Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Grained Neural Network Abstraction for Efficient Formal Verification

The advance of deep learning makes it possible to empower safety-critical systems with intelligent capabilities. However, its intelligent component, i.e., deep neural network , is di ﬃ cult to formally verify due to the large scale and intrinsic complexity of the veriﬁcation problem. Abstraction has been proved to be an e ﬀ ective way of improving the scalability. A challenging problem in abstraction is that it is di ﬃ cult to achieve a balance between the size reduced and output overestimation caused by abstraction. In this work, we propose an e ﬀ ective ﬁne-grained approach to abstract neural networks. Our approach is ﬁne-grained in that we identify four cases that should be abstracted independently under a certain neuron prioritization strategy. This allows us to merge more neurons in networks and meanwhile maintain a relatively low output overestimation. Experimental results show that our approach outperforms other existing abstraction approaches by signiﬁcantly reducing the scale of target deep neural networks with small overestimation.
Stochastic aeroelastic analysis of laminated composite plate with variable fiber spacing

In this paper, the dynamic and aeroelastic analysis of variable fiber spacing composite (VFSC) laminated plates are carried out. The effects and benefits of changing the fiber distribution pattern on natural frequency and mode shape are explored taking various boundary conditions. Taking cantilever boundary condition flutter characteristics of VFSC plate with different fiber distribution patterns are compared. Further stochasticity of flutter velocity due to randomness in material properties that could arise due to complex manufacturing and fabrication process of VFSC laminates is investigated. The perturbation technique is implemented to perform the stochastic as well as reliability analysis. Various parametric studies are conducted to check the accuracy and efficiency of perturbation technique, by comparing the results with that of Monte Carlo simulation and the first-order reliability method.
Critical care staffing ratio and outcome of COVID-19 patients requiring intensive care unit admission during the first pandemic wave: a retrospective analysis across Switzerland from the RISC-19-ICU observational cohort.

STUDY AIM The surge of admissions due to severe COVID-19 increased the patients-to-critical care staffing ratio within the ICUs. We investigated whether the daily level of staffing was associated with an increased risk of ICU mortality (primary endpoint), length of stay (LOS), mechanical ventilation and the evolution of disease (secondary endpoints). METHODS We employed a retrospective multicentre analysis of the international Risk Stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry, limited to the period between March 1 and May 31, 2020, and to Switzerland. Hierarchical regression models were used to investigate crude and adjusted effects of the critical care staffing ratio on study endpoints. We adjusted for disease severity and weekly caseload. RESULTS Among the 38 participating Swiss ICUs, 17 recorded staffing information. The study population included 437 patients and 2,342 daily assessments of patient-to-critical care staffing ratio. Median of daily patient-to-nurse ratio started at 1.0 [IQR 0.5-1.5; calendar week 9] and peaked at 2.4 (IQR 0.4-2.0; calendar week 16), while the median of daily patient-to-physician ratio started at 4.0 (IQR 2.1-5.0; calendar week 9) and peaked at 6.8 (IQR 6.3-7.3; calendar week 19). Neither the patient-to-nurse (adjusted OR 1.28, 95% CI 0.85-1.93; doubling of ratio) nor the patient-to-physician ratio (adjusted OR 1.07, 95% CI 0.87-1.32; doubling of ratio) were associated with ICU mortality. We found no association of daily critical care staffing on the secondary endpoints in adjusted models. CONCLUSION We found no association of reduced availability of critical care staffing resources in Swiss ICUs with overall ICU length of stay nor mortality. Whether long-term outcome of critically ill patients with COVID-19 have been affected remains to be studied.
Effects of Prohexadione Calcium Applications on Growth and Yield Characteristics of Cucumber (Cucumis sativus L.)

