Output Quantities Research Articles

The patterns of inbreeding depression in food-deceptive Dactylorhiza orchids.

Inbreeding depression (ID) in food-deceptive plants have been reported previously, however, it has not been often proven that selfed seeds germinate better than outbred ones or that selfing affects ID. To resolved these issues, food-deceptive related Dactylorhiza majalis, D. incarnata var. incarnata and D. fuchsii orchids were investigated. Hand pollination treatments and control pollination were conducted. Fruit set, number of seeds per fruit, seed length, number of well-developed seeds per fruit, and proportion of in vitro asymbiotic germination seeds, were analyzed in relation to inflorescence levels and used as fitness indicators for these orchids. The ID and pollen limitation were measured. The lowest ID (δ = -1.000) was in D. majalis, and present in combination with a high pollen limitation in its populations. D. fuchsii showed higher ID (δ = 0.366), and D. incarnata var. incarnata weak ID (δ = 0.065), although ID varied between its fitness components. The seed number per fruit differed significantly between the treatments and the inflorescence levels in treatments. This study emphasizes that the breeding system rather than the flower position on the inflorescence shaped the quality and quantity of reproductive output. The ID and its effect on germination of food-deceptive orchid seeds undoubtedly played an important role.

Response to responsible research assessment I and II from the perspective of the DGPs working group on open science in clinical psychology

We comment on the papers by Schönbrodt et al. (2022) and Gärtner et al. (2022) on responsible research assessment from the perspective of clinical psychology and psychotherapy research. Schönbrodt et al. (2022) propose four principles to guide hiring and promotion in psychology: (1) In addition to publications in scientific journals, data sets and the development of research software should be considered. (2) Quantitative metrics can be useful, but they should be valid and applied responsibly. (3) Methodological rigor, research impact, and work quantity should be considered as three separate dimensions for evaluating research contributions. (4) The quality of work should be prioritized over the number of citations or the quantity of research output. From the perspective of clinical psychology, we endorse the initiative to update current practice by establishing a matrix for comprehensive, transparent and fair evaluation criteria. In the following, we will both comment on and complement these criteria from a clinical-psychological perspective.

Are random forests better suited than neural networks to augment RANS turbulence models?

Machine-learning (ML) techniques have bloomed in recent years, especially in fluid mechanics applications. In this paper, we trained, validated and compared two types of ML-based models to augment Reynolds-averaged Navier–Stokes (RANS) simulations. The methodology was tested in a series of flows around bumps, characterized by different levels of flow separation and curvatures. Initially, the ML-based models were trained in three configurations presenting attached flow, small and moderate separation and tested in two configurations presenting incipient and large separation. The output quantity of the machine-learning model is the turbulent viscosity as done in Volpiani et al. (2022). The new models based on artificial neural networks (NN) and random forest (RF) improved the results if compared to the baseline Spalart–Allmaras model, in terms of velocity field and skin-friction profiles. We noted that NN has better extrapolation properties than RF, but the skin-friction distribution can present small oscillations when using certain input features. These oscillations can be reduced if the RF model is employed. One major advantages of RF is that raw quantities can be given as input features, avoiding normalization issues (such as division by zero) and allowing a larger number of universal inputs. At the end, we propose a mixed NN-RF model that combines the strengths of each method and, as a result, improves considerably the RANS prediction capability, even for a case with strong separation where the Boussinesq hypothesis (and therefore the eddy-viscosity assumption) lacks accuracy.

