Phosphorus retention capacity in red ferralitic soil

In this study the main physical-chemical characteristics of red ferralitic soil to use as substrate in subsurface wetlands was determined. The P-removal was evaluated in a short-term isotherm batch experiment and in a column percolation experiment. The acid characteristic and high content of iron minerals in the red ferralitic soil facilitated the phosphorus removal. Also the sorption isotherms at two different temperatures were obtained. The results showed that the sorption capacity increases with an increase in solution temperature from 25 to 35 degrees C. The experimental data were fitted to Langmuir and Freundlich models, having a better fit to the Freundlich isotherms. The maximum P-sorption capacities estimated using the Langmuir isotherm were 0.96 and 1.13 g/kg at 25 and 35 degrees C respectively. Moreover a column experiment was carried out at two different flows. Sequential extractions of the phosphorus-saturated soil indicated that phosphorus is mainly bound with iron or aluminum minerals. The results have demonstrated a good potential for red ferralitic soil for phosphorus removal from urban wastewater

Lifetime policy reuse and the importance of task capacity

A long-standing challenge in artificial intelligence is lifelong learning. In lifelong learning, many tasks are presented in sequence and learners must efficiently transfer knowledge between tasks while avoiding catastrophic forgetting over long lifetimes. On these problems, policy reuse and other multi-policy reinforcement learning techniques can learn many tasks. However, they can generate many temporary or permanent policies, resulting in memory issues. Consequently, there is a need for lifetime-scalable methods that continually refine a policy library of a pre-defined size. This paper presents a first approach to lifetime-scalable policy reuse. To pre-select the number of policies, a notion of task capacity, the maximal number of tasks that a policy can accurately solve, is proposed. To evaluate lifetime policy reuse using this method, two state-of-the-art single-actor base-learners are compared: 1) a value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting the number of policies based on task capacity, D(R)QN achieves near-optimal performance with 6 policies in a 27-task MDP domain and 9 policies in an 18-task POMDP domain; with fewer policies, catastrophic forgetting and negative transfer are observed. Due to slow, monotonic improvement, PPO requires fewer policies, 1 policy for the 27-task domain and 4 policies for the 18-task domain, but it learns the tasks with lower accuracy than D(R)QN. These findings validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and PPO for smaller library sizes

The Costs of Implementing a Unilateral One-Sided Exchange Rate Target Zone

In the aftermath of the recent financial crisis, the central banks of small open economies such as the Czech National Bank and the Swiss National Bank (SNB) both implemented a unilateral one-sided exchange rate target zone vis-a-vis the euro currency to counteract deflationary pressures. Recently, the SNB abandoned its minimum exchange rate regime of CHF 1.20 per euro, arguing that after having analyzed the costs and benefits of this non-standard exchange rate policy measure, it was no longer sustainable. This paper proposes a model that allows central banks to estimate ex-ante the costs of implementing and maintaining a unilateral one-sided target zone and to monitor these costs during the period where it is enforced. The model also offers central banks a tool to identify the right timing for the discontinuation of a minimum exchange rate regime. An empirical application to the Swiss case shows the actual size of these costs and reveals that these costs would have been substantial without the abandonment of the minimum exchange rate regime, which accords with the official statements of the SNB

Lifetime policy reuse and the importance of task capacity

A long-standing challenge in artificial intelligence is lifelong reinforcement learning, where learners are given many tasks in sequence and must transfer knowledge between tasks while avoiding catastrophic forgetting. Policy reuse and other multi-policy reinforcement learning techniques can learn multiple tasks but may generate many policies. This paper presents two novel contributions, namely 1) Lifetime Policy Reuse, a model-agnostic policy reuse algorithm that avoids generating many policies by optimising a fixed number of near-optimal policies through a combination of policy optimisation and adaptive policy selection; and 2) the task capacity, a measure for the maximal number of tasks that a policy can accurately solve. Comparing two state-of-the-art base-learners, the results demonstrate the importance of Lifetime Policy Reuse and task capacity based pre-selection on an 18-task partially observable Pacman domain and a Cartpole domain of up to 125 tasks

Vertical flow constructed wetlands: kinetics of nutrient and organic matter removal

The kinetics of organic matter and nutrient removal in a pilot vertical subsurface wetland with red ferralitic soil as substrate were evaluated. The wetland (20 m(2)) was planted with Cyperus alternifolius. The domestic wastewater that was treated in the wetland had undergone a primary treatment consisting of a septic moat and a buffer tank. From the sixth week of operation, the performance of the wetland stabilized, and a significant reduction in pollutant concentration of the effluent wastewater was obtained. Also a significant increase of dissolved oxygen (5 mg/l) was obtained. The organic matter removal efficiency was greater than 85% and the nutrient removal efficiency was greater than 75% in the vertical subsurface wetland. Nitrogen and biochemical oxygen demand (BOD) removal could be described by a first-order model. The kinetic constants were 3.64 and 3.27 d(-1) for BOD and for total nitrogen, respectively. Data on the removal of phosphorus were adapted to a second-order model. The kinetic constant was 0.96 (mg/l)(-1) d(-1). The results demonstrated the potential of vertical flow constructed wetlands to clean treated domestic wastewater before discharge into the environment

Biological Half-Life of Prostate-Specific Antigen after Radical Prostatectomy

Peer Reviewe

Kinetics of prostate-specific antigen after manipulation of the prostate

Kinetics of prostate-specific antigen (PSA) were investigated after manipulation of the prostate in two groups of patients: those treated with digital rectal examination (DRE), and those with needle biopsy. 8 patients had serial PSA measurements to study the effect of DRE (group 1). 7 of 8 patients had PSA baseline values < 10 ng/ml. Blood samples were taken at 1 min, 30 min, 1, 3, 6, 12 and 24 h after DRE. Some patients were further monitored for 5 days with one blood sample taken at the same time each day. Statistically significant increased PSA levels were found after DRE (P < 0.001). Maximal increase was 70%. In most patients, peak levels were found between 30 and 60 min after DRE. Based on the results, it is concluded that after DRE it is prudent to wait 3 days before PSA is determined. 7 patients had serial PSA measurements after transrectal prostate needle biopsy (group 2). PSA sampling was similar as in the previous group. All patients had increased PSA levels after biopsy (range 1.3-9.5-fold). After 5 days, only 2 of 7 patients had returned to baseline levels. We conclude that biopsies of the prostate induce an important and long-lasting PSA elevatio