Using Ensembles of Machine Learning Techniques to Predict Reference Evapotranspiration (ET0) Using Limited Meteorological Data

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R-2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R-2 Multan = 0.97, R2 Skardu = 0.99, R-2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance

Foliar applied proline and acetic acid improves growth and yield of wheat under salinity stress by improving photosynthetic pigments, physiological traits, antioxidant activities and nutrient uptake

Salinity stress (SS) is serious abiotic stress and a major limiting factor for crop productivity and global food security. In this context, the application of osmolytes is considered as an environmental friend approach to improve plant growth under SS. Thus, the present study was conducted to determine the impact of foliar applied proline (Pro) and acetic acid (AA) on growth, yield, physiological traits, photosynthetic pigments, ionic homeostasis and antioxidant activities of wheat under SS. The study contained SS levels 0, 6 and 12 dS m-1 and foliar spray of Pro and AA; water spray, Pro (75 mM), AA (15 mM) and AA (30 mM). The study was conducted in a completely randomized design with the factorial arrangement. Salinity stress significantly reduced wheat growth and yield, by decreasing relative water contents (-49.07%), photosynthetic pigments, free amino acids (FAA: -44.79%), total soluble proteins (TSP: -15.94%) and increasing the electrolyte leakage (EL: +27.28%), hydrogen peroxide (H2O2: +51.86%), and malondialdehyde (MDA: +36.91%) accumulation. The foliar spray of Pro and AA markedly improved the wheat growth and productivity through enhanced photosynthetic pigments, RWC, FAA, TSP, antioxidant activities (catalase: CAT, ascorbate peroxide: APX: peroxidase: POD), K+ and Ca2+ uptake and decreasing EL, MDA and H2O2 accumulation and restricted entry of toxic ions (Na+ and Cl-1).  Therefore, foliar application of AA and Pro effectively improves the growth and yield of wheat under SS by strengthening the antioxidant defense system, and maintaining ionic homeostasis and physiological performance

Intercomparison and Assessment of Stand-Alone and Wavelet-Coupled Machine Learning Models for Simulating Rainfall-Runoff Process in Four Basins of Pothohar Region, Pakistan

The science of hydrological modeling has continuously evolved under the influence of rapid advancements in software and hardware technologies. Starting from simple rational formulae for estimating peak discharge and developing into sophisticated univariate predictive models, accurate conversion of rainfall into runoff and the assessment of inherent uncertainty has been a prime focus for researchers. Therefore, alternative data-driven methods have gained widespread attention in hydrology. Moreover, scientists often couple conventional machine learning models with data pre-processing techniques, i.e., wavelet transformation (WT), to enhance modelling accuracy. In this context, this research work attempts to explore the latent linkage between rainfall and runoff in Pothohar region of Pakistan by developing a novel linkage of five streamline techniques of machine learning, including single decision tree (SDT), decision tree forest (DTF), tree boost (TB), multilayer perceptron (MLP), and gene expression modeling (GEP), with a more sophisticated variant of WT, i.e., maximal overlap discrete wavelet transformation (MODWT), for boundary correction of the transformed components of timeseries data. This study also implements these machine learning models in a stand-alone mode for a more comprehensive comparative analysis of performances. Furthermore, the study uses a combined-basin approach that divides Pothohar region into two basins to compensate for the complex topographic division of the study area. The results indicate that MODWT-based DTF outperformed other stand-alone and hybrid models in terms of modeling accuracy. In the first scenario, considering the Bunha-Kahan River basin, MODWT-DTF yielded the highest NSE (0.86) and the lowest RMSE (220.45 mm) and R2 (0.92 at lag order 3 (Lo3)) when transformed with daubechies4 (db4) at level three. While in the Soan-Haro River basin, MODWT-DTF produced the highest accuracy modeling at lag order 4 (Lo4) (NSE = 0.88, RMSE = 21.72 m(3)/s, and R2 = 0.91). The highly accurate performance of 3- and 4-days lagged models reflects the temporal consistency in hydrological response of the study area. The comparison of simple and hybrid model performance indicates up to a 55% increase in modeling accuracy due to data pre-processing with wavelet transformation