Latent space representation method for structural acoustic assessments

When targeting structural acoustic objectives, engineering practitioners face epistemic uncertainties in the selection of optimal geometries and material distributions, particularly during early stages of the design process. Models built for simulating acoustic phenomena generally produce vector-valued output quantities of interest, such as autospectral density and frequency response functions. Given finite compute resources and time we seek computationally parsimonious ways to distill meaningful design information into actionable results from a limited set of model runs, and thus aim to use machine learning to perform model order reduction. Unlike time series data for which recurrent neural networks can learn from prior time steps to inform subsequent steps, frequency-dependent data demands a different machine learning paradigm. We thus evaluate the utility of autoencoders to represent structural acoustic results with a low dimensional latent space to enable such objectives as surrogate modeling for design optimization. We demonstrate the accuracy of autoencoder based methods of constructing a manifold representation for frequency dependent functions of varying modal density and damping, and discuss the predictive capability thereof.

A High-Gain Directional 1 × 8 Planar Antenna Array for 2.4GHz RFID Reader Applications

This research paper deals with a directional high gain PCB [Formula: see text] antenna array for 2.4[Formula: see text]ISM band utilizations. To achieve this antenna array, a well-matched equal-split 9[Formula: see text]dB-power splitter is designed and integrated with the suggested antenna array. It exhibits a wide operating range of 506[Formula: see text]MHz (2.022–2.528[Formula: see text]GHz) and splits the feed power to 8 equal-in-phase output quantities. Then eight identical patch elements with good reflection coefficient, high gain and excellent radiation efficiency are connected at the eight-output ports of the 1 × 8-power divider in order to obtain an array antenna consisting of eight radiating elements. A quarter-wave impedance adapter is utilized to obtain a perfect matching of impedance between patches and the power divider. The suggested directional antenna resonates at 2.4[Formula: see text]GHz with good impedance matching via offering reflection coefficient [Formula: see text][Formula: see text]dB and voltage standing wave ratio (VSWR) of 1.16. Moreover, it offers good radiation traits like the enhanced gain of 14.76[Formula: see text]dB, an excellent radiating efficiency equals about 98.86% and a directional radiation pattern. Initially, the proposed planar directional antenna array has been designed and simulated utilizing high-frequency structure simulator (HFSS) EM simulation tool, the results are validated with another simulation tool via computer simulation technology (CST) software. This printed antenna array is a potential candidate to operate at around 2.4[Formula: see text]GHz for RFID reader utilizations due to its outstanding impedance and radiation characteristics.

CONTROL MODEL OF THE WASTEWATER TREATMENT PROCESS

The main stages of biological wastewater treatment are studied, existing models and control of these processes are analyzed, methods for solving the system of differential equations and the disadvantages of each model when used for control purposes are shown. To find the optimal mode using models of the biological treatment process, various criteria are used. The parameters and variables of the process of biological wastewater treatment by microorganisms in aeration tanks are determined - control and exciting influences, as well as input and output quantities, and a system of differential equations connecting them is established. The feasibility of using MatLAB software for building an analytical model and its solution, and Simulink for visual modeling is shown. Keywords: wastewater, model, aero tank, biological treatment, management.

Inverse Data Envelopment Analysis Model to Improve Efficiency by Increasing Outputs

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas.

Capital tax incentives and firm innovation: evidence from accelerated depreciation in China

PurposeThis study aims to investigate the influence and impact mechanism of capital tax incentives on firm innovation.Design/methodology/approachThis study employs the difference-in-differences (DID) method, in conjunction with the exogenous impact of accelerated depreciation (AD) pilot policy. This study selects Chinese listed companies from 2010 to 2017 as the research sample.FindingsFirstly, AD exerts a substantial positive effect on the quantity and quality of the innovation output of firms, and the positive impact results primarily from heightened investment in fixed assets, particularly, machinery and equipment. Secondly, the influence of the policy is pronounced in non-state-owned enterprises, mature enterprises, less capital-intensive enterprises and non-high-tech industries, which all exhibit strong innovation incentives. Lastly, the tax incentive policy significantly stimulates firm innovation in the short term, but its long-term impact on innovation incentives lacks statistical significance.Originality/valueThis study highlights the significance of capital tax incentives in facilitating the innovation process in firms.

Digital transformation and innovation output of manufacturing companies-An analysis of the mediating role of internal and external transaction costs.

