Empirical Analysis of Wind Power Potential at Multiple Heights for North Dakota Wind Observation Sites

Wind speed is the most critical factor that determines wind power potential and generation. In this paper, the wind speed data of multiple years from various observation sites in North Dakota, U.S. was analyzed to assess the wind power potential. The study first applied probability density functions (PDFs) to characterize the wind speed data and fit the distributions at various heights for each observation site. The fitted distributions were then used to estimate the wind power potential based on the theoretical cubic power relationship between energy potential and wind speed. Due to the complexity of functions, the numerical integration approach was employed. The following major findings were obtained from this empirical study: (1) Weibull distribution is not always the best function to fit wind speed data, while gamma and lognormal distributions produce better fitting in many occasions; (2) For different height levels at one observation site, the best performing distributions may be different; (3) The estimation accuracies of wind energy potential based on the fitted wind speed distributions range from -4% to 3.8%; (4) The rank of energy potential estimation accuracies is not always consistent with that of goodness-of-fit for wind speed distributions. In addition, a simplified approach that only relies on the hourly mean wind speed to estimate wind power potential is evaluated. Based on the theoretical cubic relationship for wind power estimation, it was found that the simplified approach may provide significantly lower estimates of wind power potential by 42-54%. As such, this approach will become more practical if this amount of difference is to be compensated.Key words: Wind speed; Distribution; Goodness-of-fit; Wind power potential; North Dakot

Qiliqiangxin Affects L Type Current in the Normal and Hypertrophied Rat Heart

Qiliqiangxin capsule is newly developed Chinese patent drug and proved to be effective and safe for the treatment of patients with chronic heart failure. We compared the effects of different dose Qiliqiangxin on L type Ca2+ current between normal and hypertrophied myocytes. A total of 40 healthy Sprague—Dawley rats were used in the study. The rats were randomly divided into two groups (control group and hypertrophy group). Cardiac hypertrophy was induced by pressure overload produced by partial ligation of the abdominal aorta. The control group was the sham-operated group. After 1 month, cardiac ventricular myocytes were isolated from the hearts of rats. Ventricular myocytes were exposed to 10 and 50 μmol/L Qiliqiangxin, and whole cell patch-clamp technique was used to study the effects of Qiliqiangxin on . The current densities of were similar in control group and in hypertrophy group . They were not statistically significant. 10 and 50 μmol/L Qiliqiangxin can decrease peak current and in control group. However, the peak current was only reduced by 50 μmol/L Qiliqiangxin in hypertrophied myocytes. The inhibited action of Qiliqiangxin on of hypertrophy group was lower than in control group. Qiliqiangxin affected L-type Ca2+ channel and blocked , as well as affected cardiac function finally. Qiliqiangxin has diphasic action that is either class IV antiarrhythmic agent or the agent of effect cardiac function

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance to unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. To do so, we employ the AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart review and DDXPlus medical diagnosis datasets for OOD evaluation. We select several popular foundation models as baselines. Results show that ChatGPT shows consistent advantages on most adversarial and OOD classification and translation tasks. However, the absolute performance is far from perfection, which suggests that adversarial and OOD robustness remains a significant threat to foundation models. Moreover, ChatGPT shows astounding performance in understanding dialogue-related texts and we find that it tends to provide informal suggestions for medical tasks instead of definitive answers. Finally, we present in-depth discussions of possible research directions.Comment: Technical report; code is at: https://github.com/microsoft/robustlear

High-performance potassium poly(heptazine imide) films for photoelectrochemical water splitting

Photoelectrochemical (PEC) water splitting is an appealing approach by which to convert solar energy into hydrogen fuel. Polymeric semiconductors have recently attracted intense interest of many scientists for PEC water splitting. The crystallinity of polymer films is regarded as the main factor that determines the conversion efficiency. Herein, potassium poly(heptazine) imide (K-PHI) films with improved crystallinity were in situ prepared on a conductive substrate as a photoanode for solar-driven water splitting. A remarkable photocurrent density of ca. 0.80 mA cm-2 was achieved under air mass 1.5 global illumination without the use of any sacrificial agent, a performance that is ca. 20 times higher than that of the photoanode in an amorphous state, and higher than those of other related polymeric photoanodes. The boosted performance can be attributed to improved charge transfer, which has been investigated using steady state and operando approaches. This work elucidates the pivotal importance of the crystallinity of conjugated polymer semiconductors for PEC water splitting and other advanced photocatalytic applications

Development and validation of a risk score model for predicting autism based on pre- and perinatal factors

BackgroundThe use of pre- and perinatal risk factors as predictive factors may lower the age limit for reliable autism prediction. The objective of this study was to develop a clinical model based on these risk factors to predict autism.MethodsA stepwise logistic regression analysis was conducted to explore the relationships between 28 candidate risk factors and autism risk among 615 Han Chinese children with autism and 615 unrelated typically developing children. The significant factors were subsequently used to create a clinical risk score model. A chi-square automatic interaction detector (CHAID) decision tree was used to validate the selected predictors included in the model. The predictive performance of the model was evaluated by an independent cohort.ResultsFive factors (pregnancy influenza-like illness, pregnancy stressors, maternal allergic/autoimmune disease, cesarean section, and hypoxia) were found to be significantly associated with autism risk. A receiver operating characteristic (ROC) curve indicated that the risk score model had good discrimination ability for autism, with an area under the curve (AUC) of 0.711 (95% CI=0.679-0.744); in the external validation cohort, the model showed slightly worse but overall similar predictive performance. Further subgroup analysis indicated that a higher risk score was associated with more behavioral problems. The risk score also exhibited robustness in a subgroup analysis of patients with mild autism.ConclusionThis risk score model could lower the age limit for autism prediction with good discrimination performance, and it has unique advantages in clinical application