This study aimed at the investigation of the effect of substrate preheating on residual stress in laser powder bed fusion using a physics-based analytical model. In this study, an analytical model is proposed to predict the residual stress through the calculation of preheating affected temperature profile and thermal stress. The effect of preheating is super-positioned with initial temperature in the modeling of temperature profile using a moving heat source approach; the resultant temperature gradient is then employed to predict the thermal stress from a point body load approach. If the thermal stress exceeds the yield strength of the material, then the residual stress under cyclic heating and cooling will be calculated based on the incremental plasticity and kinematic hardening behavior of metal. IN718 is used as a material example to pursue this investigation. To validate the predicted residual stress, experimental measurements are conducted using X-ray diffraction on IN718 samples manufactured via laser powder bed fusion under different process conditions. Results showed that preheating of the substrate could reduce the residual stress in an additively manufactured part due to the reduction in temperature gradient and resultant shrinkage stresses. However, the excessive preheating could have an opposite impact on residual stress accumulation. Moreover, the results confirm that the proposed model is a valuable tool for the prediction of residual stress- eliminating the costly experiments and time-consuming finite element simulations.

There are ~200.000 households living in the detached houses and ger (yurt) with the small-coal stove which burns a raw coal in the Ulaanbaatar city. The proper heating system and improvement on the energy efficiency of residential dwellings is vital important for Ulaanbaatar city to reduce an air pollution as well as for the operation of current central energy system. This study shows the experimental results of two Gers with two different heating system and different thermal insulation. Technical availability of the system consisting of ETS heater with daytime charging schedule and areal PV system is also examined by using a simulation of MATLAB programming. As a result of the experiment, the indoor comfort level and energy efficiency of the Ger with added insulation and ETS heater with nighttime charging was enhanced to compare with the reference Ger. The Ger with added insulation and ETS heater consumed 3169 kWh for electric appliances and 5989 kWh for the heating season. The simulation was resulted that the ETS heater to be installed in the Ger 2 had prospected to increase the PV self-consumption rate by 76% because of the daytime charging schedule of ETS heater. The PV system supplied 31% of the total energy consumption with remained 69% of the main grid.

Panic disorder (PD) is characterized by recurrent and uncontrollable panic attacks associated with behavioral changes and/or persistent anxiety due to the attacks. The development of behavioral models in animals is important for the understanding of the psychobiological and behavioral bases of PD. The present article reviews the main models used in the current literature. Biobehavioral assays used in rats and mice include fear conditioning (which presents moderate predictive, face, and construct validities); the elevated T-maze (which presents good predictive validity, but low face and construct validities); electrical stimulation of the periaqueductal gray (which presents good face validity, but moderate construct validity); predator exposure models (which present good predictive and moderate construct validity); and hypercapnia-induced responses (which present moderate construct validity). These three approaches seek coherence with theories on fear as a way to increase its translational potential; thus, while the elevated T-maze is supported by the Deakin/Graeff theory, the mouse defense test battery relies on the concept of defensive distance, and periaqueductal gray stimulation is based on the functional neuroanatomy of fear. Moreover, to higher or lower degree the three models are supported by an “etho-experimental” approach, with careful observation of animal behavior as a way of discriminating different defensive strategies that model different aspects of anxiety, fear, and panic. These assays can be used, in conjunction with independent variables that attempt to simulate the vulnerabilities and stressors which lead to panic attacks, to produce true models of PD. Finally, an alternative/complementary model is proposed that uses zebrafish alarm reaction to study this disorder.

This paper introduces an innovative framework for the integration of artificial empathy into robot swarms to enhance communication and cooperation. The proposed model relies on fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. Utilizing similarity measures, the model compares states, enabling empathetic reasoning for synchronized swarm behavior. A practical application example demonstrates the model’s efficacy in a robot swarm striving for a common goal. The evaluation methodology involves the Open Source Physical-based Experimentation Platform (OPEP), emphasizing empirical validation in real-world scenarios. The paper proposes a transitional environment that allows automated and repeatable execution of experiments on a swarm of robots using physical devices.

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

Unsteady flow is the most common and complicated form of fluid motion in nature. This paper takes the beaches of key waterways in the middle and lower reaches of the Yangtze River as the research object, analyzes the water surface distribution and average flow velocity distribution near the beach, and analyzes the differences. The relationship between the flow pattern of the steep slope and the water flow pattern of the entire survey area and the steep slope of the beach and its nearby water flow structure. The water flow near the beach is divided into four major blocks: the swelling water area, the falling water area, the backwater area and the maximum flow rate area. The water flow structure characteristics and the cause of each block are analyzed.

Precision agriculture and open-source data repositories provide a plethora of field-specific ecological data about agroecosystems, but few mechanisms have been developed to turn that information into management recommendations for crop production. The On-Farm Precision Experiments (OFPE) framework is an agroecological model-based methodology to improve crop manager’s abilities to make field-scale agronomic input decisions. This work evaluates the use of field-specific experiments that employ open-source data and the data emanating from precision agriculture technologies to gain local knowledge of the spatial and temporal variability in agroeconomic performance at the sub-field scale. Quantification of the temporal variability in crop response to inputs (e.g., crop seeding rates, crop rotations, fertilizers, other soil amendments, pesticides, etc.) allows for estimation of the probability that a future management scenario will outcompete another, in terms of crop yield, crop quality, farmer net return, or environmental quality. The challenge is to integrate OFPE into applied management with minimal disruption of stakeholder practices while drawing on historic knowledge about the field and economic constraints. OFPE is the basis of a decision support system that includes a six-step cyclical process that harnesses precision agriculture technology to apply experiments and gather field-specific data, incorporates modern data management and analytical approaches, and generates management recommendations as probabilities of outcomes. The OFPE framework allows field managers to assess the tradeoffs in agronomic input management between the maximization of crop production, quality and profits from production while considering environmental effects.