Digital transformation, based on digital technologies, has triggered economic growth in many industries and brought about production and service transformation in the manufacturing sector. As an important source of innovation output and a driving force for national economic development, it is of great significance to study the impact of digital transformation on innovation output in manufacturing companies. This study analyzes the effects of digital transformation on the quality, quantity, and overall innovation output of manufacturing companies from both the macro provincial-level digital transformation and micro enterprise-level digital transformation perspectives. Additionally, using data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2022, this study empirically tests the mechanism through which digital transformation affects innovation output from the perspectives of internal transaction costs and external transaction costs. The results show that digital transformation promotes overall improvement in innovation output of manufacturing companies and leads to improvements in both the quality and quantity of innovation output. Furthermore, the study finds that the effect of digital transformation on innovation output has a nonlinear characteristic under different levels of market competitiveness and market freedom. The mediation analysis reveals that the influence of digital transformation on innovation output can be attributed to the reduction of internal transaction costs and the enhancement of external transaction efficiency. In terms of digital policy formulation, it is necessary to coordinate the development of diverse and innovative digital infrastructure at the macro level with the micro-level ecosystems of enterprises, in order to reduce transaction costs within and outside innovative entities. Ultimately, it is essential for the government to foster a conducive free market environment that enhances transaction efficiency and timely regulates the excessive competition resulting from oligopolistic monopolies, thus maximizing the potential of digital transformation in promoting innovation output.

Numerical investigation on thermal performance of a solar greenhouse with synergetic energy release of short- and long-term PCM storage

To address the problem of low greenhouse temperature at late night in north China, a new strategy of synergetic release of short- and long-term PCM heat storage is proposed in this paper, i.e. supercooled salt hydrate units are activated to release seasonally stored latent heat after the organic PCM north wallboard with daily storage cannot provide the required indoor temperature. To achieve this strategy, a microclimate model considering multiple greenhouse elements, including solar radiation, crops, indoor air, water vapor, soil and PCMs, is developed and 3D computational fluid dynamic (CFD) simulations are employed to examine the thermal performances of this strategy under Beijing climate conditions. The results demonstrated that the new strategy promotes the greenhouse temperature suitable for tomato growth (15 °C) throughout the whole night in winter. The organic PCM (Tm = 25 °C) wallboard can reduce the daytime indoor temperature by 0.5–1.5 °C and increase the nighttime indoor temperature by 4–5.5 °C than without PCM wallboard on January 1st. But the greenhouse temperature with this single daily storage is still lower than the required 15 °C after 4:00 a.m. With consideration of the unit numbers and heating effects, triggering two supercooled sodium thiosulfate pentahydrate (STP) units at one time (rather than sodium acetate trihydrate (SAT) and calcium chloride hexahydrate (CCH)) is the reasonable choice for greenhouse heating under present conditions. The system can achieve synergetic energy release across seasons, days and nights, regulate the thermal environment in the greenhouse, and promote the quantity and quality of crop output.

A Global Approach to Modeling Injection Molding.

A problem of modeling plastic injection forming (molding) is presented, including both the plasticizing system of the injection-forming machine and the mold. When modeling the plastic flow in the mold, the input quantities are essentially unknown, e.g., the plastic melt temperature. Thus, a comprehensive (global) model of the injection-forming process is needed for the flow in the plasticizing system and in the mold. The process output quantities from the plasticizing system will be the input quantities for the mold. When modeling the plastic flow in the injection-forming machine, a comprehensive approach should be applied to consider the solid material conveying, material plasticizing, and the material melt flow. The model of material plasticizing is a basis for building such global models. In this research, the effect of the flow (including plasticizing) in the injection-forming machine on the flow in the mold is studied by simulation (using Moldex3D 2023R3OR 64-bit software) and experimentation. These studies are carried out for the injection forming of selected material using a specialized spiral mold. Simulations performed with the use of Moldex3D software for the plasticizing system significantly improved the accuracy of the simulation of the flow in the mold. However, the best results were obtained using experimental data (plastic melt temperature) as input quantities for mold filling simulations. The novel concepts of injection-forming process modeling based on our previous experimentations are also discussed.

