism, immunity and hypoxia.
An Innovative Approach for Improving the Accuracy of Digital Elevation Models for Cultivated Land

The redistribution of solar radiation, temperature, soil moisture and heat by topography affects the physical and chemical properties of the soil and the spatial distribution characteristics of crop growth. Analyses of the relationship between topography and these variables may help to improve the accuracy of digital elevation models (DEMs). The purpose of correcting Shuttle Radar Topography Mission (SRTM) data is to obtain high-precision DEM data in cultivated land. A typical black soil area was studied. A high-precision reference DEM was generated from an unmanned aerial vehicle (UAV) and extensive measured ground elevation data. The normalized differential vegetation index (NDVI), perpendicular drought index (PDI) extracted from SPOT-6 remote sensing images and potential solar radiation (PSR) extracted from SRTM. The interactions between topography and NDVI, PDI, and PSR were analyzed. The NDVI, PDI and PSR in June, July, August and September of 2016 and the SRTM were used as independent variables, and the UAV DEM was used as the dependent variable. Linear stepwise regression (LSR) and a back-propagation neural network (BPNN) were used to establish an elevation prediction model. The results indicated that (1) The correlation between topography and NDVI, PSR, PDI was significant at 0.01 level. The PDI and PSR improved the spatial resolution of SRTM data and reduce the vertical error. (2) The BPNN (R1 = 0.98, root mean square error, RMSE1 = 0.54) yielded a higher SRTM accuracy than did the studied linear model (RMSE1 = 1.00, R1 = 0.90). (3) A series of significant improvements in the SRTM were observed when assessed with the reference DEMs for two different areas, with RMSE reductions of 91% (from 14.95 m to 1.23 m) and 93% (from 15.6 m to 0.94 m). The proposed method improved the accuracy of existing DEMs and could provide support for accurate farmland management.
Morphological and ontogenetic characteristics of Demodex plecoti sp. nov. (Acariformes: Demodecidae) from the brown long-eared bat Plecotus auritus (Chiroptera: Vespertilionidae), with comments on parasitism

Abstract A new species, Demodex plecoti sp. nov. (Acariformes: Demodecidae), is described based on adult and immature stages from the brown long-eared bat Plecotus auritus (Linnaeus, 1758) in Poland. The mites were found in 100% of the examined bat individuals, with a mean intensity of 23.5 parasites per host. Demodex plecoti sp. nov. was observed only in the hairless body regions, were the skin was membranous, thin and delicate.
STUDY ON THE COUPLING RELATIONSHIP BETWEEN ECONOMIC SYSTEM AND WATER ENVIRONMENTAL SYSTEM IN BEIJING BASED ON STRUCTURAL EQUATION MODEL

To study the relationship between the economic system and the water environmental system in Beijing, China, this article brought in the concept of “system coupling”, and calculated their coupling degree using the co-coordination model. Specifically, the economic system consists of population and economic development subsystems; the water environmental system consists of water supply and water environmental carrying capacity subsystems. A total of twelve components were chosen, including population, water consumption per ten thousand Yuan GDP, etc. Besides, the Structural Equation Model (SEM) was innovatively used to analyze the micro-coupling paths among the subsystems. Finally, we drew conclusions from the coupling state perspective and coupling paths perspective, respectively. From the macroscopic point of view, there are complex interactions between the economic system and water environmental system in Beijing, and their coupling degree will increase with the optimization of economic structure and advancement in technologies. From the microscopic point of view, there are also complex interactions among the four subsystems in Beijing. Especially, the water supply subsystem has a certain degree of promotion effect on the other three subsystems.
Robust stability analysis for fuzzy stochastic Hopfield neural networks with time–varying delays

This paper investigates delay-dependent robust stability problem of fuzzy stochastic Hop(cid:133)eld neural networks with random time-varying delays. Moreover, in this paper, the stochastic delay is assumed to satisfy a certain probability distribution. By introducing a stochastic variable with Bernoulli distribution, the neural networks with random time delays is trans- formed into one with deterministic delays and stochastic parameters. Based on a Lyapunov-Krasovskii functional and stochastic analysis approach, delay- probability-distribution-dependent stability criteria have been derived in terms of linear matrix inequalities (LMIs), which can be checked easily by the LMI control toolbox. Finally two numerical examples are given to illustrate the e⁄ectiveness of the theoretical results. 92B20, 37H30, 15A39, 93C43. time-varying delays
Collaborative Governance in the Age of Managed Behavioral Health Care