This study was conducted to determine the effects of growth regulator prohexadione calcium (Pro-Ca) and inhibitor gibberellic acid on some parameters (yield, pigment levels of leaves, nutrient contents, proline, enzyme activities and fruit quality) when applied at different doses (0, 15, 30, 45, and 60 mg L-1). Chemicals were applied after transplanting seedlings. A total of 12 plots including 3 plants for each dose were used in randomised plot experimental design. Although it has a positive effect on leaf proline content and enzyme activities, 45, and 60 mg L-1 doses of Pro-Ca had negative effects on morphological parameters (excluding stem diameter), yield, leaf pigment (excluding chlorophyll a) and nutrient contents compared to control. High Pro-Ca doses generally affected the fruit quality properties positively while 15 mg L-1 of Pro-Ca did not affect other parameters, except decreasing fruit yield. A 30 mg L-1 of Pro-Ca dose was the most positive effective dose on the examined parameters. The highest vegetative development, yield, nutrient uptake and proline content were obtained from the plants subjected to 30 mg L-1 of Pro-Ca dose. Moreover, this dose increased fruit firmness, TSS, EC values and enzyme activities compared to control. Being an important quality criterion in cucumber storage after harvest, the weight loss rate 29.4% was lower in fruits obtained from the plant having 30 mg L-1 of Pro-Ca dose compared to that of the control plants. Therefore, 30 mg L-1 of Pro-Ca application, beside its positive effects on plant growth, yield and fruit quality, can be suggested for also increase shelf life of fruits in cucumber cultivation in greenhouse.
Comparative Study of SMART and FMCDM Methods in Smartphone Selection Decision Support System

: Smartphones are one of the communication technology tools currently used by both children and the elderly, so that interest in shopping for smartphones in Indonesia is increasing. The variety of smartphone brands makes buyers confused about which smartphone to buy. This research can help buyers to choose a smartphone to buy and help sellers provide recommendations. This study applies the Simple Multi Attribute Rating Technique (SMART) and Fuzzy Multi Criteria Decision Making (FMCDM) methods for the decision making process for smartphone selection. The purpose of this study is to apply and analyze the comparison of the SMART method and the FMCDM method in the Smartphone Selection Decision Support System. The study compared: the differences and similarities between the two methods, the results of the selection process for the two methods, and calculating the value of the sensitivity analysis of the selection results so that the best method could be determined. The criteria used: price, screen size, battery capacity, operating system, RAM, camera, and smartphone brand. The comparison results show that there are differences between the standard for determining the results, while the similarities in the calculation results, the smartphone recommended to buy is the same, namely the Asus Zenfone 2 Laser ZE500KG (16 GB) smartphone. Measurement of the accuracy of the results of the two methods uses sensitivity analysis values. It can be concluded that the better method is the FMCDM method because it has a smaller average sensitivity value than the SMART method, namely 0.2795 <0.3906.
Group Knowledge and Mathematical Collaboration: A Philosophical Examination of the Classification of Finite Simple Groups

Abstract In this paper we apply social epistemology to mathematical proofs and their role in mathematical knowledge. The most famous modern collaborative mathematical proof effort is the Classification of Finite Simple Groups. The history and sociology of this proof have been well-documented by Alma Steingart (2012), who highlights a number of surprising and unusual features of this collaborative endeavour that set it apart from smaller-scale pieces of mathematics. These features raise a number of interesting philosophical issues, but have received very little attention. In this paper, we will consider the philosophical tensions that Steingart uncovers, and use them to argue that the best account of the epistemic status of the Classification Theorem will be essentially and ineliminably social. This forms part of the broader argument that in order to understand mathematical proofs, we must appreciate their social aspects.
The Graphene Field Effect Transistor Modeling Based on an Optimized Ambipolar Virtual Source Model for DNA Detection

