Physical Inputs Research Articles

The Effects of Different Tillage Techniques on the Production Costs in Fallow-Wheat Rotation System

The adoption and applicability of the technologies developed in agriculture by farmers are very important for agronomic studies. There are a limited number of economic studies comparing different tillage methods with each other. This study was conducted in the land of Polatlı Agricultural Enterprise between 2011 and 2017 to determine the amounts of physical production inputs and unit production costs of three different tillage operations in the fallow-wheat rotation system. The data consist of the production inputs in the Conventional Tillage, Minimum Tillage, and Direct Seeding practices. For the economic analysis, Single Product Budget Analysis was used. All the farmers’ production costs in the fallow-wheat rotation system were considered in the analysis. Among the practices, Gross Production Value (GPV) was obtained the most from the (509.63$ ha-1) practice. This practice was followed by Direct Seeding (486.13$ ha-1) and Minimum Tillage (421.92$ ha-1) practices, respectively. The profit threshold was exceeded in all practices, and production activities’ variable and total costs were fulfilled. The highest relative profit rate was obtained in Direct Seeding (1.77) practice, and it was followed by Conventional Tillage (1.30) and Minimum Tillage (1.08) practices, respectively. In order to obtain an income of $1 in wheat production, the lowest production cost was obtained in the Direct Seeding practice. With the Direct Seeding application, it can be ensured that economic wheat production, preservation of soil structure, reduction of input costs, and less economic damage to the farmer in changing climate conditions can be achieved.

Endothelial Mechanosensors for Atheroprone and Atheroprotective Shear Stress Signals.

Vascular endothelial cells (ECs), derived from the mesoderm, form a single layer of squamous cells that covers the inner surface of blood vessels. In addition to being regulated by chemical signals from the extracellular matrix (ECM) and blood, ECs are directly confronted to complex hemodynamic environment. These physical inputs are translated into biochemical signals, dictating multiple aspects of cell behaviour and destination, including growth, differentiation, migration, adhesion, death and survival. Mechanosensors are initial responders to changes in mechanical environments, and the overwhelming majority of them are located on the plasma membrane. Physical forces affect plasma membrane fluidity and change of protein complexes on plasma membrane, accompanied by altering intercellular connections, cell-ECM adhesion, deformation of the cytoskeleton, and consequently, transcriptional responses in shaping specific phenotypes. Among the diverse forces exerted on ECs, shear stress (SS), defined as tangential friction force exerted by blood flow, has been extensively studied, from mechanosensing to mechanotransduction, as well as corresponding phenotypes. However, the precise mechanosensors and signalling pathways that determine atheroprone and atheroprotective phenotypes of arteries remain unclear. Moreover, it is worth to mention that some established mechanosensors of atheroprotective SS, endothelial glycocalyx, for example, might be dismantled by atheroprone SS. Therefore, we provide an overview of the current knowledge on mechanosensors in ECs for SS signals. We emphasize how these ECs coordinate or differentially participate in phenotype regulation induced by atheroprone and atheroprotective SS.

Effects of climate change on paddy expansion and potential adaption strategies for sustainable agriculture development across Northeast China

Sustainably feeding an increasing population is a grand challenge in the context of climate change and rapid urbanization. Over the past decades, there has been a remarkable trend in paddy expansion into the mid-high latitude regions of Northeast Asia, especially in China. Yet, knowledge on paddy expansion patterns and corresponding responses to climate change and socioeconomic factors are limited. To bridge this gap, satellite-based land use data for 1990, 2000, 2010 and 2018 at 30-m resolution, as well as historical and projected climatic data (including ERA5 data for 1982–2019 and GCMs data of CMIP5 for 2006–2050) and diverse socioeconomic data, were utilized to investigate patterns and quality changes of paddy fields across Northeast China. Results showed that the 0 °C isotherm moved northward by 270 km during 1961–2000 and 190.5 km during 2000–2019. Net paddy area expanded by 2.6 × 104 km2 in Northeast China during 1990–2018; in Heilongjiang Province, paddy area dramatically expanded by about 4.0 × 104 km2 during 1958–2018, of which 1.8 × 104 km2 and 2.2 × 104 km2 increment in 1958–2000 and 2000–2018, respectively. Even though climate hiatus occurred around 2010, the expansion rate of paddy area in Northeast China was up to 1.76 × 103 km2/a during 2010–2018. In addition to warming climate, improved agricultural technologies and physical inputs, increasing market demands, and agricultural policy implement were largely responsible for the paddy expansion in Northeast China. However, continuously cultivated paddy fields had generally more suitable natural conditions and better infrastructure than newly cultivated paddy fields. This study provides some potential suggestions for the sustainable development of modern agriculture in Northeast China.

