Values Of Function Parameters Research Articles

STUDY OF PARAMETER SPACE OF MULTIDIMENSIONAL SYSTEM WITH RELAY HYSTERESIS AND PERTURBATION

The object of research is an 𝑛-dimensional system of ordinary differential equations that contains a constant diagonal matrix with real eigenvalues, two-position relay nonlinearity of hysteresis type with a parameter and a continuous periodic perturbation function with a parameter. In the case of a special type of feedback vector (one non-zero element), we obtain conditions for system parameters that ensure the existence of a unique two-point oscillatory periodic solution with a period multiple of the perturbation function period. We establish a functional dependence of the nonlinearity parameter on the perturbation function parameter. Influence of perturbation function parameter values on the existence of the solution is investigated. We offer an algorithm for seeking system parameters and the instant of the first relay switching in the case when the solution period is given. Theoretical results, including the proposed algorithm, are illustrated by the example of a three-dimensional system.

Online Attentive Kernel-Based Off-Policy Temporal Difference Learning

Temporal difference (TD) learning is a powerful framework for value function approximation in reinforcement learning. However, standard TD methods often struggle with feature representation and off-policy learning challenges. In this paper, we propose a novel framework, online attentive kernel-based off-policy TD learning, and in combination with well-known algorithms, introduce OAKGTD2, OAKTDC, and OAKETD. This framework uses two-timescale optimization. In the slow-timescale, a sparse representation of state features is learned using an online attentive kernel-based method. In the fast-timescale, auxiliary variables are used to update the value function parameters under the off-policy setting. We theoretically prove the convergence of all three algorithms. Through experiments conducted in several standard reinforcement learning environments, we demonstrate the effectiveness of the improved algorithms and compare their performance with existing algorithms. Specifically, from the perspective of cumulative rewards, the proposed algorithm achieves an average improvement of 15% compared to on-policy algorithms and an average improvement of 25% compared to common off-policy algorithms.

Government size and the effectiveness of fiscal policy: the bigger the better?

Abstract This study investigates the effect of government size, as measured by the tax revenue to gross domestic product (tax-GDP) ratio, on output responses to increases in government purchases. First, we show that in a standard static neoclassical model, the stimulus effect of fiscal expansion on output increases with the tax-GDP ratio. This finding is quantitatively confirmed using a dynamic neoclassical model with standard functional forms and parameter values. To empirically test the theoretical findings, we analyze the responses of macroeconomic variables to an unanticipated increase in government purchases for 12 Organisation for Economic Cooperation and Development (OECD) countries during 1985–2019 using a state-dependent local projection method. The estimation results reveal that while output responses to an unanticipated fiscal expansion are significantly positive when the tax-GDP ratio is high, they are statistically indistinguishable from zero when the ratio is low. Overall, our findings suggest that fiscal expansion can stimulate output more effectively at high tax rates, unlike the well-known predictions of the traditional Keynesian model.

Combining morphological and functional imaging parameters to diagnose primary bone neoplasms in the skull base, spine and sacrum

PurposeMorphological magnetic resonance (MR) and computed tomography (CT) features are used in combination with histology for diagnosis and treatment selection of primary bone neoplasms. Isolated functional MRI parameters have shown potential in diagnosis. Our goal is to facilitate diagnosis of primary bone neoplasms of the skull base, mobile spine and sacrum, by a comprehensive approach, combining morphological and functional imaging parameters.Materials and methodsPre-treatment MR of 80 patients with histologically proven diagnosis of a primary bone neoplasm of the skull base, mobile spine and sacrum were retrospectively analyzed for morphological and functional MRI parameters. Functional parameters were measured in 4 circular regions of interest per tumor placed on non-adjacent scan slices. Differences in values of functional parameters between different histologies were analyzed with Dunn’s test.ResultsChordomas were the predominant histology (60.0%). Most neoplasms (80.0%) originated in the midline and had geographical (78.2%) bone destruction. Amorphous-type calcification (pre-existing bone) was seen only in chordomas. Homogeneous contrast enhancement pattern was seen only in chondrosarcoma and plasmacytoma. Ktrans and Kep were significantly lower in both chordoma, and chondrosarcoma compared to giant cell tumor of the bone (p = 0.006 – 0.011), and plasmacytoma (p = 0.004 – 0.014). Highest diffusion-weighted MRI apparent diffusion coefficient (ADC) values corresponded to chondrosarcoma and were significantly higher to those of chordoma (p = 0.008).ConclusionWe identified the most discriminating morphological parameters and added functional MR parameters based on histopathological features that are useful in making a confident diagnosis of primary bone neoplasms in the skull base, mobile spine and sacrum.