The graphene-based Field Effect Transistors (GFETs), due to their multi-parameter characteristics, are growing rapidly as an important detection component for the apt detection of disease biomarkers, such as DNA, in clinical diagnostics and biomedical research laboratories. In this paper, the non-equilibrium Green function (NEGF) is used to create a compact model of GFET in the ballistic regime as an important building block for DNA detection sensors. In the proposed method, the self-consistent solutions of two-dimensional Poisson’s equation and NEGF, using the nearest neighbor tight-binding approach on honeycomb lattice structure of graphene, are modeled as an efficient numerical method. Then, the eight parameters of the phenomenological ambipolar virtual source (AVS) circuit model are calibrated by a least-square curve-fitting routine optimization algorithm with NEGF transfer function data. At last, some parameters of AVS that are affected by induced charge and potential of DNA biomolecules are optimized by an experimental dataset. The new compact model response, with an acceptable computational complexity, shows a good agreement with experimental data in reaction with DNA and can effectively be used in the plan and investigation of GFET biosensors.
Advances in yeast autolysis technology - a faster and safer new bioprocess

Abstract The yeast autolysis process - an endogenous and irreversible lytic event, which occurs in cells caused by the action of intracellular enzymes, proteases and carbohydrases - is a well-known and an economic process, however, there is a constant risk of serious microbial contamination since there are many nutrients in the broth and this process is slow, favoring the growing of pathogens. The present work comes up with an attempt to accelerate the autolysis of Saccharomyces cerevisiae with focus on the high yield of yeast extract production through a fast, economic and simple technology. The proposed strategy is based on decreasing the pH of the yeast suspension at the beginning of autolysis through an acid shock to activate the cell autolytic system under stressful conditions of temperature and pH. The influence of cell concentration, temperature, time and acid shock at the beginning of the autolysis on yeast extract yields were studied. The best yields of proteins and total solids were observed for autolysis treated with acid shock (H2SO4 10 µL/g of dried yeast and final pH 4.4) at 60 °C (36, 84% of protein and 48, 47% of total solids extracted) and gradual increase of temperature 45 to 60 °C (41.20% of protein and 58.48% of total solids extracted). The shock could increase the speed of the process since the control reached about 30% of extract at 60 °C and the same experiment, however, with acid shock reached more than 43% in 12 h. When considering time in an industrial scale, it could be noted that the time was very important for the productivity as well as avoiding risk of pathogen contamination in autolysis. These results were very relevant for industrial purposes in the production of yeast extract, autolyzed yeast and glucan and mannan.
EPNS: Efficient Privacy-Preserving Intelligent Traffic Navigation From Multiparty Delegated Computation in Cloud-Assisted VANETs

Real-time navigation is an essential service in many applications, such as intelligent transportation and crowdsensing. There are, however, considerations (e.g., the potential of location privacy breach for vehicular users) in real-time navigation services. We observe that most existing state-of-the-art approaches utilize either pseudonyms or public key fully homomorphic encryption (FHE) to protect location privacy. The former (pseudonym-based approach) relies on an online certificate authority (CA) to update pseudonym certificates periodically, and FHE-based approaches incur significant computational/communication overheads. In this paper, a new cryptographic primitive, coined efficient multiparty delegated computation (MPDC), is proposed, where any one-way trapdoor permutation is required to perform in constant time (e.g., twice in our context) on each resource-constrained data provider for the encryption of (multiple) messages in a batch. We also remark that MPDC is designed to support secure evaluations (e.g., addition, multiplication, equality, and less than operations) over ciphertexts encrypted under multiple keys associated with different entities. Building on MPDC, we devise a lightweight privacy-preserving real-time intelligent traffic navigation scheme (EPNS) for cloud-assisted vehicular ad hoc networks (VANETs). The proposed approach facilitates the prediction of an optimal driving route, in terms of the shortest time from source to destination. In other words, EPNS securely evaluates the auto-regression moving average (ARMA) model with spatiotemporal correlations of high accuracy and efficiency. Therefore, neither vehicular users’ private location information nor the navigation result will be disclosed, even in the presence of colluding semi-trusted cloud server (or cryptography service provider, CSP) and unauthorized users. Finally, we prove the security and demonstrate the utility of both MPDC and EPNS.
Determinants of Household Poverty Status in Kediri City

