Set Of Values Research Articles

Incorporating diverse values of nature in decision-making-theory and practice.

Values play a significant role in decision-making, especially regarding nature. Decisions impact people and nature in complex ways and understanding which values are prioritised, and which are left out is an important task for improving the equity and effectiveness of decision-making. Based on work done for the IPBES Values Assessment, this paper develops a framework to support analyses of how decision-making influences nature as well as whose values get prioritised. The framework is used to analyse key areas of environmental policy: a) the present model for nature protection in market economies, b) the role of valuation in bringing nature values into decisions, and c) values embedded in environmental policy instruments, exemplified by protected areas for nature conservation and payments for ecosystem services. The analyses show that environmental policies have been established as mere additions to decision-making structures that foster economic expansion, which undermines a wide range of nature's values. Moreover, environmental policies themselves are also focused on a limited set of nature's diverse values. This article is part of the theme issue 'Bringing nature into decision-making'.

Population-level norm values by EQ-5D-3L in Hungary - a comparison of survey results from 2022 with those from 2000.

Although population norms of the EQ-5D-3L instrument had been available in Hungary since 2000, their evaluation was based on a United Kingdom (UK) value set. Our objective was to estimate the population norms for EQ-5D-3L by using the new Hungarian value set available since 2020, to extend the scope to adolescents, and to compare with norms from 2000. A cross sectional EQ-5D-3L survey representative of the Hungarian population was conducted in 2022. The EQ-5D-3L dimensional responses were analyzed by age and sex and compared with the survey from 2000, by estimating population frequencies with their 95% confidence intervals; index values were evaluated by both value sets. Altogether, 11,910 respondents, aged 12 or more (578 between 12 and 17), completed the EQ-5D-3L. There was a notable improvement in reporting problems for both sexes (age 35-64) regarding the pain/discomfort and anxiety/depression compared to 2000. Below the age 44, both sexes had an EQ-5D-3L index plateau of 0.98, while above the age 55, men tended to have numerically higher index values compared to women, with the difference increasing with older age. Improvement in dimensional responses were also translated to numerically higher index values for both sexes between ages 18 and 74 compared to 2000. Multivariate regression analysis showed that higher educational attainment, lower age, larger household size, and active occupational status were associated with higher index values. Over the past 22 years, there was a large improvement in HRQoL of the middle-aged to elderly men and women in Hungary.

Precisely Extracting Complex Variable Values from Android Apps

Millions of users nowadays rely on their smartphones to process sensitive data through apps from various vendors and sources. Therefore, it is vital to assess these apps for security vulnerabilities and privacy violations. Information such as to which server an app connects through which protocol, and which algorithm it applies for encryption, are usually encoded as variable values and arguments of API calls. However, extracting these values from an app is not trivial. The source code of an app is usually not available, and manual reverse engineering is cumbersome with binary sizes in the tens of megabytes. Current automated tools, however, cannot retrieve values that are computed at runtime through complex transformations. In this article, we present ValDroid , a novel static analysis tool for automatically extracting the set of possible values for a given variable at a given statement in the Dalvik byte code of an Android app. We evaluate ValDroid against existing approaches (JSA, Violist, DroidRA, Harvester, BlueSeal, StringHound, IC3, and COAL) on benchmarks and 794 real-world apps. ValDroid greatly outperforms existing tools. It provides an average F 1 score of more than 90%, while only requiring 0.1 s per value on average. For many data types including Network Connections and Dynamic Code Loading, its recall is more than twice the recall of the best existing approaches.

A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images from low-dose Cone Beam CT (CBCT) acquired on a modern linear accelerator integrating AI. Methods: A total of 53 patients treated in the pelvic region were enrolled and split into training (30), validation (9), and testing (14). A Generative Adversarial Network (GAN) was trained for 200 epochs. The image accuracy was evaluated by calculating the mean and mean absolute error (ME and ME) between sCT and CT. RT treatment plans were calculated on CT and sCT images, and dose accuracy was evaluated considering Dose Volume Histogram (DVH) and gamma analysis. Results: A total of 4507 images were selected for training. The MAE and ME values in the test set were 36 ± 6 HU and 7 ± 6 HU, respectively. Mean gamma passing rates for 1%/1 mm, 2%/2 mm, and 3%/3 mm tolerance criteria were respectively 93.5 ± 3.4%, 98.0 ± 1.3%, and 99.2 ± 0.7%, with no difference between curative and palliative cases. All the DVH parameters analysed were within 1 Gy of the difference between sCT and CT. Conclusion: This study demonstrated that sCT generation using the DL approach is feasible on low-dose CBCT images. The proposed approach can represent a valid tool to speed up the online adaptive procedure and remove CT simulation from the RT workflow.

