Research to establish ecological standards for water resources developments.

Three levels of development were identified to represent the environmental condition of the region under consideration. The least disturbed environments are designated as level I areas to which the strictest ecological standards is assigned to preserve and protect natural environment. The level II areas are designated by an intermediate level of human involvement in the ecological communities. Areas where large concentrations of population are located are designated as level III environments and the standards applied to these areas should be more tolerant of pollutants in the ecological systems.It is not intended that these four indicators be exhaustive; users should feel free to employ additional indicators for their study area. All the estimates of the indicator are transformed into their corresponding indicator performance level by comparing it with the maximum value in the region. They are then weighted in proportion to their relative importance.The system to establish ecological standards for water resources developments has been developed in this study. Two steps were undertaken to establish ecological standards. The first one is the categorization of development levels of natural environment by measuring specific socio-economic factors which are capable of delineating the human modification of the ecological standards in response to various development levels so as to reflect the human influence on the ecological system.Three different scales (X-axis) were proportionally developed for each of the twelve parameters reflecting the three levels of restriction. Formulas were also developed for calculating the parameter estimates. The parameter estimate obtained can be applied to the appropriate scale of a study region whose level of development was previously determined from its socio-economic evaluation. By interpolation, the ecological performance of a parameter in a study region can be derived.The validation of the methodology is accomplished by using data from the Mid-Arkansas River Basin, including Pawnee, Creek, Tulsa, and Osage counties of Oklahoma.Four socio-economic indicators were proposed to quantitatively measure the degree of human involvement in the natural environment: inhabitance index, land value, intensity of water use, and transportation facility. Inhabitance index is an indicator that explains the development level in terms of population size and settlement pattern. It is defined as the result of total population density multiplied by rural population density. Average land value (in dollars per acre) is a significant indicator of the development level of a region because the higher the development level, the more people compete for land, thus, giving rise to higher land values. The indicator of intensity of water use is expressed in terms of domestic, municipal and industrial water use (in acre-feet/sq mile-year). Because the inadequacy of transportation facilities presents great limitations to land resources development, transportation facilities (miles of streets and highways per acre) can be a significant indicator.Twelve parameters were employed to assess the ecological performance of the natural environment. For each parameter, a parameter function graph was developed, which illustrates the relationship between the parameter estimate (X-axis) and ecological performance (Y-axis). A measuring scale was set up to determine how well each parameter performs. This scale ranges from -1 to 0 and from 0 to +1. A zero value is designated as the standard value, while -1 and +1 represent the extremely poor and excellent performances respectively.The indicator performance levels are multiplied by their relative weights to obtain the weighted indicator performance levels which are then summed to obtain the development level estimates. The cutoff points on these development level estimates are determined to differentiate level I, II, and III regions

The role of electron and phonon temperatures in the helicity-independent all-optical switching of GdFeCo

Ultrafast optical heating of the electrons in ferrimagnetic metals can result in all-optical switching (AOS) of the magnetization. Here we report quantitative measurements of the temperature rise of GdFeCo thin films during helicity-independent AOS. Critical switching fluences are obtained as a function of the initial temperature of the sample and for laser pulse durations from 55 fs to 15 ps. We conclude that non-equilibrium phenomena are necessary for helicity-independent AOS, although the peak electron temperature does not play a critical role. Pump-probe time-resolved experiments show that the switching time increases as the pulse duration increases, with 10 ps pulses resulting in switching times of ~sim 13 ps. These results raise new questions about the fundamental mechanism of helicity-independent AOS.Comment: 18 pages, 6 figures and supplementary material

Prompt Tuning for Generative Multimodal Pretrained Models

Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining. In this work, we explore the transfer of prompt tuning to multimodal pretraining, with a focus on generative multimodal pretrained models, instead of contrastive ones. Specifically, we implement prompt tuning on the unified sequence-to-sequence pretrained model adaptive to both understanding and generation tasks. Experimental results demonstrate that the light-weight prompt tuning can achieve comparable performance with finetuning and surpass other light-weight tuning methods. Besides, in comparison with finetuned models, the prompt-tuned models demonstrate improved robustness against adversarial attacks. We further figure out that experimental factors, including the prompt length, prompt depth, and reparameteratization, have great impacts on the model performance, and thus we empirically provide a recommendation for the setups of prompt tuning. Despite the observed advantages, we still find some limitations in prompt tuning, and we correspondingly point out the directions for future studies. Codes are available at \url{https://github.com/OFA-Sys/OFA}Comment: Work in progres