Determining Drought and Salinity Stress Response Function for Garlic

Garlic (Allium sativum L.) is an important crop cultivated in arid and semi-arid climates. To quantify the tolerance of garlic to drought and salinity stresses in terms of parameter values of the stress response function, we conducted pot experiments in a greenhouse for two years. Nine 1/5000a Wagner pots were used for three treatments, namely drought-treated, salinity-treated, and control pots, for estimating the relative transpiration. Daily transpiration rates were observed by weighing pots, and the soil surface of each pot was covered. The soil water contents were measured hourly using two soil moisture probes for drought-treated pots, and two salinity probes for both soil water content and bulk electrical conductivity were monitored for salinity-treated pots. When the ratio of actual to potential transpiration fell below 50%, the root length distributions were obtained by dismantling the pots. The parameter values for both drought-stress and salinity-stress functions were estimated using inverse-analysis and bulk-analysis methods. The parameter values of drought-stress and salinity-stress functions obtained by the simpler and cheaper bulk method gave similar results to the inverse method when the root length distributions were relatively uniform.

Thyroid Function Test in Preterm Neonates: Normative Data.

Initial surge of thyroid-stimulating hormone (TSH) in neonates increases free and total triiodothyronine (T3) and tetraiodothyronine (T4) in 24-36 hours following birth, and the effect then gradually wanes off. As somatic and intellectual development is dependent on normal thyroid function especially in infancy, normative data in these children may be of immense value to diagnose hypothyroidism in this subset of infants. Comprehensive normative values of thyroid function parameters in preterm neonates are scarcely available. The objective of this study was to determine the normative value of thyroid function parameters in preterm neonates. Preterm neonates (n = 102) born at 34 and 35 weeks of gestation of euthyroid mothers from an iodine-sufficient population were evaluated for T3, T4, free thyroxine (FT4) and TSH during 3-7 days after birth and again after 1 month. The expected date of delivery (EDD) and Ballard score were used to identify the duration of gestation. The mean gestational age was 34.7 ± 0.41 weeks. The mean (± SD) for T3 (ng/dl), T4 (μg/dl), FT4 (ng/ml) and TSH (μIU/ml) on days 3-7 following birth was as follows: 156 ± 44.6, 12.8 ± 3.7, 1.50 ± 0.54 and 7.13 ± 6.04, respectively. Around 4 weeks of age, values changed to 104 ± 38.4, 12.1 ± 4.02, 1.46 ± 0.42 and 3.25 ± 2.85, respectively. All parameters changed significantly around 4 weeks, except FT4. None of the parameters were correlated with gestational age or body weight at birth. Normative values for each parameter in percentiles were generated. This study generated the normative values of the thyroid function test during the first week and after around 4 weeks of life for premature neonates (born at 34-35 weeks).

NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m6A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

Abax: Extracting Mathematical Formulas from Chart Images Using Spatial Pixel Information

Current state-of-the-art techniques in 2D chart analysis primarily emphasize the recognition of textual information as a means of comprehending and summarizing chart contents. However, the effective analysis and understanding of information embedded in chart images depends on accurate reverse-engineering of the behavior of depicted variables. In this paper, we propose a methodology, named Abax, as an initial study for recognizing and approximating the mathematical functions that describe the behavior of variables illustrated in chart images, particularly those containing curves. Abax is focused on approximating the values of function parameters using spatial pixel information derived from the identified keypoints of each curve. Qualitative results of the described method are presented as a proof of concept, demonstrating accurate extraction of information from fives types of functions: linear, polynomial, asymptotic, sinusoidal and arbitrary.

Parametric RSigELU: a new trainable activation function for deep learning

Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions are preferred since linear activation functions limit the learning performances of the deep learning models. Non-linear activation functions can be classified as fixed-parameter and trainable activation functions based on whether the activation function parameter is fixed (i.e., user-given) or modified during the training process of deep learning models. The parameters of the fixed-parameter activation functions should be specified before the deep learning model training process. However, it takes too much time to determine appropriate function parameter values and can cause the slow convergence of the deep learning model. In contrast, trainable activation functions whose parameters are updated in each iteration of deep learning models training process achieve faster and better convergence by obtaining the most suitable parameter values for the datasets and deep learning architectures. This study proposes parametric RSigELU (P+RSigELU) trainable activation functions, such as P+RSigELU Single (P+RSigELUS) and P+RSigELU Double (P+RSigELUD), to improve the performance of fixed-parameter activation function of RSigELU. The performances of the proposed trainable activation functions were evaluated on the benchmark datasets of MNIST, CIFAR-10, and CIFAR-100 datasets. Results show that the proposed activation functions outperforms PReLU, PELU, ALISA, P+FELU, PSigmoid, and GELU activation functions found in the literature. To access the codes of the activation function; https://github.com/serhatklc/P-RsigELU-Activation-Function.

Efficacy and safety of the initial hypotensive treatment with an optimized latanoprost composition

Purpose: a comparative study of the efficacy and safety of Latanoprost-Optic, a new composition of excipients and the original latanoprost, in the initial therapy of primary open-angle glaucoma (POAG)Material and methods. We performed a prospective, randomized, comparative study of 60 previously untreated patients (71 eyes) with early and advanced POAG stages. During the screening visit, patients were randomly divided into the main (Latanoprost-Optic) and the control (original latanoprost) groups. The target points included intraocular pressure (IOP), visual acuity, MD and PSD perimetry indices, RNFL thickness, MRW, NFL, GCL, IPL, tear film break time, OSDI, and adverse events. The observation period was 12 weeks. IOP was measured 4 and 12 weeks from observation start.Results. The average decrease in IOP in the study group by the end of the observation was 32% and did not show any statistically significant differences from the original drug at any control point. A positive unreliable dynamic of the values of most functional parameters was noted, which may indicate the presence of an indirect neuroprotective effect. In the study group, there was a positive trend in tear film rupture time and OSDI. According to corneal confocal microscopy, no negative changes in dynamics were noted, in contrast to the control group. Reversible discomfort and mild redness of the bulbar conjunctiva were recorded in both groups. No serious or systemic adverse events were noted.Conclusion. Latanoprost-Optic has a hypotensive efficacy, comparable to the original latanoprost, and a high favorable safety profile.

Differentiator-Based Output Feedback MPPT Controller for DFIG Wind Energy Conversion Systems with Minimal System Information

This paper introduces a novel differentiator-based maximum power point tracking (MPPT) controller for a wind energy conversion system (WECS) equipped with a doubly fed induction generator (DFIG). Building upon our previous algorithms, the proposed controller reduces the need for detailed system information and displays enhanced robustness against parameter variations and disturbances. The innovation lies in the elimination of the need for explicit functional forms or specific parameter values in the system’s dynamics, relying solely on relative degrees and control directions. Utilizing a higher-order switching differentiator (HOSD), this paper outlines a method for overestimating the time derivatives of system outputs, thereby simplifying both the controller design and stability analysis. Compared to existing solutions, the proposed method requires minimal information, offers simpler control law structures, and follows a systematic design approach with fewer design constants. Simulation results demonstrate the efficacy of the proposed controller in both tracking maximum power and regulating reactive power to zero, suggesting a more efficient and simplified approach to MPPT control in WECS.