Profitability and determinants of output in catfish (Clarias gariepinus) production in South-East, Nigeria

This study investigated the profitability and determinants of output in catfish production in Southeast, Nigeria. The specific objectives were to estimate the costs and returns of catfish production, identify the determinants of output of catfish production, and identify the constraints faced by catfish farmers in the area. Multi-stage and snowballing sampling procedures were adopted in the selection of 384 respondents who participated in the study. The results showed that the gross margin, net income, return on investment, capital turn-over/benefit cost ratio and gross ratio were N452.64, N439.39, 34.4, 1.34 and 0.74 respectively (per kg of fish). In comparison, the total revenue showed a significant difference from the total cost at 1% level of probability indicating a significant profit level. The findings on the production function revealed that only quantity of fingerlings and quantity of fuel had significant impacts on the quantity of output, while the cost function showed that quantities of fingerlings, fuel, labour and feed had significant impacts on the total cost of production. Lastly, high cost of feed, high cost of fuel & poor electricity, insufficient finance/capital, and poor quality of feed ranked 1st to 4th among constraints militating against catfish production in the area.

Analysis of three-phase asymmetrical currents in the secondary voltage of signal change sensors in the power supply system using graph models

This article presents the principles of modeling three-phase asymmetrical currents in the form of secondary voltages of signal change sensors in the power supply system. The classical methods of signal transformation modeling do not provide the necessary accuracy due to the complexity in representing the interdependence between input and output quantities and parameters, as well as the difficulty in capturing the interchain dependencies and associated physical-technical effects. The analysis in this work focuses on the principles of signal transformation based on graph models, including the physical and technical effects of the three-phase primary current electromagnetic sensor that creates the interacting magnetic fields. The modifications to elements with nonlinear parameters and the values of the electric network currents in the power supply system during estimation have been considered as an object with concentrated parameters. In contrast to traditional single-phase current transformers, the complex transducing components, weight, and volume of the items are laborintensive when used in combined power measurement and control systems. They also do not provide the universality of the output value when interfacing with microprocessors and electronic technologies in intelligent systems.

Evaluation of wind resource uncertainty on energy production estimates for offshore wind farms

Wind farm design generally relies on the use of historical data and analytical wake models to predict farm quantities, such as annual energy production (AEP). Uncertainty in input wind data that drive these predictions can translate to significant uncertainty in output quantities. We examine two sources of uncertainty stemming from the level of description of the relevant meteorological variables and the source of the data. The former comes from a standard practice of simplifying the representation of the wind conditions in wake models, such as AEP estimates based on averaged turbulence intensity (TI), as opposed to instantaneous. Uncertainty from the data source arises from practical considerations related to the high cost of in situ measurements, especially for offshore wind farms. Instead, numerical weather prediction (NWP) modeling can be used to characterize the more exact location of the proposed site, with the trade-off of an imperfect model form. In the present work, both sources of input uncertainty are analyzed through a study of the site of the future Vineyard Wind 1 offshore wind farm. This site is analyzed using wind data from LiDAR measurements located 25 km from the farm and NWP data located within the farm. Error and uncertainty from the TI and data sources are quantified through forward analysis using an analytical wake model. We find that the impact of TI error on AEP predictions is negligible, while data source uncertainty results in 0.4%–3.7% uncertainty over feasible candidate hub heights for offshore wind farms, which can exceed interannual variability.

Does China's national volume-based drug procurement policy promote or hinder pharmaceutical innovation?

