NutriFD: Proving the medicinal value of food nutrition based on food-disease association and treatment networks

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy

Decelerating Airy pulse propagation in highly non-instantaneous cubic media

The propagation of decelerating Airy pulses in non-instantaneous cubic medium is investigated both theoretically and numerically. In a Debye model, at variance with the case of accelerating Airy and Gaussian pulses, a decelerating Airy pulse evolves into a single soliton for weak and general non- instantaneous response. Airy pulses can hence be used to control soliton generation by temporal shaping. The effect is critically dependent on the response time, and could be used as a way to measure the Debye type response function. For highly non- instantaneous response, we theoretically find a decelerating Airy pulse is still transformed into Airy wave packet with deceleration. The theoretical predictions are confirmed by numerical simulations

Selective gas detection using Mn3O4/WO3 composites as a sensing layer

Pure WO3 sensors and Mn3O4/WO3 composite sensors with different Mn concentrations (1 atom %, 3 atom % and 5 atom %) were successfully prepared through a facile hydrothermal method. As gas sensing materials, their sensing performance at different temperatures was systematically investigated for gas detection. The devices displayed different sensing responses toward different gases at specific temperatures. The gas sensing performance of Mn3O4/WO3 composites (especially at 3 atom % Mn) were far improved compared to sensors based on pure WO3, where the improvement is related to the heterojunction formed between the two metal oxides. The sensor based on the Mn3O4/WO3 composite with 3 atom % Mn showed a high selective response to hydrogen sulfide (H2S), ammonia (NH3) and carbon monoxide (CO) at working temperatures of 90 degrees C, 150 degrees C and 210 degrees C, respectively. The demonstrated superior selectivity opens the door for potential applications in gas recognition and detection

BVFB: Training Behavior Verification Mechanism for Secure Blockchain-Based Federated Learning

There are still two problems of the existing methods of defending against poisoning attacks of the blockchain-based federated learning: 1) It is difficult to accurately identify the nodes under attack; 2) The effect of the model is greatly affected when the number of malicious nodes exceeds a half. So, an innovative secure mechanism is proposed for blockchain-based federated learning, which is called the training behavior verification mechanism. The mechanism describes the consistent training behavior rules of nodes by constructing the training behavior model, and distinguishes honest nodes from malicious nodes by comparing the differences in training behavior models on the training behavior verification algorithm. Experiments show that the new mechanism can effectively resist more than half of the label-flipping attacks and backdoor attacks, and has the advantages of higher stability and higher accuracy than methods such as Krum, Trimmed Mean, and Median

From the Lab to the Classroom: Research at the Interface Between Cognitive Science and Education

Presented at the 29th Association for Psychological Science (APS) Annual Convention in Boston, MA

Financial Vulnerability of Midwest Grain Farms: Implications of Price, Yield, and Cost Shocks

Recent years have witnessed increasing volatility in crop prices and yields, fertilizer prices, and farm asset values. In this study, the financial performance of illustrative Midwest grain farms with different scales, tenure status, and capital structures was examined under the shocks of volatile crop prices, yields, fertilizer prices, farmland value, and cash rent. Illustrative farms of 550, 1,200, and 2,500 acres were constructed reflecting the production activity for these farms with three different farmland ownership structures (15%, 50%, and 85% of land owned) and two capital structures measured by debt-to- asset ratio (25% and 50%). Absolute measures and financial ratios were used to evaluate the income, cash flow, debt servicing, and equity position of these illustrative farms. The “stress test” results suggest that farms with modest size (i.e., 550 acres) and a large proportion of their land rented are very vulnerable irrespective of their leverage positions. Large-size farms with modest leverage (25% debt-to- asset ratio) that combine rental and ownership of the land they operated have strong financial performance and limited vulnerability to price, cost, yield, and asset value shocks. And these farms can increase their leverage positions significantly (from 25% to 50% in this study) with only modest deterioration in their financial performance and a slight increase in their vulnerability. These results suggest that the perspective that farmers are resilient to price, cost, yield, and asset value shocks because of the current low use of debt in the industry (an average of about 13% debt-to- asset ratio for the farming sector) does not adequately recognize the financial vulnerable of many typical family farms to those shocks

Enhancing the conversational agent with an emotional support system for mental health digital therapeutics

As psychological diseases become more prevalent and are identified as the leading cause of acquired disability, it is essential to assist people in improving their mental health. Digital therapeutics (DTx) has been widely studied to treat psychological diseases with the advantage of cost savings. Among the techniques of DTx, a conversational agent can interact with patients through natural language dialog and has become the most promising one. However, conversational agents' ability to accurately show emotional support (ES) limits their role in DTx solutions, especially in mental health support. One of the main reasons is that the prediction of emotional support systems does not extract effective information from historical dialog data and only depends on the data derived from one single-turn interaction with users. To address this issue, we propose a novel emotional support conversation agent called the STEF agent that generates more supportive responses based on a thorough view of past emotions. The proposed STEF agent consists of the emotional fusion mechanism and strategy tendency encoder. The emotional fusion mechanism focuses on capturing the subtle emotional changes throughout a conversation. The strategy tendency encoder aims at foreseeing strategy evolution through multi-source interactions and extracting latent strategy semantic embedding. Experimental results on the benchmark dataset ESConv demonstrate the effectiveness of the STEF agent compared with competitive baselines

NH3 sensor based on 3D hierarchical flower-shaped n-ZnO/p-NiO heterostructures yields outstanding sensing capabilities at ppb level

Hierarchical three-dimensional (3D) flower-like n-ZnO/p-NiO heterostructures with various ZnxNiy molar ratios (Zn5Ni1, Zn2Ni1, Zn1Ni1, Zn1Ni2 and Zn1Ni5) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed ZnxNiy-based sensors toward ammonia (NH3) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn1Ni2 sensor exhibited superior NH3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH3 sensing capabilities of Zn1Ni2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n-ZnO/p-NiO heterostructure is indeed very promising sensing material toward NH3 detection for different applications