Objective: Collaborative governance engages public and private stakeholders in shared decision-making. In this article, we investigate New York State’s Regional Planning Consortiums (RPCs), a collaborative governance model designed to monitor the transition of the state’s public behavioral health system to Medicaid managed care. We use an integrative framework to examine how officials believed collaborative governance would address policy challenges in the system context. By distinguishing between features of the state’s program that were consistent with and different from past studies, we identify how collaborative governance is evolving to address perceived threats to democratic institutions and how it might indirectly contribute to their unmooring. Method: We conducted semistructured interviews with 30 state- and county-level behavioral health officials and quasi-governmental personnel. Data were analyzed using grounded theory methods. Results: Informants identified the drivers of the RPCs, which included challenges related to privatization of service administration and centralization of government. They said they expected the collaborative governance regime to attenuate these hazards by involving affected stakeholder groups in shared decision-making, placing local officials in leadership positions, and developing a regional monitoring system to track adverse impacts. Informants also said they anticipated that collaboration dynamics would improve behavioral health policymaking and enhance the effectiveness of services. Some informants were reluctant to fully embrace the aspirations of collaborative governance, believing it would indirectly contribute to a reduced role for government in public administration and undermine democratic norms, especially at the local level. Conclusions: This study describes how collaborative governance is changing under advanced liberalism. It highlights opportunities and limitations of collaborative governance that are important for public policymakers and scholars of social work, social welfare, and behavioral health to understand.
Macroscopic Modeling of A3B15A3 Triblock Copolymers in B Solvent

Abstract : We have performed dynamic density functional theory calculations for an ABA triblock copolymer immersed in a B-attractive solvent. The triblock copolymer was parameterized for poly[styrene-b-(ethylene-co-butylene)-b-styrene] (SEBS), while the solvent was parameterized for the hydrocarbon oil tetradecane (C14). The effect of the solvent concentration and SEB interaction on the morphology was studied. For a 50/50 vol.% mixture of polymer/solvent, we also considered the effect of solvent molecular weight on the morphology, where we observed a transition between micro- and macro-phase separation at a critical molecular weight. Lastly, elastic properties were calculated using a finite-element based mechanical homogenization method.
CSR-Net: Camera Spectral Response Network for Dimensionality Reduction and Classification in Hyperspectral Imagery

Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images.
Reimagining Business Planning, Accessibility, and Web Design Instruction: A Stacked Interdisciplinary Collaboration Across National Boundaries

The authors present the results of a study of a three-way international collaboration project among one Hungarian class and two classes from Michigan and Washington, respectively. This multifaceted study focused on business planning, web design, and accessibility with the aim of investigating the effect of accessibility instruction on the production of business plans and websites. The distinguishing feature of this study was its emphasis to orient the three student groups on disability and accessibility issues from the perspective of the critical social model of disability advanced by disability studies theorists. The researchers collected quantitative and qualitative pre/postproject survey data from their three classes. They combined this data with the text of student emails sent among the project teams and instructor notes about their teaching to arrive at conclusions about the effectiveness of the collaboration using a mixed-methods approach. The results from the data analyses revealed significant benefit of the client–provider relationships among the three classes and the accessibility instruction provided by the Washington class to the other two classes on the business plans and websites.
Antidiabetic effect of bitter melon/Kerala (Momordica charantia) in alloxan induced diabetic rat

This study aims at investigating the effect of Momordica charantia extract on glucose tolerance and some biochemical parameters in alloxan induced diabetes rat. A total of 150 rats (50 normal rats and 100 alloxan induced diabetic rats) were used for five trials. The rats were divided into three groups for each trial, each containing 10 individuals as follows: Group A: is the control group, Group B as diabetic control group and group C were diabetic rat received bitter melon (Momordica charantia). Then alloxan injection was injected at a dose rate of 150 mg/kg body weight through intra-peritoneal route to each rat to induce diabetes in groups B and C. Aqueous extract of bitter melon (Momordica charantia) were fed by gavage at a dose of 300 mg/kg body weight daily for 21 days in group C. On 15th day blood glucose level and the body weights, biochemical parameters were measured for the first time to ensure diabetic induction. Then all the rats of this group were kept for more 21 days for the treatment of diabetic condition. Alloxan produced a significant increase in serum glucose, triglyceride (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), Alanine aminotransaminase (ALT) and Aspartate aminotransaminase (AST) and significant decrease in body weight and high density lipoprotein cholesterol (HDLC). Bitter melon treatment reduced non-fasting and fasting glucose level (p<0.05). In addition, administration of bitter melon juice was associated with a reduction in the serum levels of Alanine aminotransaminase (ALT) and Aspartate aminotransaminase (AST), Triglyceride (TG), Total Cholesterol (TC) and Low Density Lipoprotein Cholesterol (LDL-C) when compared with positive diabetic control (p<0.05). High density lipoprotein cholesterol (HDLC) and body weight level significantly increased in bitter melon treated group C compare to Group B (p<0.05). Res. Agric. , Livest. Fish.5(3): 373-379, December 2018
A novel distance measure for intuitionistic fuzzy sets with diverse applications

Distance function is a canonical quantitative tool to measure the similarity or difference between two intuitionistic fuzzy sets (IFSs). In spite of having a vast range of such tools, one fails to distinguish accurately the IFSs with higher hesitancy. In view of that, we introduce a distance function for IFSs and validate the axiomatic definition of it. Boundedness and nonlinear characteristics of the proposed distance measure are corroborated. Moreover, its efficacy is not restricted to highly uncertain IFSs only, but it is equally effective in other cases, which is testified via some numerical examples. We establish the veracity of the prescribed distance by comparing it with reported results through some illustrative examples. In light of recent COVID‐19 pandemic, selection of a proper antivirus face mask has become a daunting task, which is addressed via the introduced distance measure as a multi‐attribute decision‐making problem. We further extend the applicability of the proposed distance in the areas of pattern recognition and medical diagnosis.