Initial Value Research Articles

Association of RT-qPCR Ct Values and Disease Severity among COVID-19 Patients Visiting a Tertiary Care Hospital in Nepal

COVID-19 pandemic due to SARS-CoV-2 has been one of the major global health issues of this aeon. The aim of this study was to evaluate the association of SARS-CoV-2 cycle threshold (Ct) values with multiple factors among COVID-19 patients visiting a tertiary care hospital in Sudurpashchim province of Nepal. A retrospective analysis was performed on the data of randomly selected COVID-19 cases among the total RT-qPCR tested patients from March 2020 to April 2022. The Ct values at the time of patient admission and their clinical outcomes (discharge or death) were compared. Among the COVID-19 patients, survivor group had significantly higher initial Ct value compared to non-survivors [median Ct values 23.21 and 24.39 (P < 0.0001)]. Selected haematological parameters; white blood cells (P<001), neutrophils (P<001), and monocytes (P<0.0001), and all the biochemical parameters were significantly different between these two groups (p < 0.005). Furthermore, significantly increased CRP (61.54±63.00, P<0.0017), D-dimer levels (0.8979± 1.480, P<0.0001), creatinine (0.7931±0.2551, P<0.0001), monocytes (0.6782±0.7981, P<0.0001), and random blood sugar (152.4±34.32, P<0.0001) were observed among non-survivors indicating as cause of disease severity in COVID-19. The findings of this study imply that the Ct value, CRP and D-dimer levels could be a crucial marker for the early detection of severe COVID-19 patients or those at higher risk of developing severe disease. This will eventually help to identify cases requiring immediate and critical medical care and reduce mortality.

Neural Network Methods Based on Efficient Optimization Algorithms for Solving Impulsive Differential Equations

In view of the fact that impulsive differential equations have the discreteness due to the impulse phenomenon, this paper proposes a single hidden layer neural network method based extreme learning machine and a physicsinformed neural network method combined with learning rate attenuation strategy to solve linear impulsive differential equations and nonlinear impulsive differential equations respectively. For the linear impulsive differential equations, firstly, the interval is segmented according to the impulse points, and a single hidden layer neural network model is constructed, the weight parameters of hidden layer are randomly set, the optimal output parameters and solution of the first segment are obtained by the extreme learning machine algorithm, then we calculate the initial value of the second segment according to the jumping equation, and the remaining segments are solved in turn in the same way. Although the single hidden layer neural network method proposed can solve linear equations with high accuracy, it is not suitable for solving nonlinear equations. Therefore, we propose the physics-informed neural network combined with learning rate attenuation strategy to solve the nonlinear impulsive differential equations, then the Adam algorithm and L-BFGS algorithm are combined to find the optimal approximate solution of each segment. Numerical examples show that the single hidden layer neural network method with Legendre polynomials as the activation function and the physics-informed neural network method combined with learning rate attenuation strategy can solve linear and nonlinear impulsive differential equations with higher accuracy.

Measures of stability and instability of the differential system zero solution and their dependence on the initial moment

The recently introduced concepts of stability measures and instability measures of different types are studied: Lyapunov, Perron or upper-limit. These concepts allow a natural probabilistic interpretation, which shows the dependence of specific properties of solutions of a differential system, starting close to its zero solution, on arbitrarily small perturbations of the initial values of the Cauchy problem with a fixed initial moment. The work examines precisely the dependence of these measures on the initial moment. It has been proved that this dependence is completely absent for one-dimensional and autonomous systems, as well as for many types of stability or instability of linear systems. Moreover, it has been proved that the extreme values of the measures of stability or instability themselves are always invariant with respect to the choice of the initial moment. Finally, an example of a system is given for which this dependence, on the contrary, manifests itself to the maximum possible extent.

Regularization with two differential operators and its application to inverse problems

We introduce a regularization method with two differential operators for solving a linear ill-posed operator equation system. The existence and uniqueness of regularized solutions to the problem are derived. With an a priori as well as an a posteriori parameter choice strategy, convergence and convergence rates of the regularized solution are also obtained. As an application, we apply the regularization to a simultaneous inversion of the source term and the initial value problem for a heat conduction equation, numerical experiments are presented to demonstrate the effectiveness of the proposed method.