Content of physical activity documentation in Canadian family physicians' electronic medical records.

The purpose of this study was to examine the content of physical activity inputs in Canadian family physician electronic medical records. Of 1 225 948 patients aged 18-64years, a sample of 1535 patients' charts were reviewed. A minority (n = 148; 9.6%) of patients had at least 1 mention of physical activity at any time. Insufficient information existed to determine physical activity domain (21.6%), purpose (50.0%), or meeting of guidelines (98.1%). Novelty: This study examines the physical activity content of what Canadian family physicians document in their electronic medical records.

Tuning the Consonance of Microscopic Neuro-Cardiac Interactions Allows the Heart Beats to Play Countless Genres.

Different from skeletal muscle, the heart autonomously generates rhythmic contraction independently from neuronal inputs. However, speed and strength of the heartbeats are continuously modulated by environmental, physical or emotional inputs, delivered by cardiac innervating sympathetic neurons, which tune cardiomyocyte (CM) function, through activation of β-adrenoceptors (β-ARs). Given the centrality of such mechanism in heart regulation, β-AR signaling has been subject of intense research, which has reconciled the molecular details of the transduction pathway and the fine architecture of cAMP signaling in subcellular nanodomains, with its final effects on CM function. The importance of mechanisms keeping the elements of β-AR/cAMP signaling in good order emerges in pathology, when the loss of proper organization of the transduction pathway leads to detuned β-AR/cAMP signaling, with detrimental consequences on CM function. Despite the compelling advancements in decoding cardiac β-AR/cAMP signaling, most discoveries on the subject were obtained in isolated cells, somehow neglecting that complexity may encompass the means in which receptors are activated in the intact heart. Here, we outline a set of data indicating that, in the context of the whole myocardium, the heart orchestra (CMs) is directed by a closely interacting and continuously attentive conductor, represented by SNs. After a roundup of literature on CM cAMP regulation, we focus on the unexpected complexity and roles of cardiac sympathetic innervation, and present the recently discovered Neuro-Cardiac Junction, as the election site of “SN-CM” interaction. We further discuss how neuro-cardiac communication is based on the combination of extra- and intra-cellular signaling micro/nano-domains, implicating neuronal neurotransmitter exocytosis, β-ARs and elements of cAMP homeostasis in CMs, and speculate on how their dysregulation may reflect on dysfunctional neurogenic control of the heart in pathology.

Technical progress and induced innovation in China: a variable profit function approach

We propose a new methodology to estimate empirically the input price-induced technical change and total factor productivity (TFP) growth in China. Our primary goal is to test Hicks’ induced innovation hypothesis by examining whether technical change in China has been induced by sharp increase in input prices that have accompanied its rapid economic growth. Utilizing the idea of a firm’s two-stage optimization problem, we develop a new parametric form of the variable profit function wherein the derived input demand and output supply functions can be easily constrained to be regular, and the functional structure is parsimonious in the number of parameters. Applying this methodology to Chinese time series data for 1986–2017, we find that not only is wage-induced innovation significant and quantitatively important, but also that it substantially buffered a decline in TFP growth before 2006 that would otherwise be substantial. Overall, we conclude that China’s economic growth is predominantly driven by wage-induced innovation along with massive injection of heavily subsidized physical inputs in public works and huge investment in industrial sectors.

Critical factors for the use of machine learning to predict lake surface water temperature