Robust multi-agent reinforcement learning via Bayesian distributional value estimation

Reinforcement learning in multi-agent scenarios is essential for real-world applications as it can vividly depict agents’ collaborative and competitive behaviors from a perspective closer to reality. However, most existing studies suffer from poor robustness, preventing multi-agent reinforcement learning from practical applications where robustness is the core indicator of system security and stability. In view of this, we propose a novel Bayesian Multi-Agent Reinforcement Learning method, named BMARL, which leverages the distributional value function calculated by Bayesian inference to improve the robustness of the model. Specifically, Bayesian linear regression is adopted to estimate a posterior distribution concerning value function parameters, rather than approximating an expectation value for Q-value by point estimation. In this way, the value function is more generalized than previously obtained by point estimation, which is beneficial to the robustness of our model. Meanwhile, we utilize the Gaussian prior knowledge to integrate more prior knowledge while estimating the value function, which improves learning efficiency. Extensive experimental results on three benchmark multi-agent environments comparing with seven state-of-the-art methods demonstrate the superiority of BMARL in terms of both robustness and efficiency.

Selection of a representative sorting model in a preference disaggregation setting: A review of existing procedures, new proposals, and experimental comparison

We consider preference disaggregation in the context of multiple criteria sorting. The value function parameters and thresholds separating the classes are inferred from the Decision Maker’s (DM’s) assignment examples. Given the multiplicity of sorting models compatible with indirect preferences, selecting a single, representative one can be conducted differently. We review several procedures for this purpose, aiming to identify the most discriminant, average, central, parsimonious, or robust models. Also, we present three novel procedures that implement the robust assignment rule in practice. They exploit stochastic acceptabilities and maximize the support given to the resulting assignments by all feasible sorting models. The performance of fourteen procedures is verified on problem instances with different complexities. The results of an experimental study indicate the most efficient procedures in terms of classification accuracy, reproducing the DM’s model, and delivering the most robust assignments. These include approaches identifying differently interpreted centers of the feasible polyhedron and robust methods introduced in this paper. Moreover, we discuss how the performance of all procedures is affected by different numbers of classes, criteria, characteristic points, and reference assignments.

Reducing sample size requirements by extending discrete choice experiments to indifference elicitation

Discrete choice (DC) methods provide a convenient approach for preference elicitation and they lead to unbiased estimates of preference model parameters if the parameterization of the value function allows for a good description of the preferences. On the other hand, indifference elicitation (IE) has been suggested as a direct trade-off estimator for preference elicitation in decision analysis decades ago, but has not found widespread application in statistical analysis frameworks as for discrete choice methods. We develop a hierarchical, probabilistic model for IE that allows us to do Bayesian inference similar to DC methods. A case study with synthetically generated data allows us to investigate potential bias and to estimate parameter uncertainty over a wide range of numbers of replies and elicitation uncertainties for both DC and IE. Through an empirical case study with laboratory-scale choice and indifference experiments, we investigate the feasibility of the approach and the excess time needed for indifference replies. Our results demonstrate (i) the absence of bias of the suggested methodology, (ii) a reduction in the uncertainty of estimated parameters by about a factor of three or a reduction of the required number of replies to achieve a similar accuracy as with DC by about a factor of ten, (iii) the feasibility of the approach, and (iv) a median increase in time needed for indifference reply of about a factor of three. If the set of respondents is small, the higher elicitation effort may be worth to achieve a reasonable accuracy in estimated value function parameters.

