Computer-assisted assessment of depression and function in older primary care patients

We wanted to test the psychometric reliability and validity of self-reported information on psychological and functional status gathered by computer in a sample of primary care outpatients. Persons aged 65 years and older visiting a primary care medical practice in Baltimore (n=240) were approached. Complete baseline data were obtained for 54 patients and 34 patients completed 1-week retest follow-up. Standard instruments were administered by computer and also given as paper and pencil tests. Test-retest reliability estimates were calculated and comparisons across mode of administration were made. Separately, an interviewer administered a questionnaire to gauge patient attitudes and feelings after using the computer. Most participants (72%) reported no previous computer use. Nevertheless, inter-method reliability of the GDS15 at baseline (0.719, n=47), intra-method reliability of the computer in time (0.797, n=31), inter-method reliability of the CESDR20 at baseline (0.740, n=53), and the correlation between the CESDR20 computer version at baseline and follow-up (0.849, n=34) were all excellent. The inter-method reliability of the CESDR20 at follow-up (0.615, n=37) was lower but still acceptable. Although 28% were anxious prior to using the computer testing system, that percent decreased to 19% while using the system. The efficiency and reliability in comparison to the paper instruments were good or better. Even though most participants had not ever used a computer prior to participating in the study, they had generally favorable attitudes toward the use of computers, and also reported having favorable experience with the computer testing system.     

Effect of 5-hydroxytryptamine on the peripheral chemoreceptors in the rat

5-Hydroxytryptamine (5-HT), in the doses of 2-5 mug/kg injected into the carotid body area, produced a significant increase in the respiratory rate and carotid sinus nerve activity. Selective ablation of the ipsilateral carotid sinus nerve abolished the respiratory stimulation produced by the drug. On the other hand, the same doses of 5-HT injected into the ascending aorta did not produce stimulation of respiration when the carotid sinus nerves were sectioned. The activity of the aortic nerves did not increase after injecting the drug into the ascending aorta or at the root of the right subclavian artery. These results indicate that 5-HT stimulates the chemoreceptors in the carotid body of the rat. The lack of responses to the injections of the drug in the aortic or subclavian region was due to the absence of chemoreceptors in these regions of the rat.     

REINFORCEMENT LEARNING FOR POMDP USING STATE CLASSIFICATION

Reinforcement learning (RL) has been widely used to solve problems with a little feedback from environment. Q learning can solve Markov decision processes (MDPs) quite well. For partially observable Markov decision processes (POMDPs), a recurrent neural network (RNN) can be used to approximate Q values. However, learning time for these problems is typically very long. We present a new combination of RL and RNN to find a good policy for POMDPs in a shorter learning time. This method contains two phases: firstly, state space is divided into two groups (fully observable state group and hidden state group); secondly, a Q value table is used to store values of fully observable states and an RNN is used to approximate values for hidden states. Results of experiments in two grid world problems show that the proposed method enables an agent to acquire a policy with better learning performance compared to the method using only a RNN.     

Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data

ABSTRACT

Aboveground biomass (AGB) of mangrove forest plays a crucial role in global carbon cycle by reducing greenhouse gas emissions and mitigating climate change impacts. Monitoring mangrove forests biomass accurately still remains challenging compared to other forest ecosystems. We investigated the usability of machine learning techniques for the estimation of AGB of mangrove plantation at a coastal area of Hai Phong city (Vietnam). The study employed a GIS database and support vector regression (SVR) to build and verify a model of AGB, drawing upon data from a survey in 25 sampling plots and an integration of Advanced Land Observing Satellite-2 Phased Array Type L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) dual-polarization horizontal transmitting and horizontal receiving (HH) and horizontal transmitting and vertical receiving (HV) and Sentinel-2A multispectral data. The performance of the model was assessed using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and leave-one-out cross-validation. Usability of the SVR model was assessed by comparing with four state-of-the-art machine learning techniques, i.e. radial basis function neural networks, multi-layer perceptron neural networks, Gaussian process, and random forest. The SVR model shows a satisfactory result (R² = 0.596, RMSE = 0.187, MAE = 0.123) and outperforms the four machine learning models. The SVR model-estimated AGB ranged between 36.22 and 230.14 Mg ha^?1 (average = 87.67 Mg ha^?1). We conclude that an integration of ALOS-2 PALSAR-2 and Sentinel-2A data used with SVR model can improve the AGB accuracy estimation of mangrove plantations in tropical areas.     

Strategic parameter-driven routing models for multidestinationtraffic in telecommunication networks

We present mathematical models that determine the optimal parameters for strategically routing multidestination traffic in an end-to-end network setting. Multidestination traffic refers to a traffic type that can be routed to any one of a multiple number of destinations. A growing number of communication services is based on multidestination routing. In this parameter-driven approach, a multidestination call is routed to one of the candidate destination nodes in accordance with predetermined decision parameters associated with each candidate node. We present three different approaches: (1) a link utilization (LU) approach, (2) a network cost (NC) approach, and (3) a combined parametric (CP) approach. The LU approach provides the solution that would result in an optimally balanced link utilization, whereas the NC approach provides the least expensive way to route traffic to destinations. The CP approach, on the other hand, provides multiple solutions that help leverage link utilization and cost. The LU approach has in fact been implemented by a long distance carrier resulting in a considerable efficiency improvement in its international direct services, as summarized.     

Metal protection by anticorrosion papers in humid tropical climate

Results of 3-year full-scale field climatic tests on application of five types of inhibiting papers for anticorrosion protection of ferrous and nonferrous metals during storage in a humid tropical climate have been carried out by the Joint Russian and Vietnamese Tropical Research and Technology Center and presented here. It has been shown that the inhibiting paper IPCAS-118 can be recommended for anticorrosion protection of equipment made of steel, copper, and aluminum during 3-year storage in sheds in humid tropical climate conditions     

Microlens Arrays by Direct-Writing Inkjet Print for LCD Backlighting Applications

In this paper, we demonstrated the Inkjet print method to fabricate a ultraviolet (UV)-curable epoxy microlens deflecting array with controllable curvature and filling factor, by which a 7-in light guide plate with 82% uniformity and 70% light efficiency was successfully implemented. The microlenses can be directly formed by plastic discharged by inkjet head, eliminating the need for molds and slashing development time. Proposed method will certainly has a promising impact on rapid prototyping and other specialized microfeatures due to its simplicity and versatility.