Analyses of the causes and the characteristics of poverty at micro levels provide more efficient strategies for the attainment of main Sustainable Development Goals. This study aimed to analyze the extent to which the characteristics of individuals, households, and communities influence the probability of household poverty status. The 2019 Social Welfare Integrated Data and Village Potential Data of Kediri City were analyzed using an ordered logit regression model and then interpreted based on marginal effect calculation. The study found that household headsâ€™ squared-age, household membersâ€™ education, household membersâ€™ occupation, household head gender (female), ownership of assets, access to the internet, access to proper sanitation, and access to financial institutions reduced the probability of households being categorized as very poor and poor. This finding indicated that household productivity influenced by the household headâ€™s characteristics in managing productive assets, supported by access to infrastructure, could increase the household's welfare. However, the household headâ€™s age and marital status, dependency ratio, and access to health facilities increased householdâ€™s probability of being very poor and poor. Policies regarding poverty must be adjusted to the poverty characteristics and status. Improving access, equalizing education, and improving job opportunity and infrastructure management that ensure accessibility and enhancement in service quality need to be made to increase the status of households with the lowest 40% welfare in Kediri City. Policies regarding poverty should be focused more on social programs for very poor and poor households. Meanwhile, those near-poor and vulnerable-to-poor need more empowering programs.
Cytostatic treatment with irinotecan liposomal in a patient with advanced pancreatic adenocarcinoma

Pancreatic cancer is one of the most common malignant neoplasms with a short survival time and a low cure rate. This neoplasm progresses quickly, it is often diagnosed in the advanced stage, which means that systemic treatment regimens are not sufficiently effective. A case of 65-year-old patient with metastatic pancreatic cancer who underwent sequential chemotherapy with the use of liposomal irinotecan was presented.
How Do Basic Personality Traits Map Onto Moral Judgments of Fairness-Related Actions?

Reliance on fairness norms is a core feature of moral behavior and judgment, and is conceptually and empirically linked with basic personality dimensions. However, the specific nature of these links is poorly understood. In this study (N = 313, 68% female), we employed a novel third-party judgment paradigm, in which participants made moral judgments of various sharing actions of virtual others. This allowed us to capture individual variation in the relative importance of several fairness norms. We correlated these norm profiles with Big Five personality traits. We observed distinct associations between agreeableness, conscientiousness, openness, and extraversion and estimates of the importance of generosity, selfishness, relative generosity, and relative selfishness norms. Comparisons of these associations at the domain- versus facet-level of personality traits suggested these relations are specific to domain-level traits. These findings are an important step toward unraveling the complex links between fairness norms and basic personality traits.
A comparative analysis of staining propensity of SDF, SDF with potassium iodide and SDF with glutathione biomolecule on demineralized enamel: An in-vitro study

Aim: To evaluate and compare the staining propensity of SDF, SDF with Potassium iodide and SDF + Glutathione biomolecule on demineralized enamel. Study Design: Forty-five teeth were selected and stratified to receive either 38% SDF; 38% SDF + KI or 38% SDF + 20% GSH. The analysis for staining (  E) was done using a UV- Vis Spectrophotometer and was recorded at different intervals for one month. Data analysis was done using Kruskal- Wallis test, Post-hoc analysis (Dunn) and a repeat-measures analysis of variance (RM-ANOVA). Results: SDF caused maximum staining. SDF + KI staining was significantly less (P<0.05) compared to SDF at 1 week, 2 weeks and one month. SDF + GSH showed least staining at 5 minutes and 24 hours among the three groups but the staining increased gradually and had no statistical difference after 1 week, 2 weeks and one month when compared to other groups. Conclusion: Application of KI following SDF helps in reducing the staining. Addition of GSH had no significant [2]
Impacts of the Affordable Care Act Medicaid Expansion in California