Time From Hospital Arrival Until Endovascular Thrombectomy and Patient-Reported Outcomes in Acute Ischemic Stroke

The time-benefit association of endovascular thrombectomy (EVT) in ischemic stroke with patient-reported outcomes is unknown. To assess the time-dependent association of EVT with self-reported quality of life in patients with acute ischemic stroke. Data were used from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial, which tested the effect of nerinetide on functional outcomes in patients with large vessel occlusion undergoing EVT and enrolled patients from March 1, 2017, to August 12, 2019. The ESCAPE-NA1 trial was an international randomized clinical trial that recruited patients from 7 countries. Patients with EuroQol 5-dimension 5-level (EQ-5D-5L) index values at 90 days and survivors with complete domain scores were included in the current study. Data were analyzed from July to September 2023. Hospital arrival to arterial puncture time and other time metrics. EQ-5D-5L index scores were calculated at 90 days using country-specific value sets. The association between time from hospital arrival to EVT arterial-access (door-to-puncture) and EQ-5D-5L index score, quality-adjusted life years, and visual analog scale (EQ-VAS) were evaluated using quantile regression, adjusting for age, sex, stroke severity, stroke imaging, wake-up stroke, alteplase, and nerinetide treatment and accounting for clustering by site. Using logistic regression, the association between door-to-puncture time and reporting no or slight symptoms (compared with moderate, severe, or extreme problems) was determined in each domain (mobility, self-care, usual activities, pain or discomfort, and anxiety or depression) or across all domains. Time from stroke onset was also evaluated, and missing data were imputed in sensitivity analyses. Among 1105 patients in the ESCAPE-NA1 trial, there were 1043 patients with EQ-5D-5L index values at 90 days, among whom 147 had died and were given a score of 0, and 1039 patients (mean [SD] age, 69.0 [13.7] years; 527 male [50.7%]) in the final analysis as 4 did not receive EVT. There were 896 survivors with complete domain scores at 90 days. There was a strong association between door-to-puncture time and EQ-5D-5L index score (increase of 0.03; 95% CI, 0.02-0.04 per 15 minutes of earlier treatment), quality-adjusted life years (increase of 0.29; 95% CI, 0.08-0.49 per 15 minutes of earlier treatment), and EQ-VAS (increase of 1.65; 95% CI, 0.56-2.72 per 15 minutes of earlier treatment). Each 15 minutes of faster door-to-puncture time was associated with higher probability of no or slight problems in each of 5 domains and all domains concurrently (range from 1.86%; 95% CI, 1.14-2.58 for pain or discomfort to 3.55%; 95% CI, 2.06-5.04 for all domains concurrently). Door-to-puncture time less than 60 minutes was associated higher odds of no or slight problems in each domain, ranging from odds ratios of 1.49 (95% CI, 1.13-1.95) for pain or discomfort to 2.59 (95% CI, 1.83-3.68) for mobility, with numbers needed to treat ranging from 7 to 17. Results were similar after multiple imputation of missing data and attenuated when evaluating time from stroke onset. Results suggest that faster door-to-puncture EVT time was strongly associated with better health-related quality of life across all domains. These results support the beneficial impact of door-to-treatment speed on patient-reported outcomes and should encourage efforts to improve patient-centered care in acute stroke by optimizing in-hospital processes and workflows.

Values and Ethics Domain for Professional Identity in Nursing.

A crucial domain of professional identity in nursing is the area of values and ethics. Comprising a set of core values and principles, values and ethics are used to guide nurse conduct. Professional development specialists can use the nursing code of ethics and organizational structure and examples to teach nurses the values and ethics domain of professional identity in nursing. [J Contin Educ Nurs. 2024;55(6):279-281.].

