High Sensitivity Tunable Radio Frequency Sensors

Highly sensitive and tunable RF sensors that provide detection and analysis of single cells and particles are provided. The tunable RF sensors are configured as tunable interferometers, wherein cells or particles to be analyzed are passed through a channel, such as a microfluidic channel, across waveguides corresponding to reference and test branches of the interferometers. A network analyzer coupled to the interferometers can be configured to measure a plurality of scattering parameters, such as transmission scattering coefficients (S.sub.21) of the reference and test branches, to evaluate characteristics of cells passing through the channel. A plurality of tunable interferometers may be employed, each interferometer operating in different frequency bands such that information obtain from the plurality of interferometers may be combined to provide further information

Who can help me? Understanding the antecedent and consequence of medical information seeking behavior in the era of bigdata

IntroductionThe advent of bigdata era fundamentally transformed the nature of medical information seeking and the traditional binary medical relationship. Weaving stress coping theory and information processing theory, we developed an integrative perspective on information seeking behavior and explored the antecedent and consequence of such behavior.MethodsData were collected from 573 women suffering from infertility who was seeking assisted reproductive technology treatment in China. We used AMOS 22.0 and the PROCESS macro in SPSS 25.0 software to test our model.ResultsOur findings demonstrated that patients’ satisfaction with information received from the physicians negatively predicted their behavior involvement in information seeking, such behavior positively related to their perceived information overload, and the latter negatively related to patient-physician relationship quality. Further findings showed that medical information seeking behavior and perceived information overload would serially mediate the impacts of satisfaction with information received from physicians on patient-physician relationship quality.DiscussionThis study extends knowledge of information seeking behavior by proposing an integrative model and expands the application of stress coping theory and information processing theory. Additionally, it provides valuable implications for patients, physicians and public health information service providers

Influence of Transfer Plot Area and Location on Chemical Input Reduction in Agricultural Production: Evidence from China

The development of a farmland transfer market and the spatial characteristics of transfer plots are crucial factors influencing chemical input reduction in agricultural production with relation to the endowment of fragmented agricultural land resources. Through a theoretical discussion, this study analyzed the heterogeneity of transfer plots’ spatial characteristics and their effect on the intensity of chemical input in agricultural production in the process of farmland transfer. Plot-level survey data from the Heilongjiang, Henan, Zhejiang, and Sichuan provinces were used for empirical analysis. The results indicated that the values of pesticide and fertilizer input in the large plot group were CNY 10.154 and CNY 8.679 lower than those in the small plot group, respectively. Additionally, compared with non-adjacent plots, the per-unit area input was CNY 2.396 and CNY 6.691 lower in adjacent plots. This indicated that plot area expansion and location adjacence significantly reduced the intensity of pesticide application and fertilizer input in the plots. Simultaneously, location linkage reduced chemical input in agricultural production in small plots; however, the difference was unnoticeable in large plots. This study provides a theoretical basis for promoting farmland integration in China as well as introduces a specialized method for reducing agricultural chemical usage

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively

Deep Collaborative Recommendation Algorithm Based on Attention Mechanism

Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF methods directly learn the complicated mapping functions that map user-item pairs to matching scores, which has limitations in identifying low-rank relationships. To this end, we propose a deep collaborative recommendation algorithm based on attention mechanism (DACR). First, before the user-item representations are input into the DNNs, we utilize the attention mechanism to adaptively assign different weights to the user-item representations, which captures the hidden information in implicit feedback. After that, we input the user-item representations with corresponding weights into the representation learning and matching function learning modules. Finally, we concatenate the prediction vectors learned from different dimensions to predict the matching scores. The results show that we can improve the expression ability of the model while taking into account not only the nonlinear information hidden in implicit feedback, but also the low-rank relationships of user-item pairs to obtain more accurate predictions. Through detailed experiments on two datasets, we find that the ranking capability of the DACR model is enhanced compared with other baseline models, and the evaluation metrics HR and NDCG of DACR are increased by 0.88–1.19% and 0.65–1.15%, respectively