Models based on Machine Learning (ML) are pervading all fields of science and practical applications, including the problem of forecasting water temperature in lakes, a crucial variable for ecosystems and a proxy of climate change. Here, we review the most used ML algorithms in this field and highlight some physical constraints that should be carefully considered when adopting a black-box approach. To illustrate them, we refer to an artificial case study representing a temperate lake simulated by means of a physically based model, for which we take full control of input and output variables, and restrict the analysis to lake surface water temperature (LSWT). Three main factors are relevant for a successful prediction of LSWT by means of ML models: the choice of the predictors (mostly, meteorological variables), their pre-processing (we tested three approaches), and the specific ML algorithm (nine different algorithms). We show that selecting the suitable physical inputs plays the most important role. In our case study, which is the product of a numerical model and not a real lake, the minimum amount of information that is needed to obtain acceptable results is to consider air temperature (AT) and day of the year. The use of additional predictors does not substantially improve the performances (the relative improvement of RMSE was 7.75% for the test data set). We also demonstrate that better results than the normal case are obtained by either pre-processing air temperature data averaging them over a time window or including values from previous days as inputs in the model. Considering the recent history of the forcing (AT) allows one to comply with the physical fact that the large water mass makes lakes acting as “filters” in their thermal response (thus, influenced also by AT from previous days), which changes depending on the lake’s depth. Eventually, we did not find a definite answer about a single optimal ML algorithm when using the same inputs (although artificial neural network had slightly better results), suggesting that the insight into the physical dynamics is still the most important factor for a successful exploitation of ML.

Controlling Synthetic Cell-Cell Communication.

Synthetic cells, which mimic cellular function within a minimal compartment, are finding wide application, for instance in studying cellular communication and as delivery devices to living cells. However, to fully realise the potential of synthetic cells, control of their function is vital. An array of tools has already been developed to control the communication of synthetic cells to neighbouring synthetic cells or living cells. These tools use either chemical inputs, such as small molecules, or physical inputs, such as light. Here, we examine these current methods of controlling synthetic cell communication and consider alternative mechanisms for future use.

Nonlinear analysis of open-chain flexible manipulator with time-dependent structure

• Nonlinear dynamics analysis of time-varying structure are investigated. • The equations derived by using Hamilton’s principle. • The distinct bifurcation parameters of force & torque inputs have considered. • The effects of excitation frequency, damping coefficient have studied. The analysis of motion equations of flexible-link manipulators indicates that nonlinear terms and behavioral instability exist in their outcomes. In this system, when the differential equations possess structural time-dependent parameters, there is a higher chance for system instability, especially chaos in comparison to time-invariant ones. It is a point for abnormal behavior that is unpredictable and always accompanied by damages. Therefore, the physical parameters and system inputs that cause chaotic responses should be detected and prevented as much as possible. To this aim, the dynamic equations of flexible link manipulator with revolute-prismatic joints as a case of time-variant dynamic system are obtained by Hamilton’s principle with the assumption of nonlinear strains. The discretized motion equations are solved and the results are presented in the forms of bifurcation diagrams (for variation of torque/force amplitude), Poincaré maps, phase-plane portraits, and the largest Lyapunov exponent. Finally, the obtained results are validated with the aid of the experimental setup. The results indicate that although the system response in low torque/force amplitudes is subharmonic (amplitude of 0.01) and quasi-periodic (amplitudes of 0.02–0.03), it becomes quasi-periodic/chaotic with a mild slope with respect to time in high amplitude values. Moreover, since the behavior of modal generalized coordinate is different from the rigid ones, the quasi-periodic and chaotic vibrations do not hurt the joints.

Integration of Multisource Data to Estimate Downward Longwave Radiation Based on Deep Neural Networks

Downward longwave radiation (DLR) at the surface is a key variable of interest in fields, such as hydrology and climate research. However, existing DLR estimation methods and DLR products are still problematic in terms of both accuracy and spatiotemporal resolution. In this article, we propose a deep convolutional neural network (DCNN)-based method to estimate hourly DLR at 5-km spatial resolution from top of atmosphere (TOA) brightness temperature (BT) of the Himawari-8/Advanced Himawari Imager (AHI) thermal channels, combined with near-surface air temperature and dew point temperature of ERA5 and elevation data. Validation results show that the DCNN-based method outperforms popular random forest and multilayer perceptron-based methods and that our proposed scheme integrating multisource data outperforms that only using remote sensing TOA observations or surface meteorological data. Compared with state-of-the-art CERES-SYN and ERA5-land DLR products, the estimated DLR by our proposed DCNN-based method with physical multisource inputs has higher spatiotemporal resolution and accuracy, with correlation coefficient (CC) of 0.95, root-mean-square error (RMSE) of 17.2 W/m², and mean bias error (MBE) of -0.8 W/m² in the testing period on the Tibetan Plateau.