How to Mitigate Community Harms of Antibacterial Resistance With Patient-Centered Care.

Overprescription of antibiotics in cases in which bacterial infection is clinically uncertain contributes to increased prevalence of multidrug-resistant bacteria. Ethically, merits and drawbacks of stricter prescription practice oversight should be weighed against risks of untreatable bacterial infections to patients and communities. This article considers how to balance this set of ideas and values.

A Prerequisite for need of Harmonized Nutrient Reference Values & its Standardized Terminology for Better access of Nutritional Supplements Globally

Abstract: To reduce the risk of insufficient nutrient intake, nutrient reference values for humans are set at the average nutrient need level without a safety margin. The vast majority of nations and areas in the globe set nutritional intake guidelines for their citizens. Currently, no harmonized nutrient reference value has been published, as well as the development of standards cannot be achieved through the use of formalized frameworks or terminology. The study's objective was to create recommendations for appropriate and secure micronutrient intake ranges that could be used to inform dietary advice. As relevant to the study, we examined micronutrient data from food intake surveys from representative nations. The comparison of national standards illustrates that the level of reference values for nutrient for individual countries is dependent on variables such as methodology, data sources, physical activity exemptions, standard body weights, and age range. In the current study, iron, calcium, magnesium, vitamin A, B12, D and folate were identified as the most commonly under-consumed micronutrients. In this article, the level of six countries' standards are compared to the current reference values for nutrients. Further, numerous words for different dietary standards components are discussed, and a final set of harmonized reference nutrient intake for the dietary advice is provided.

Fire behaviour of liquid solvents for energy storage applications

The introduction of innovative solutions for the energy supply chain is promoting the use of batteries and supercapacitors, posing new challenges from a safety point of view. This work presents bench-scale experiments devoted to the evaluation of the fire behaviour of liquid solvents, employing an in-house procedure for the characterization of the ignition and flame behaviour of liquids either in the presence or absence of ignition sources. Different boundary conditions were tested to identify the most suitable set of values for a standardized assessment of safety parameters for acetonitrile, ethyl acetate, lactic acid, and hexane. The produced vapours and the exhaust gas were analysed together with the evolution in time of the rate of heat generation. The fire behaviour was quantified in terms of time to reach the maximum value of the heat release rate and the peak. The former is related to the flash point, whereas the latter is determined by the thermodynamics and kinetics of the investigated species. In addition, the overall rate and the ruling regimes (e.g., smouldering or flaming) were determined based on the collected data. The comparison between the obtained values and the typical velocities attributed to vaporization, mixing, and gas-phase reactions suggests that vaporization can be considered as the rate-determining step only in the case of flaming.

Spectral norm and energy of a digraph with respect to a VDB topological index

The set of singular values of a digraph with respect to a vertex-degree based topological index is the set of all singular values of its general adjacency matrix. The spectral norm is the largest singular value and the energy the sum of the singular values. In this paper we characterize the digraphs which have exactly one singular value different from zero and the digraphs for which all singular values are equal. As a consequence, we deduce sharp upper and lower bounds for the spectral norm and energy of digraphs. In addition to being a natural generalization, proving the results in the general setting of digraphs allows us to deduce new results on graph energy.

CMRight: Chinese Morph Resolution based on end-to-end model combined with enhancement algorithms

As the primary means of daily communication, SMS and instant messaging have the advantage of being inexpensive, but as a result, they have received the attention of practitioners in the underground industry. By using morph to bypass the regulation of platforms that mainly rely on rules, they disseminate information that draws traffic for industries such as pornography and gambling. To alleviate the above problems, existing researchers try to use the Chinese spelling error correction model, but the effect could be better. To this end, we manually analyze 11,358 deformed texts in real scenarios and identify four prevalent morph types in the field. Further, we propose a machine translation model-based algorithm for morph resolution, which achieves more than 96.2% BLEU and F1 values in the validation set. We also utilize the alignment and enhancement algorithms to improve the model performance by nearly 2.3% to 98.6%. The above results show that our proposed method can effectively identify the connection between the lexeme and the target word, thus resolving morphs and providing more efficient data support for traceability and information mining in the underground industry.