Individual features of the temporal dynamics of inhibitory processes in visual short-term memory depending on the content of stimuli and memorization training

Introduction. Age-related changes in cognitive functions are caused by various processes: degrading inhibitory control, decrease in the volume of working memory, or a slowdown in the speed of thought processes. However, there is no unanimous opinion on the dynamics of these processes and their interactions yet. Based on this, our research goal became finding the temporal dynamics and individual relationship of the effects of proactive interference (Retrival-Induced Forgeting (RIF)) and learning to remember (Retrieval-Based Learning (RBL)) depending on the content of the presented stimuli as well as the age-related specifics of memorization when training the short-term visual memory. Materials and methods. Memory training was carried out in a mode independently organized by the study participants using a computerized memorization program in a proactive interference model, including five sessions of visual presentation of stimuli. University students participated in the first part of the study, aimed at clarifying the meaning of the content of two different sets of stimuli, (220 people, 20.4 ± 1.9 years old), whereas second part aimed to determine the age-related characteristics of visual short-term memory training in a group of students, (70 subjects, 21.7±2.5 years) and a group of elderly women (25 subjects, 64.4±6.3 years). Research results. The results of the memory trace retrieval analysis showed that the RIF effect dominates over the RBL effect regardless of the content of the stimuli, but at different rates of its development depending on the set of stimuli used. With significant individual diversity in the combination of memory strategies, the RIF effect dominated at the initial stage of stimulus presentation (67.6% of cases) and gradually replaced by RBL (56.2% in the fifth run of the experiment). Analysis of age-related characteristics of the effectiveness of short-term visual memory training showed that when it is independently organized, maintaining activity is observed in 32% of university students and 50% in the elderly group. Along with this. Only average of ten approaches to complete this task accompanied by a further decrease in the number of training participants. Comparison of the results of the first three training sessions revealed a significant increase in the memory index in both groups compared to the initial value and the presence of a RIF effect with a further more rapid increase in the efficiency of memorization in students. Conclusion. Proactive interference while memorization of visual stimuli repeatedly presented in five sessions, appears regardless of their content, age of the subjects and memorization training. The efficiency of memorization due to training grows faster in the group of students and accompanied by the combination of the RIF and RBL effects that is characterized by individual diversity without a significant effect on the final result of the memory retrieval.

A Block Hybrid Method with Equally Spaced Grid Points for Third-Order Initial Value Problems

In this paper, we extend the block hybrid method with equally spaced intra-step points to solve linear and nonlinear third-order initial value problems. The proposed block hybrid method uses a simple iteration scheme to linearize the equations. Numerical experimentation demonstrates that equally spaced grid points for the block hybrid method enhance its speed of convergence and accuracy compared to other conventional block hybrid methods in the literature. This improvement is attributed to the linearization process, which avoids the use of derivatives. Further, the block hybrid method is consistent, stable, and gives rapid convergence to the solutions. We show that the simple iteration method, when combined with the block hybrid method, exhibits impressive convergence characteristics while preserving computational efficiency. In this study, we also implement the proposed method to solve the nonlinear Jerk equation, producing comparable results with other methods used in the literature.

A novel accelerated convergence method for solving adjoint equations based on modal reduction

The efficiency of adjoint-based aerodynamic shape optimization depends critically on the solution efficiency of adjoint equations. In this letter, we employ the Proper Orthogonal Decomposition (POD) method to analyze the adjoint field samples and project them from the physical space into a low-order modal space. Subsequently, the full-order adjoint equations are reduced to low-order equations using the POD modes. Thus, we can efficiently predict the initial values for pseudo-time marching, thereby accelerating the solution of adjoint equations. Results indicate that the high-order POD modes are crucial for constructing the low-dimensional system. Moreover, this method can be seamlessly integrated with our previously established Dynamic Mode Decomposition (DMD) acceleration method to form a POD+DMD acceleration approach. Application of this approach to the flow past a National Advisory Committee for Aeronautics 0012 airfoil demonstrates a noteworthy 80.9% reduction in iteration numbers when solving the adjoint equations. Even for the airfoil located on the upper boundary of sampling space, the number of iterations is still reduced by 72.6%. Therefore, we believe that the proposed method holds significant promise for improving the efficiency of adjoint-based aerodynamic shape optimization in future research.