The national volume-based drug procurement policy initiated in China since 2018 represents a significant reform in China's pharmaceutical distribution system. It has largely squeezed out the price bubble of low-end generic drugs, making competition in the pharmaceutical sales segment more intense and transparent. This policy intervenes in the distribution link of the pharmaceutical industry by intensifying market competition, thereby enhancing the innovation willingness and R&D capabilities of pharmaceutical companies. Taking the national volume-based drug procurement policy as the policy shock, we used the multi-period difference-in-difference method to study the impact of the policy on innovation input, innovation output quantity and innovation output quality of listed pharmaceutical companies and its impact mechanism. We found that the volume-based policy can significantly promote the pharmaceutical companies' innovation input and the innovation output quality, but significantly reduced the innovation output quantity. For innovative and generic drug companies, this policy has limited impact on innovative drug companies, but force generic drug companies to pay more attention to cost control and market positioning, and the quality and cost-effectiveness of R&D output to ensure competitiveness in the market. For bid-winning and non-winning companies, the policy has a greater innovation incentive for non-winning companies than winning companies, by imposing greater survival pressure on non-winning companies, forcing them to increase R&D investment intensity and adopt the innovation strategy of preferring quality to quantity. The results show that the national volume-based drug procurement policy should be expanded to lower drug prices and lighten the medical burden on patients, with enhanced quality and safety supervision. Additionally, it suggests cautious application of such policies to innovative and high-end generic drugs to encourage continued pharmaceutical innovation and industry advancement.

Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature

Abstract Offshore wind turbines intend to take a rapidly growing share in the electric mix. The design, installation, and exploitation of these industrial assets are regulated by international standards, providing generic guidelines. Constantly, new projects reach unexploited wind resources, pushing back installation limits. Therefore, turbines are increasingly subject to uncertain environmental conditions, making long-term investment decisions riskier (at the design or end-of-life stage). Fortunately, numerical models of wind turbines enable to perform accurate multi-physics simulations of such systems when interacting with their environment. The challenge is then to propagate the input environmental uncertainties through these models and to analyze the distribution of output variables of interest. Since each call of such a numerical model can be costly, the estimation of statistical output quantities of interest (e.g., the mean value, the variance) has to be done with a restricted number of simulations. To do so, the present paper uses the kernel herding method as a sampling technique to perform Bayesian quadrature and estimate the fatigue damage. It is known from the literature that this method guarantees fast and accurate convergence together with providing relevant properties regarding subsampling and parallelization. Here, one numerically strengthens this fact by applying it to a real use case of an offshore wind turbine operating in Teesside, UK. Numerical comparison with crude and quasi-Monte Carlo sampling demonstrates the benefits one can expect from such a method. Finally, a new Python package has been developed and documented to provide quick open access to this uncertainty propagation method.

Breast morphology classification method based on generalized neural network

In order to establish a breast shape classification method with general indicators, this paper selects six measurement indicators that can characterize the three-dimensional shape of the breast, such as circumference, length, width, and height. The relevant data of the measurement indicators are used to establish a generalized neural network model, and the generalized neural network is applied to the evaluation of breast shape classification recognition. The MATLAB software toolbox is used, based on training sample data, We have established a generalized neural network (GRNN) model, which involves establishing corresponding functional relationships through training to approximate the training data. After obtaining the function relationship through training, the predicted values were obtained, and the prediction results reflected that the established generalized neural network GRNN model is quite reliable for predicting the category of breast shape. By establishing a mapping relationship between the input quantity of breast feature parameters and the output quantity of categories, training samples can be established. When encountering new test data, these data can also be used as test samples. Based on the established model, appropriate spreads can be adjusted to predict the output quantity, providing relevant personnel with basic and simple basis for judgment and evaluation. It can also provide convenience for customers in various consumption channels Accurate bra size recommendations can help consumers purchase bras that fit well and are comfortable.