Hydroxychloroquine improves high-fat-diet-induced obesity and organ dysfunction via modulation of lipid level, oxidative stress, and inflammation

ABSTRACT Hydroxychloroquine (HCQ) is a successful therapeutic anti-inflammatory drug frequently utilized in clinical settings for a number of disorders and diseases, however, it impact on obesity has not yet been fully established. Therefore, this study aimed to investigate the effectiveness and mechanism of action of HCQ on obesity in rats fed high fat diet (HFD). Adult male rats were feed a HFD for 18 weeks and concurrently treated with HCQ. The data showed that HCQ significantly reduced body weight gain, decreased blood glucose, HbA1c, and improved insulin level and insulin sensitivity. Furthermore, it normalized the serum lipid profile and D-dimer level. Comparable values of cardiac function parameters with the control group were observed The effects of HCQ was explained by striking amelioration of oxidative stress indicated by the lower levels of 4HNE and higher levels of GPx and GSH in the liver of HFD+HCQ than HFD fed rats. Furthermore, the inflammatory and the anti-inflammatory markers in serum of HFD+HCQ-treated rats were normalized to the control levels. In conclusion, the stabilization of redox balance and modulation of inflammatory response and their interdependence explain the ameliorative effect of HCQ on obesity and related metabolic disruption as well as organ functions.

Risk stratification according to diastolic function indices in heart failure patients with mildly reduced or reduced ejection fraction.

The aim of the study is to evaluate the risk of all-cause mortality or heart failure hospitalizations in ambulatory patients with heart failure with reduced and mildly reduced ejection fraction (HFrEF or HFmrEF) according to diastolic function indices. Diastolic dysfunction in HF is both common and associated with poor prognosis. However, specific cut-off values of diastolic function parameters for prognostication of hard outcomes in HF have not been conclusively established. Analysis of full echocardiographic examination of consecutive ambulatory HFrEF and HFmrEF patients seen at a single tertiary hospital between 2010 and 2021 was retrospectively done. Data on all-cause mortality or heart failure hospitalizations were obtained from the electronic medical records and national mortality registry. Patients with > moderate left heart valvular dysfunction were excluded from the study. The final cohort included 4717 patients (75% males, median age 70 years interquartile range 61.3-78.4). After adjusting for clinical or echocardiographic variables, increased rates of mortality or HF hospitalizations were found when E/e'>10, left atrial volume index (LAVI) > 40 mL/m2, E/A ratio < 0.6, deceleration time (DT) < 180 ms, peak E-wave velocity > 0.78 m/s, and sPAP > 26 mmHg. However, no significant difference in outcomes between near-normal and normal values of E/e' (< 8 compared with 8-10) or LAVI (≤34 mL/m2 compared with LAVI 34-40 mL/m2) was found. In patients with HFmrEF and HFrEF, slightly abnormal diastolic indices were found to be associated with worse outcomes. We have demonstrated that in patients with heart failure with reduced and mildly reduced ejection fraction (HFrEF or HFmrEF), near-normal diastolic indices are associated with worse outcomes with the following cut-off values: max E-wave velocity > 0.78 m/s, E/e' ratio > 10, a LAVi > 40 mL/m2, DT > 180, E/A between 0.6 and 1.4, and a sPAP > 26 mmHg. Further research is needed to establish these suggested cut-off values.

A Compact IIoT System for Remote Monitoring and Control of a Micro Hydropower Plant.

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition monitoring assurance, and assessments of the need for maintenance activities) require the design of reliable and efficient devices or systems. The present work describes the design and implementation procedure of an Industrial Internet of Things (IIoT) system configured for a basic micro hydropower plant architecture and assuring simple means of customization for plant differences in structure and operation. The designed system features a set of commonly used functions specific to micro hydropower exploitation, providing maximum performance and efficiency.