Robust design optimization of spin algorithm to reduce spinning time and vibration in washing machine

This study focuses on optimizing the spin algorithm of washing machines to reduce spin time and vibration, two crucial performance metrics often in a trade-off relationship due to structural limitations. We developed a simulator for the spin process that accounts for the uncertainty associated with the clothes’ position in the drum. Using this simulator, we designed a robust optimization algorithm to minimize spin time while maintaining acceptable vibration levels. The variation in the mass and position of laundry within the drum primarily affects vibration performance during the spin process. We formulated the unknown unbalance caused by the laundry as test-implementable unbalance parameters (UBP), represented as random variables. The uncertainties in the laundry’s unbalance for repeated experiments were estimated by the distribution of these UBPs. Experiments were conducted at parametrically designed points to measure vibrations and internal sensor values for control in specific revolutions per minute (RPM)ranges. These data were used to create approximate models. Monte Carlo simulation was then applied with UBP distributions obtained from the clothing experiments to meta-models, and the simulator was equipped with the spin algorithm. This process allowed us to predict the distribution of the maximum tub vibration displacement and spin time. A robust optimization algorithm was subsequently applied to the simulation model to derive the optimal spin algorithm. The proposed methodology yielded substantial improvements: a 25% reduction in the mean value of maximum displacement and a 90% reduction in the mean value of spin time compared to initial values.

Elastoplastic solution for cylindrical cavity contraction in unsaturated soils

This paper develops an elastoplastic solution for cylindrical cavity contraction in unsaturated soils under constant suction conditions. The elastoplastic cavity contraction problem is formulated into a set of first-order ordinary differential equations by introducing a new auxiliary variable, which is solved as an initial value problem. The new solution is validated by comparison with numerical simulation results. Finally, parametric studies show that, as soil suction increases, the internal support pressure decreases faster with cavity contraction, the unloading-induced plastic zone becomes narrower, and the changes in effective stresses are smaller for a given tunnel convergence.

An Improved Grey Wolf Optimizer with Multi-Strategies Coverage in Wireless Sensor Networks

For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability.

Comparing the Performance of Two Butcher-Based Block Hybrid Algorithms for the Solution of Initial Value Problems

In this paper, we compare the performances of two Butcher-based block hybrid methods for the numerical integration of initial value problems. We compare the condition numbers of the linear system of equations arising from both methods and the absolute errors of the solution obtained. The results of the numerical experiments illustrate that the better conditioned method outperformed its less conditioned counterpart based on the absolute errors. In addition, after applying our method on some examples, it was discovered that the absolute errors in this work were better than those of a recent study in the literature. Hence, we recommend this method for the numerical solution of stiff and non-stiff initial value problems.

Mechanisms of anaerobic treatment of sulfate-containing organic wastewater mediated by Fe0 under different initial pH values.

The anaerobic treatment of sulfide-containing organic wastewater (SCOW) is significantly affected by pH, causing dramatic decrease of treatment efficiency when pH deviates from its appropriate range. Fe0 has proved as an effective strategy on mitigating the impact of pH. However, systematic analysis of the influence mechanism is still lacking. To fill this gap, the impact of different initial pH values on anaerobic treatment efficiency of SCOW with Fe0 addition, the change of fermentation type and methanogens, and intra-extracellular electron transfer were explored in this study. The results showed that Fe0 addition enhanced the efficacy of anaerobic treatment of SCOW at adjusted initial pH values, especially at pH 6. Mechanism analysis showed that respiratory chain-related enzymes and electron shuttle secretion and resistance reduction were stimulated by soluble iron ions generated by Fe0 at pH 6, which accelerated intra-extracellular electron transfer of microorganisms, and ultimately alleviated the impact of acidic pH on the system. While at pH 8, Fe0 addition increased the acetogenic bacteria abundance, as well as optimized the fermentation type and improved the F420 coenzyme activity, resulting in the enhancement of treatment efficiency in the anaerobic system and remission of the effect of alkaline pH on the system. At the neutral pH, Fe0 addition had both advantages as stimulating the secretion ofrespiratory chainand electron transfer-related enzymes at pH 6 and optimizing the fermentation type pH 8, and thus enhanced the treatment efficacy. This study provides important insights and scientific basis for the application of new SCOW treatment technologies.

Uncertain data in initial boundary value problems: Impact on short and long time predictions

We investigate the influence of uncertain data on solutions to initial boundary value problems with well posed boundary conditions. Uncertainty in the forcing function, initial conditions and boundary conditions are considered and we quantify their relative influence for short and long time calculations. For short time calculations, uncertainty in the initial data dominates. As time grows, the influence of initial data vanishes exponentially fast. For longer time calculations, the uncertainty in the forcing function and boundary data dominate, as they grow in time. Errors due to the forcing function grow faster (linearly in time) than the ones due to the boundary data (grow as the square root of time). Roughly speaking, the results indicate that for short time calculations, the initial conditions are the most important, but for longer time calculations, focus should be on the forcing function and boundary conditions. The findings are especially important when similar mathematical and numerical techniques are used for both short and long times. Our qualitative results can guide more quantitative investigations where details of the uncertain data are known.