INVERSE ANALYSIS WITH VARIATIONAL AUTOENCODERS: A COMPARISON OF SHALLOW AND DEEP NETWORKS

Inverse problems are applied to determine unknown properties by matching observational data with a physical model that often takes many parameters as input. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented involving regularization with a technique known as a variational autoencoder (VAE), which is trained to map a high-dimensional parameter space with a complex structure to a low-dimensional latent space with a simple structure. We apply this approach to unconditioned realizations of the parameters (heterogeneous hydraulic fields) for a hydrogeological inverse problem. Two types of hydraulic conductivity fields are used to evaluate the characterization for the different levels of heterogeneity complexity of the physical inputs. This approach keeps the computational cost of generating the training data low. The reason is unconditioned realizations neither rely on the observational data used to perform the inverse analysis nor require any groundwater flow model forward runs. In addition, this approach applies regularization on a low-dimensional latent space from the VAE and increases optimization efficiency through automatic differentiation. Furthermore, two different neural network (NN) structures are tested for their utility in using the VAE for inverse analysis. The performance of a deep, convolutional neural network strongly depends on the dimensionality of the latent space, which requires tuning. In contrast, a shallow, dense neural network provides consistently accurate characterization without tuning. Our approach evaluates the advantages of the shallow, dense neural network over the deep, convolutional one and enables future application to a wide range of inverse problems.

Income inequality in China 1952-2017: persistence and main determinants

Research background: China's economic growth, however remarkable, is due to the Harrod-Domar nature of economic growth and, therefore, limited. The main limitation lies in the extension of the neoclassical growth model and the government need to decrease regional disparities using new migration, urbanization and social policy.
 Purpose of the article: It is the rising regional disparity in the total factor productivity to cause the income inequality increase (measured by GINI index) in China from 1952?2017. Our paper brings new insight into the main inequality determinants and causes in China, using a fractional integration modeling framework.
 Methods: Using fractional integration, we find total factor productivity (TFP), real gross domestic product per capita and growth and expenditures for the social safety net and employment effort to have a statistically significant impact on GINI. Income inequality in China is of a persistent nature with the effects of the shocks affecting the GINI index enduring over time.
 Findings & value added: The results of this study highlight the importance for model/policy changes by the policy makers and practitioners in China to deal with the inequality issue. This involves improving the growth model through innovation and technological advancement, relaxing TFP dependence on the physical inputs (labor and capital) to reduce income inequality.

Sensitivity of wheat yields to rise in growing season temperature: Evidences from panel data analysis


 The impact of climate change on agriculture is well studied yet there is scope for improvement as crop specific and location specific impacts need to be assessed realistically to frame adaptation and mitigation strategies to lessen the adverse effects of climate change. Many researchers have tried to estimate potential impact of climate change on wheat yields using indirect crop simulation modeling techniques. Here, this study estimated the potential impact of climate change on wheat yields using a crop specific panel data set from 1981 to 2010,for six major wheat producing states. The study revealed that 1°C increase in average maximum temperature during the growing season reduces wheat yield by 3 percent. Major share of wheat growth and yield (79%) is attributed to increase in usage of physical inputs specifically fertilizers, machine labour and human labour. The estimated impact was lesser than previously reported studies due to the inclusion of wide range of short-term adaptation strategies to climate change. The results reiterate the necessity of including confluent factors like physical inputs while investigating the impact of climate factors on crop yields.

Dynamics of history-dependent perceptual judgment

Identical physical inputs do not always evoke identical percepts. To investigate the role of stimulus history in tactile perception, we designed a task in which rats had to judge each vibrissal vibration, in a long series, as strong or weak depending on its mean speed. After a low-speed stimulus (trial n − 1), rats were more likely to report the next stimulus (trial n) as strong, and after a high-speed stimulus, they were more likely to report the next stimulus as weak, a repulsive effect that did not depend on choice or reward on trial n − 1. This effect could be tracked over several preceding trials (i.e., n − 2 and earlier) and was characterized by an exponential decay function, reflecting a trial-by-trial incorporation of sensory history. Surprisingly, the influence of trial n − 1 strengthened as the time interval between n − 1 and n grew. Human subjects receiving fingertip vibrations showed these same key findings. We are able to account for the repulsive stimulus history effect, and its detailed time scale, through a single-parameter model, wherein each new stimulus gradually updates the subject’s decision criterion. This model points to mechanisms underlying how the past affects the ongoing subjective experience.