Author(s): Schwehr, Natalie; Alarcon, Giovann; Hartman, Lacey | Abstract: We examined the impact of the Affordable Care Act (ACA) on healthcare coverage, access, health status, and affordability, as well as disparities in these outcomes by race/ethnicity among low-income Californians. We used nationally representative survey data from the Behavioral Risk Factor Surveillance Survey 2011-2019 and a difference-in-differences approach that compared California with nonexpansion states. We examined the impact of Medicaid expansion on health insurance coverage, having a usual source of care, self-reported health status, frequent (≥14) unhealthy days in the past month (physical, mental, and both), and foregone care due to cost. The sample population included low-income Californians (l100% of the federal poverty guidelines) aged 19-64 and low-income childless adults. Low-income adults, childless adults, and white childless adults in California saw post-ACA gains in six of seven outcomes, including a 7.7 percentage point increase in having a usual source of care for all low-income adults (CI: 0.051 to 0.104). Childless adult people of color (POC) reported significant improvements in three measures, with a 6.6 percentage point increase in having a usual source of care (CI: 0.013 to 0.120). All of the groups we examined had coverage gains, ranging from 3.9 percentage points for all low-income adults (CI: 0.013 0.066) to 8.4 percentage points for white childless adults (CI: 0.025 to 0.143). Additionally, all groups reported improved mental health, including an 8.2 percentage point decrease in frequent mental distress for childless adults (CI: -0.120 to -0.044). These findings indicate that the ACA coverage expansion benefitted the targeted population of low-income Californians. Additionally, the disparity between white and non-white Californians decreased for the unadjusted mean rate of having a usual source of care. However, unadjusted means showed that white low-income adults remained more likely to have health insurance coverage and a usual source of care compared with POC in both California and nonexpansion states.
The Gender-Equality Paradox and Optimal Distinctiveness: More Gender-Equal Societies Have More Gendered Names

Findings in several domains have documented a gender-equality paradox, where greater social and economic gender equality predicts increased gender differentiation. Many of these findings have used subjective rating scales and thus have been dismissed as artifactual due to different reference groups in more versus less gender-equal societies. Although recent research has documented the gender-equality paradox using an objective criterion—pursuit of degrees in STEM—the robustness of this finding has also been challenged. The current investigation offers evidence for the gender-equality paradox using an objective marker of gender differentiation: baby names. We find given names are more phonetically gendered in more gender-equal societies, with female names being more likely unvoiced (a softer sound) and male names being more likely voiced (a harder sound). We offer a theoretical explanation based on optimal distinctiveness theory to explain why increasing gender equality might motivate a preference for greater gender differentiation.
Evaluation of Nursing Care Satisfaction of Patients Who Underwent Open Heart Surgery

Aim This study aimed to determine the nursing services satisfaction level of patients who underwent open heart surgery. Method One hundred forty patients who were hospitalized for at least 2 days between the dates 20 November 2013 and 20 January 2014 in the cardiovascular clinics of three different public hospitals were included in this descriptive research. To collect data, a survey of 13 questions about individuals’ sociodemographic and informative features and the Newcastle Satisfaction with Nursing Scale that consists of 19 clauses was used. The data were analyzed using the statistical tests of number, percentage, average, standard deviation, minimum, maximum, Mann–Whitney U-test for two groups, Kruskal–Wallis for more than two groups, chi-square and chi-square trend for comparison of categorical variables. Results The nursing care satisfaction level of patients who underwent an open heart surgery was determined to be high at the rate of 77.77 ± 15.93. Satisfaction status of the patients included in the study was comparatively analyzed with age, sex, marital status, level of income, health insurance, level of education, employment status, number of hospitalization, and length of hospitalization. Results indicate that variables did not have any effect on satisfaction scores. Conclusion The nursing services satisfaction level of patients who underwent open heart surgery was determined to be high.