Unveiling optimal half-cell potentials in RCC slabs through cutting-edge ANFIS, ANN and genetic algorithm integration

Purpose The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms. Design/methodology/approach In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model. Findings In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model. Originality/value Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.

Environmental Constitutionalism in China: A Constitution without Constitutionalism?

Abstract There is a long tradition of constitutionalising environmental protection in the People's Republic of China ('China'). This is illustrated, for example, by the constitutionalisation of the need to construct ‘ecological civilisation’ – a set of values and development concepts included in 2018 as part of constitutional amendments. Yet, the Constitution of China is often described as a constitution without constitutionalism. This article examines the constitutional environmental provisions in China, as well as the underpinning constitutional theories, to demonstrate how China enjoys environmental constitutionalism. This version of constitutionalism, however, is absent of rights and overwhelmingly enforced through state-based approaches, which means that it is distinct from the rights-based and courts-centred versions of liberal constitutionalism. This study thus exemplifies how constitutional practices may adopt different formulations and environmental constitutionalism exists without being committed to liberal principles.

The influence of different Cheek and lip retractors and emissivity on intraoral infrared thermography.

To assess the influence of two conventional and one adapted cheek and lip retractors and three emissivity setting values on intraoral infrared thermography (IT) temperature values. The sample was composed by 50 volunteers. Three cheek and lip retractors were tested: Group 1 - flex retractor (FR); Group 2 - FR adapted with Styrofoam; Group 3 - U-type retractor (UR) for cheek and lip. All thermograms were acquired using FLIR T650 infrared camera. A set of three thermograms in frontal norm were acquired for each lip and cheek retractor at 0.91, 0.96 and 0.98Ɛ, with an interval of 15 minutes between each set of images to avoid thermal interference. All images were assessed by two observers. The ROIs' mean temperature of the four upper incisors were recorded. Two-way ANOVA and Sidak posttest were used for data assessment with a significance level of 5%. Group 3 showed higher mean temperature than groups 1 and 2 at all emissivity settings for all assessed teeth (p < 0.05). 0.91Ɛ showed higher temperature than 0.96Ɛ and 0.98Ɛ for all assessed variables (p < 0.01). Contralateral teeth assessed using Group 3 at 0.91Ɛ showed statistical differences between each other (p < 0.05). No statistical difference was observed between contralateral teeth assessed using Group 1 and 2 at 0.96 and 0.98Ɛ (p > 0.05). The choice of cheek and lip retractor and emissivity setting can interfere on intraoral IT temperature values. U-type cheek and lip retractor and 0.91Ɛ setting should not be used for IT image acquisition when assessing dental tissues.

GA-BP neural network-based nonlinear regression model for machining errors of compressor blades

Given that blade machining errors can substantially degrade aeroengine performance and reliability, there is a critical imperative to implement stringent control over blade machining errors during the manufacturing process. Nevertheless, the prevailing complexity surrounding the numerous machining error types and their obfuscated interrelationships impedes the comprehensive control of blade profile errors. Consequently, unveiling the nonlinear relationships between the distinct machining errors represents a task of paramount importance. To uncover these nonlinear relationships, this work pioneers the application of factor analysis and Genetic Algorithm-optimized BP neural networks to enable nonlinear regression for diverse blade machining errors. Specifically, this paper first constructs an orthogonal factor analysis model to reduce the dimensionality of complex, high-dimensional blade machining error data and extract underlying correlations. This analysis reveals three distinct error groups, each dominated by a latent common factor. Building on these factor analysis outputs, GA-BP neural networks are then leveraged to perform nonlinear regression for intra-group errors. Guided by the Maximal Information Coefficients (MIC) between different errors, we identify control error types within each group to serve as network inputs, with other errors designated as outputs. To prevent overfitting and accelerate convergence of the neural network, Monte Carlo method is used to augment the limited raw error dataset. The augmented data trains the GA-BP network, establishing multivariate nonlinear models with control errors as independent variables and other errors as dependent variables. The model accuracy is validated by a separate validation set. The comparisons between the predicted and true values in the validation set reveal that the relative errors are around 5 %, indicating that the models achieve an accuracy of approximately 95 %. These results demonstrate the satisfactory performance of the established models. Overall, this work elucidates previously unclear relationships between distinct blade machining errors. These new insights establish the foundation for comprehensive error control and will directly benefit blade design, manufacturing, and performance.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