Rancang Bangun Akuarium Dengan Pengkondisian PH Air Otomatis Berbasis PLTS

Many people in Indonesia keep freshwater ornamental fish, including guppy fish (Poecilia reticulata), green growth fish (Gyrinocheilus aymonieri), neon tetra fish (Paracheirodon innesi). Neon tetra fish are popular because of their distinctive striking and bright body lines, while guppy fish are kept because of their ease of care and beautiful color variations, especially in male guppies. Guppy fish can grow well at temperatures of 22°C - 27°C and pH 6.5-7.2. Because managers are busy, this becomes an obstacle in fish cultivation, because the water quality that determines fish development becomes unstable. The results of the PLTS output test for 7 days 7 hours per day from 09.00 – 15.00 WIB can charge the battery with an average voltage of 13.89 V and an average current of 1.2 A and an average power of 14.15 W and can run for 24 hours. O'clock. Testing the battery for 7 days and 7 hours from 09.00 – 15.00 WIB can meet power needs for 24 hours with an average battery voltage of 13.36 V and an average current of 0.71 A. Meanwhile, the test results for the servo motor with a feed weight of 2, 45 grams and a servo motor delay of 2 seconds. Testing the pH of the water using a pH pump showed a change from the initial value of 8.06 to 6.91 and was measured with the PH4502C sensor. Tests without a PH pump showed an increase in changes from the initial pH value of 7.76 to 8.05.

Melt pond fractions on Arctic summer sea ice retrieved from Sentinel-3 satellite data with a constrained physical forward model

Abstract. The presence of melt ponds on Arctic summer sea ice significantly alters its albedo and thereby the surface energy budget and mass balance. Large-scale observations of melt pond coverage and sea ice albedo are crucial to investigate the role of sea ice for Arctic amplification and its representation in global climate models. We present the new Melt Pond Detection 2 (MPD2) algorithm, which retrieves melt pond, sea ice, and open-ocean fractions as well as surface albedo from Sentinel-3 visible and near-infrared reflectances. In contrast to most other algorithms, our method uses neither fixed values for the spectral albedo of the surface constituents nor an artificial neural network. Instead, it aims for a fully physical representation of the reflective properties of the surface constituents based on their optical characteristics. The state vector X, containing the optical properties of melt ponds and sea ice along with the area fractions of melt ponds and open ocean, is optimized in an iterative procedure to match the measured reflectances and describe the surface state. A major problem in unmixing a compound pixel is that a mixture of half open water and half bright ice cannot be distinguished from a homogeneous pixel of darker ice. In order to overcome this, we suggest constraining the retrieval with a priori information. Initial values and constraint of the surface fractions are derived with an empirical retrieval which uses the same spectral reflectances as implemented in the physical retrieval. The snow grain size and optical thickness change with time, and thus the ice surface albedo changes throughout the season. Therefore, field observations of spectral albedo are used to develop a parameterization of the sea ice optical properties as a function of the temperature history of the sea ice. With these a priori data, the iterative optimization is initialized and constrained, resulting in a retrieval uncertainty of below 8 % for melt pond and 9 % for open-ocean fractions compared to the reference dataset. As reference data for evaluation, a 10 m resolution product of melt pond and open-ocean fraction from Sentinel-2 optical imagery is used.

Reduction of Boundary Value Problem using Shape Function

This research looks at the MHD flow of a power-law fluid on a stretched sheet with a uniform heat source. The boundary shape function technique translated the resulting Couple of Nonlinear Ordinary Differential equations (BVP) with boundary conditions into a related initial value problem (IVP). The BVP's solution is represented by the boundary shape function (BSF), and a further new variable is the free function. With the right method, the initial value of the problem may be numerically solved.’

The Effect of Different Decontamination Methods on the Micro-Tensile Bond Strength of CAD/CAM Resin-Based Blocks to Resin Cement.