Viewing Intentional Teaching Gestures: The Impact on Learners’ Japanese Output

Intentional Teaching Gestures are gestures that have been created as a pedagogical tool for second language teaching and learning and are used in many Japanese programs. Despite their widespread use, little is known about their impact on students’ oral language production. Therefore, the purpose of this study was to explore the effect of Intentional Teaching Gestures on students’ Japanese production. This paper reports on the quality of students’ oral language production from two Story Re-tell tasks, one with and one without viewing Intentional Teaching Gestures (ITG), after learning Japanese as a second language with ITG. Thematic analysis was undertaken of the Story Re-tell transcription data from 170 primary school participants. Findings identify that viewing ITG whilst doing Story Re-tell positively impacted learners’ oral production. The breadth of benefits included increases in the quantity of output that students produced, the expanded informational content included in their stories, and students’ increased use of a range of verbs and particles in their utterances. Viewing ITG also provided support for students’ structuring of utterances, supported self correction, and led to an improvement in their overall oral performance. These benefits have wide ranging pedagogical implications for Japanese teachers and learners.

Algorithmical determination of the equilibrium/stead position of a dynamic system by the finite-step method

The problem of algorithmic estimation of the equilibrium/stationary state of the dynamic system based on the minimum number of measurements of the output quantity at equidistant time points is considered. For this, a finite-step method was used, which consists in the formation, according to certain rules, of the sums and differences of the values of the output quantity, which are located symmetrically on the time axis relative to a certain moment that can be determined a priori. As a result, overdetermined systems of linear equations are formed with respect to the introduced fictitious unknowns, and based on the necessary condition for the existence of a solution to these systems, formulas are found to determine the equilibrium/stationary position of the dynamic system. Algorithms for determining the equilibrium /stationary state for the following mathematical models of the output value are given: in the form of a constant component and a damped exponent, in the form of a constant component and a damped sinusoid, in the form of a constant component and two undamped sinusoids, in the form of a constant component and a damped sinusoid. It is shown that in the absence of measurement errors of the output value, the error in estimating the equilibrium/stationary state of the transient process depends only on the error in solving the system of linear equations. Ways of using the redundant number of measurements are discussed.
 Keywords: equilibrium position, stationary state, dynamic system, mathematical model, finite-step method, numerical algorithm.

Influence of Tools and Cutting Strategy on Milling Conditions and Quality of Horizontal Thin-Wall Structures of Titanium Alloy Ti6Al4V.

Titanium and nickel alloys are used in the creation of components exposed to harsh and variable operating conditions. Such components include thin-walled structures with a variety of shapes created using milling. The driving factors behind the use of thin-walled components include the desire to reduce the weight of the structures and reduce the costs, which can sometimes be achieved by reducing the machining time. This situation necessitates, among other things, the use of new machining methods and/or better machining parameters. The available tools, geometrically designed for different strategies, allow working with similar and improved cutting parameters (increased cutting speeds or higher feed rates) without jeopardizing the necessary quality of finished products. This approach causes undesirable phenomena, such as the appearance of vibrations during machining, which adversely affect the surface quality including the surface roughness. A search is underway for cutting parameters that will minimize the vibration while meeting the quality requirements. Therefore, researching and evaluating the impact of cutting conditions are justified and common in scientific studies. In our work, we have focused on the quality characteristics of horizontal thin-walled structures from Ti6Al4V titanium alloys obtained in the milling process. Our experiments were conducted under controlled cutting conditions at a constant value of the material removal rate (2.03 cm3⁄min), while an increased value of the cut layer was used and tested for use in finishing machining. We used three different cutting tools, namely, one for general purpose machining, one for high-performance machining, and one for high-speed machining. Two strategies were adopted: adaptive face milling and adaptive cylindrical milling. The output quantities included the results of acceleration vibration amplitudes, and selected surface topography parameters of waviness (Wa and Wz) and roughness (Ra and Rz). The lowest values of the pertinent quantities were found for a sample machined with a high-performance tool using adaptive face milling. Surfaces typical of chatter vibrations were seen for all samples.