In Pursuit of Sex Parity: Are Girls Becoming more Educated than Boys?

One of the Millennium Development Goals (MDGs) is the elimination of gender disparity in primary and secondary education. Global initiatives toward this end have mostly been to bring the benefits of education closer to girls so that the gap between them and boys may eventually close. In the Philippines, however, mounting evidence points to a reversed direction of the gender disparity. Are our boys falling behind on education? Why? Read more.education, millennium development goal (MDG), education indicators, Education for All (EFA), sex parity

Allelomimesis as universal clustering mechanism for complex adaptive systems

Animal and human clusters are complex adaptive systems and many are organized in cluster sizes $s$ that obey the frequency-distribution $D(s)\propto s^{-\tau}$. Exponent $\tau$ describes the relative abundance of the cluster sizes in a given system. Data analyses have revealed that real-world clusters exhibit a broad spectrum of $\tau$-values, $0.7\textrm{(tuna fish schools)}\leq\tau\leq 2.95\textrm{(galaxies)}$. We show that allelomimesis is a fundamental mechanism for adaptation that accurately explains why a broad spectrum of $\tau$-values is observed in animate, human and inanimate cluster systems. Previous mathematical models could not account for the phenomenon. They are hampered by details and apply only to specific systems such as cities, business firms or gene family sizes. Allelomimesis is the tendency of an individual to imitate the actions of its neighbors and two cluster systems yield different $\tau$ values if their component agents display different allelomimetic tendencies. We demonstrate that allelomimetic adaptation are of three general types: blind copying, information-use copying, and non-copying. Allelomimetic adaptation also points to the existence of a stable cluster size consisting of three interacting individuals.Comment: 8 pages, 5 figures, 2 table

MEASUREMENT OF SMALLHOLDER TREE FARMS ON LEYTE ISLAND

This paper describes the field techniques used to measure timber volume and log quality from smallholder tree farms on Leyte Island conducted as part of ACIAR project ASEM/2003/052, Improving Financial Returns from Smallholder Tree Farms in the Philippines . Tree farms were included in the sample if they were 0.1 ha or greater in area and contained 100 or more trees. Paired circular blocks were chosen for measurement, one in the centre and one on the edge of the tree block. Where tree farms included multiple blocks of trees, two circular plots were established within each block. For each tree over 10 cm diameter at breast height (dbh) in the plot, measurements were made of dbh, diameter at the base (db), tree height, location, crown depth, crown radius, bearing and distance of each tree with reference to the plot centre. Estimates of log lengths and grade that each tree was expected to yield were also recorded, along with a sketch of each tree. In addition, data were collected on tree farm, block and plot characteristics, and were entered into an ACCESS database for subsequent analysis

Choice of tracers for the evaluation of spray deposits

Tracer substances, used to evaluate spraying effectiveness, ordinarily modify the surface tension of aqueous solutions. This study aimed to establish a method of using tracers to evaluate distribution and amount of spray deposits, adjusted to the surface tension of the spraying solution. The following products were tested: 0.15% Brilliant Blue, 0.15% Saturn Yellow in 0.015% Vixilperse lignosulfonate, and 0.005% sodium fluorescein, and mixtures of Brilliant Blue plus Saturn Yellow and Brilliant Blue plus sodium fluorescein at the same concentrations. Solutions were deposited on citrus leaves and stability was determined by measuring fluorescence and optical density of solutions without drying, dried in the dark and exposed to sunlight for 2, 4 and 8 h. These values were compared to those obtained directly in water. The static surface tension of the tracer solution was determined by weighing droplets formed during a period of 20 to 40 seconds. The Brilliant Blue and Saturn Yellow mixture at 0.15% was stable under all conditions tested. It was not absorbed by the leaves and maintained the same surface tension as that of water, thus permitting concentration adjustment to the same levels used for agrochemical products, and allowing the development of a qualitative method based on visual evaluation of the distribution of the pigment under ultraviolet light and of a quantitative method based on the determination of the amount of the dye deposited in the same solution. Spray deposition could be evaluated at different surface tensions of the spraying solution, simulating the effect of agrochemical formulations

Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial: study protocol for a multicentre international trial of cardiac output-guided fluid therapy with low-dose inotrope infusion compared with usual care in patients undergoing major elective gastrointestinal surgery.

INTRODUCTION: Postoperative morbidity and mortality in older patients with comorbidities undergoing gastrointestinal surgery are a major burden on healthcare systems. Infections after surgery are common in such patients, prolonging hospitalisation and reducing postoperative short-term and long-term survival. Optimal management of perioperative intravenous fluids and inotropic drugs may reduce infection rates and improve outcomes from surgery. Previous small trials of cardiac-output-guided haemodynamic therapy algorithms suggested a modest reduction in postoperative morbidity. A large definitive trial is needed to confirm or refute this and inform widespread clinical practice. METHODS: The Optimisation of Perioperative Cardiovascular Management to Improve Surgical Outcome II (OPTIMISE II) trial is a multicentre, international, parallel group, open, randomised controlled trial. 2502 high-risk patients undergoing major elective gastrointestinal surgery will be randomly allocated in a 1:1 ratio using minimisation to minimally invasive cardiac output monitoring to guide protocolised administration of intravenous fluid combined with low-dose inotrope infusion, or usual care. The trial intervention will be carried out during and for 4 hours after surgery. The primary outcome is postoperative infection of Clavien-Dindo grade II or higher within 30 days of randomisation. Participants and those delivering the intervention will not be blinded to treatment allocation; however, outcome assessors will be blinded when feasible. Participant recruitment started in January 2017 and is scheduled to last 3 years, within 50 hospitals worldwide. ETHICS/DISSEMINATION: The OPTIMISE II trial has been approved by the UK National Research Ethics Service and has been approved by responsible ethics committees in all participating countries. The findings will be disseminated through publication in a widely accessible peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: ISRCTN39653756.The OPTIMISE II trial is supported by Edwards Lifesciences (Irvine, CA) and the UK National Institute for Health Research through RMP’s NIHR Professorship

Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

10.1371/journal.pcbi.1004504PLoS Computational Biology119e100450

Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works

Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works