Predicting the mechanical properties of stainless steels using Artificial Neural Networks

Abstract Knowing the material properties is of a crucial importance when planning to manufacture some structure. That is true for the steel structures, as well. Thus, for the proper planning of a certain steel part or a structure production, one must be aware of the properties of the material, to be able to make a qualified decision, which material should be used. Considering that the manufacturing of steel products is constantly growing in various branches of industry and engineering, the problem of predicting the material properties, needed to satisfy the requirements for the certain part efficient and reliable functioning, becomes an imperative in the design process. A method of predicting four material properties of the two stainless steels, by use of the artificial neural network (ANN) is presented in this article. Those properties were predicted based on the particular steels’ known chemical compositions and the corresponding material properties available in the Cambridge Educational System EDU PACK 2010 software, using neural network module of MathWorks Matlab. The method was verified by comparing the values of the material properties predicted by this method to known values of properties for the two stainless steels, X5CrNi18-10 (AISI 304), X5CrNiMo17-12-2 (AISI 316). The difference between the two sets of values was below 5% and, in some cases, even negligible.

Verification of a simplified aneurysm dimensionless flow parameter to predict intracranial aneurysm rupture status.

Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data. A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature. Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56). This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting. This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status.

Why Professional Branding: What difference will it make for me (as Gen Z)?

This article discusses professional branding that is rooted in marketing aimed at helping professionals differentiate themselves from others through personal branding to project their uniqueness and related values. It streamlines the process behind creating a robust profile that enhances simple elements of a resume by providing distinctive information to corporations or target markets. Furthermore, the article highlights the two broad theoretical underpinnings of personal branding: marketing-based and self-presentation behaviors. Marketing-based definitions frame the individual as a product to be marketed, while self-presentation-based definitions emphasize the formation of a person's public persona through strategic relationship management. Additionally, the article portrays personal branding as a defined set of individual characteristics, attributes, values, and beliefs crafted into a narrative and imagery that establishes a competitive advantage by influencing the perceptions of a targeted audience. It concludes that professional branding is an effective personal tool for individual differentiation and advancement in a highly globalized employment environment, particularly for Gen Z individuals with Arab cultural backgrounds in STEAM professional fields.

A specific diagnostic metabolome signature in adult IgA vasculitis

IntroductionIgA vasculitis diagnosis relies primarily on clinical features and is confirmed by pathological findings. To date, there is no reliable noninvasive diagnostic biomarker.ObjectiveWe aimed to explore the baseline serum metabolome of adult patients with IgA vasculitis to identify potential diagnostic biomarkers.MethodsWe performed a study comparing the serum metabolome of patients with IgA vasculitis to that of patients with inflammatory condition, namely spondyloarthritis. Serum analyses were performed by high-performance liquid chromatography-mass spectrometry.ResultsFifty-five patients with IgA vasculitis and 77 controls with spondyloarthritis (age- and sex-matched) were included in this study. The median age of IgA vasculitis patients was 53 years. Two-thirds of patients were female (n = 32). At the time of vasculitis diagnosis, 100% of patients had skin involvement and 69% presented with glomerulonephritis (n = 38). Joint and digestive involvement were observed in 56% (n = 31) and 42% (n = 23) of patients. Four discriminative metabolites between the two groups were identified: 1-methyladenosine, L-glutamic acid, serotonin, and thymidine. The multivariate model built from the serum metabolomes of patients with IgA vasculitis and spondyloarthritis revealed an accuracy > 90%. As this model was significant according to the permutation test (p < 0.01), independent validation showed an excellent predictive value of the test set: sensitivity 98%; specificity 98%, positive predictive value 97% and negative predictive value 98%.ConclusionTo our knowledge, this study is the first to use the metabolomic approach for diagnostic purposes in adult IgA vasculitis, highlighting a specific diagnostic metabolome signature.