Extremum-seeking control integrated online input selection with application to a chilled-water plant

Extremum Seeking Control (ESC) is a model-free control solution for real-time optimization of system operation where model acquisition is difficult and/or cost prohibitive,. For many HVAC and refrigeration systems, there can be a large number of candidate inputs for ESC design, however, some inputs affect the performance measure to a greater degree than others. This paper presents an online input selection method for multivariable ESC, which uses a singular value decomposition (SVD) analysis coupled with a dither-demodulation based online Hessian estimate for the underlying static map. A subset of physical inputs or a new set of inputs via linear combination of the physical inputs can be determined using the proposed approach. We present an analysis for quantifying the loss bound of achievable optimum output with the underlying input selection. Further, the Hessian estimation error bound is quantified with perturbation analysis. The proposed method is evaluated with Modelica simulation models of chilled-water plants, one with a single chiller and the other with two parallel chillers. The simulation results validate the effectiveness of the proposed method of input selection.

Protean Kinematics: A Blended Model of VR Physics.

Avatar research largely focuses on the effects of the appearance and external characteristics of avatars, but may also warrant further consideration of the effects of avatar movement characteristics. With Protean kinematics, we offer an expansion the avatar-user appearances-based effects of the Proteus Effect to a systematic exploration into the role of movement in affecting social perceptions (about others) and idealized perceptions (about self). This work presents both a theoretical (typology) and methodological (physics-based measurement) approach to understanding the complex blend of physical inputs and virtual outputs that occur in the perceptual experience of VR, particularly in consideration of the collection of hippocampal (e.g., place cells, grid cells) and entorhinal neurons (e.g., speed cells) that fire topologically relative to physical movement in physical space. Offered is a novel method that distills the blend of physical and virtual kinematics to contribute to modern understandings of human-agent interaction and cognitive psychology.

Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network.

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

Does credit utilization lead to increasing farm outcome? a micro-perspective of tea production from Rwanda

Credit is a crucial factor for tea growers to pay for physical farm inputs mainly input fertilizers, research and development of high yielding tea clones and labour in order to improve the production of green tea leaf and to meet factories’ demand for raw materials. However, mismanagement of accessed credits by farmers has been reported among the snags affecting the sector development. The study analyzed the determinants and impact of credit utilization on farm income among smallholder tea growers in Nyaruguru District, Rwanda. Crosssectional tea household level data were collected from 358 farmers randomly selected from tea cooperatives. The credit utilization and causal effect were estimated using the Endogenous Switching Regression model. Results showed a positive and significant relationship between credit utilization and tea farm income. Precisely, the causal effect of credit is a 7% increase in tea income for farmers who utilised credit for tea production, while its potential effect is up to a 55% decrease in tea income for those who divert credit for out-off tea production uses. Furthermore, training on good agricultural practices and credit management, cost of farm inputs, labour and access to group credit significantly influence utilization of credit for tea production. However, the size of credit (cash) and off-farm businesses significantly increase the diversion of credit and level of tea farm income. Tea farmers are encouraged to use tea credits for planned projects. Sensitizing farmers to procure farm input fertilizers in bulk through cooperatives should be vigorously pursued to discourage credit diversion.
 Key words: tea credits, tea farming households, farm income, endogenous switching regression

The economic value of sustainable soil management in arable farming systems – A conceptual framework

Soil quality is an important determinant of agricultural productivity, farm resilience and environmental quality. Despite its importance, the incorporation of sustainable soil management in economic models is lacking. This study approaches farmers as decision makers on soil management. Sustainable soil management may be an investment that goes at the expense of short-term returns but increases future soil quality. Hence, the key problem is economic: establishing long-term sustainable soil management at a minimized loss of income. In this study, we define the Economic Value of Land (EVL) as the cumulative returns of a piece of land over a period in time. Maximum long-term EVL is obtained if a soil’s potential is maximally utilized in a sustainable way. From this follows that the Economic Value of Sustainable soil Management (EVSM) is defined as the difference between a sustainable and unsustainable EVL. To acquire a fundamental understanding of EVSM, agronomic and technical factors must be integrated with economics. Production management, the complete set of physical and non-physical inputs is the primary determinant of future soil quality and hence EVL. Maximizing EVL first requires a fundamental understanding of soil quality management: What are the properties of soil quality and how are these influenced by crop production? Subsequently, production management has to be organized in such a way EVL is maximized. This study provides an overview of soil quality management and crop production management linked to economics. The framework provides a qualitative blueprint for bio-economic modelling and a basis for policies to enhance sustainable soil management.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.