Assessment of respiratory effects of cement dust on exposed and unexposed citizens in Rabak City, Sudan

Globally, the cement industry has been identified as a factor which causes significant pollution. This study is conducted to assess the respiratory effects of cement dust on exposed and unexposed citizens in Rabak city, Sudan. This descriptive cross- sectional study was carried out in Rabak city. 134 samples were taken from the population living near the Factory (exposed) and population living far from the factory (unexposed). The respiratory symptoms experienced, were scored and recorded using BMRC questionnaire on respiratory symptoms. The lung function measurements were performed using a portable Spirometer. The respiratory measurements of the subjects were Forced Vital Capacity (FVC), Forced Expiratory Volume in one second (FEV1) and Forced Expiratory Volume ratio (FEV1%). The results of the study showed that: there was significant reduction in the mean values of ventilatory function parameters: FVC (2.9097), FEV1 (2.5718) and FEV1% (87.4743) in the exposed group compared with the FVC (3.1175), FEV1 (2.8296) and FEV1% (90.7985) in unexposed group in the study area (P &lt; 0.05). The majority of the exposed citizens were infected by allergy 41.0%. The Factory Management should further provide the latest technologies to reduce industrial emissions to protect the environment and population living around the Factory.

An investigation of (2+1)-dimensional asymmetric Nizhnik–Novikov–Veselov system: Lie symmetry reductions, invariant solutions, dynamical behaviors and conservation laws

In this study, we develop the asymmetric Nizhnik–Novikov–Veselov (ANNV) system in (2+1)-dimensions, which has applications in processes of interaction of exponentially localized wave structures, as well as the infinitesimal generators, Lie symmetries, vector fields, and the commutator table. The link between Lie symmetry vectors and conserved vectors is constructed using symmetry conservation principles once Lie point symmetries are first deduced. Using the aforementioned Lie symmetry technique, two-stage symmetry reductions are used to obtain the precise analytical answers. These analytical solutions all incorporate a number of different functional parameters as well as arbitrary constant parameters. The diversity of the physical phenomena of the obtained soliton solutions is illustrated by the inclusion of arbitraryness of functional parameters and constants. By using Noether’s method, conservation laws have subsequently been attained. The innovative aspect of the work described in this paper is an attempt to use 3-dimensional and 2-dimensional visuals, along with appropriate arbitrary parameter selections and functional parameter values, to represent the dynamical behavior of the solutions that have been produced. In order to make this research more intriguing, stripe solitons, dark-bright solitons, solitary waves, singular wave-form soliton, and other types of soliton wave profiles of the achieved solutions are described. The effectiveness, benefits, and utility of the employed approach are demonstrated by the physical and graphical interpretation of the answers attained.

Non-parametric data-driven background modelling using conditional probabilities

Background modelling is one of the main challenges in particle physics data analysis. Commonly employed strategies include the use of simulated events of the background processes, and the fitting of parametric background models to the observed data. However, reliable simulations are not always available or may be extremely costly to produce. As a result, in many cases uncertainties associated with the accuracy or sample size of the simulation are the limiting factor in the analysis sensitivity. At the same time, parametric models are limited by the a priori unknown functional form and parameter values of the background distribution. These issues become ever more pressing when large datasets become available, as it is already the case at the CERN Large Hadron Collider, and when studying exclusive signatures involving hadronic backgrounds.A widely applicable approach for non-parametric data-driven background modelling is proposed, which addresses these issues for a broad class of searches and measurements. It relies on a relaxed version of the event selection to estimate conditional probability density functions and two different techniques are discussed for its realisation. The first relies on ancestral sampling and uses data from a relaxed event selection to estimate a graph of conditional probability density functions of the variables used in the analysis, while accounting for significant correlations. A background model is then generated from events drawn from this graph, on which the full event selection is applied. In the second, a novel generative adversarial network is trained to estimate the joint probability density function of the variables used in the analysis. The training is performed on a relaxed event selection, which excludes the signal region, and the network is conditioned on a blinding variable. Subsequently, the conditional probability density function is interpolated into the signal region to model the background. The application of each method on a benchmark analysis and on ensemble tests is presented in detail, and the performance is discussed.