This study evaluates the impact of decontamination methods on Microtensile bond strength (μTBS) between resin cement and resin blocks. Twenty CAD/CAM resin blocks from two manufacturers were wet-polished, sandblasted, and ultrasonically cleaned. After blood and saliva contamination, blocks were divided into subgroups: control, water rinsing, acid etching, alkaline cleaning paste, or 10-MDP containing cleaner. Resin-based cement was then applied. After 24 hours, the blocks were sectioned to obtain bars for testing. Half of the specimens were tested immediately for μTBS, and the other half underwent artificial aging. The surfaces of the blocks were inspected with a scanning electron microscope (SEM). Three-way ANOVA was performed for μTBS values (α=0.05). In one of the substrates, the positive control subgroup obtained the highest value (56,01 MPa, SD:6,96) followed by 10-MDP cleaner and universal cleaning paste, when immediately tested, with significant differences respect to the water rinsing (p⟨0.041) and acid etching (p⟨0.048) groups. After thermocycling, higher values were found in the 10-MDP cleaner (47,57 MPa, SD:8,15), but differences were not significant. In the other substrate group, the 10-MDP cleaner subgroup showed highest bond strengths (64,46 MPa SD: 10,92) at the initial test. After thermocycling, 10-MDP cleaner (58,66 MPa, SD: 9,93) gave the highest μTBS value. Significant differences between water rinsing group and the rest of subgroups (p⟨0.001), and between 10-MDP cleaner and the positive control group (p⟨0.006) were observed. Cleaning after contamination improves bonding. 10-MDP containing cleaner can help to restore initial μTBS value and maintain it in the long-term.

Variability of intervertebral joint stiffness between specimens and spine levels.

Introduction: Musculoskeletal multibody models of the spine can be used to investigate the biomechanical behaviour of the spine. In this context, a correct characterisation of the passive mechanical properties of the intervertebral joint is crucial. The intervertebral joint stiffness, in particular, is typically derived from the literature, and the differences between individuals and spine levels are often disregarded. Methods: This study tested if an optimisation method of personalising the intervertebral joint stiffnesses was able to capture expected stiffness variation between specimens and between spine levels and if the variation between spine levels could be accurately captured using a generic scaling ratio. Multibody models of six T12 to sacrum spine specimens were created from computed tomography data. For each specimen, two models were created: one with uniform stiffnesses across spine levels, and one accounting for level dependency. Three loading conditions were simulated. The initial stiffness values were optimised to minimize the kinematic error. Results: There was a range of optimised stiffnesses across the specimens and the models with level dependent stiffnesses were less accurate than the models without. Using an optimised stiffness substantially reduced prediction errors. Discussion: The optimisation captured the expected variation between specimens, and the prediction errors demonstrated the importance of accounting for level dependency. The inaccuracy of the predicted kinematics for the level-dependent models indicated that a generic scaling ratio is not a suitable method to account for the level dependency. The variation in the optimised stiffnesses for the different loading conditions indicates personalised stiffnesses should also be considered load-specific.

Fuzzy cognitive maps for municipal governance improvement.

This paper applies Fuzzy Cognitive Maps (FCMs) to understand the diverse behavior of municipal governments in Ecuador to find common elements that influence the well-being of citizens in the short and long term. Information gathering was conducted in two stages: in the first one, a group of 16 national experts was consulted to develop the initial FCM; in the second stage, local experts from 220 municipalities were interviewed to collect information on the general validity of initial FCMs and specific values given to concepts and relationships in their municipalities. Results show the importance of certain concepts for long-term municipal performance, such as the need for a competitive entrepreneurial sector, improving human resources in the municipality, and, particularly, having a competent mayor with leadership skills and a forward-looking vision that enables the development of municipal projects required to reach an efficient and equitable coverage of goods and services throughout the city. Through the application of genetic algorithms, the FCM was calibrated to ascertain the long-term dynamics of municipal development and the optimal values of the concepts that would optimize the attainment of the set objectives. The derived outcomes suggest the desirability of the maintenance of, in principle, unwanted structures like financial transfers from the central government and the need to exploit natural resources to attain urban development.

TRIBOLOGICAL PROPERTIES OF A SILICONE-BASED COMPOSITEWITH INORGANIC ADDITIVES

The paper presents the results of experimental studies, including tribological tests of silicone-based compositeswith additions of hexagonal boron nitride (hBN) and titanium (Ti). The tests were conducted on a BrukerUMT2 tribotester and using a pin-on-disk setup developed by the authors, without a lubricating medium,and they employed a steel ball made of 100Cr6 steel and a sample made of the composite. During the tests,the products were not removed from the contact area. The paper analyzes the influence of additives on thetribological properties of the composite, i.e., the coefficient of friction (COF) as a function of distance and thewear of the tested samples. In the case of samples containing hBN, the COF decreases with an increase in itscontent. After reaching a volumetric percentage concentration of 20%, it begins to stabilize with the increasein mass loss. The profiles of COF changes as a function of distance for samples with different additivecontents are comparable. The self-lubricating properties of hBN have been confirmed. The addition of Tireduces the COF value, which decreases with the increase in the Ti content. Samples with a mass percentageconcentration exceeding 100% of the Ti content have a COF value equal to the initial value for silicone. Thecomposite containing hBN has a lower COF value than samples with the Ti addition, and the wear tracks ontheir surface are narrower and shallower.