Z' Bosons at Colliders: a Bayesian Viewpoint

We revisit the CDF data on di-muon production to impose constraints on a large class of Z' bosons occurring in a variety of E_6 GUT based models. We analyze the dependence of these limits on various factors contributing to the production cross-section, showing that currently systematic and theoretical uncertainties play a relatively minor role. Driven by this observation, we emphasize the use of the Bayesian statistical method, which allows us to straightforwardly (i) vary the gauge coupling strength, g', of the underlying U(1)'; (ii) include interference effects with the Z' amplitude (which are especially important for large g'); (iii) smoothly vary the U(1)' charges; (iv) combine these data with the electroweak precision constraints as well as with other observables obtained from colliders such as LEP 2 and the LHC; and (v) find preferred regions in parameter space once an excess is seen. We adopt this method as a complementary approach for a couple of sample models and find limits on the Z' mass, generally differing by only a few percent from the corresponding CDF ones when we follow their approach. Another general result is that the interference effects are quite relevant if one aims at discriminating between models. Finally, the Bayesian approach frees us of any ad hoc assumptions about the number of events needed to constitute a signal or exclusion limit for various actual and hypothetical reference energies and luminosities at the Tevatron and the LHC.Comment: PDFLaTeX, 24 pages, 7 figures. Version with improved tables and figure

Simulation of the CMS Resistive Plate Chambers

The Resistive Plate Chamber (RPC) muon subsystem contributes significantly to the formation of the trigger decision and reconstruction of the muon trajectory parameters. Simulation of the RPC response is a crucial part of the entire CMS Monte Carlo software and directly influences the final physical results. An algorithm based on the parametrization of RPC efficiency, noise, cluster size and timing for every strip has been developed. Experimental data obtained from cosmic and proton-proton collisions at $\sqrt{s}=7$ TeV have been used for determination of the parameters. A dedicated validation procedure has been developed. A good agreement between the simulated and experimental data has been achieved.Comment: to be published in JINS

Sentinel node detection in N0 cancer of the pharynx and larynx

Neck lymph node status is the most important factor for prognosis in head and neck squamous cell carcinoma. Sentinel node detection reliably predicts the lymph node status in melanoma and breast cancer patients. This study evaluates the predictive value of sentinel node detection in 50 patients suffering from pharyngeal and laryngeal carcinomas with a N0 neck as assessed by ultrasound imaging. Following 99m-Technetium nanocolloid injection in the perimeter of the tumour intraoperative sentinel node detection was performed during lymph node dissection. Postoperatively the histological results of the sentinel nodes were compared with the excised neck dissection specimen. Identification of sentinel nodes was successful in all 50 patients with a sensitivity of 89%. In eight cases the sentinel node showed nodal disease (pN1). In 41 patients the sentinel node was tumour negative reflecting the correct neck lymph node status (pN0). We observed one false-negative result. In this case the sentinel node was free of tumour, whereas a neighbouring lymph node contained a lymph node metastasis (pN1). Although we have shown, that skipping of nodal basins can occur, this technique still reliably identifies the sentinel nodes of patients with squamous cell carcinoma of the pharynx and larynx. Future studies must show, if sentinel node detection is suitable to limit the extent of lymph node dissection in clinically N0 necks of patients suffering from pharyngeal and laryngeal squamous cell carcinoma

Experiences of children’s self-wetting (including urinary incontinence) in Cox’s Bazar’s Rohingya refugee camps, Bangladesh

Self-wetting is the leakage of urine, either due to the medical condition of urinary incontinence (UI), or because a person does not want to, or cannot, access a toileting facility in time. This study explored the attitudes towards self-wetting and experiences of children (aged five to 11), their caregivers, community leaders and humanitarian practitioners in the Rohingya refugee camps in Cox’s Bazar, Bangladesh. We particularly focused on how water, sanitation and hygiene (WASH) and protection interventions might assist in improving these experiences. We purposively selected participants from two camps where our partner organisation works. We conducted Key Informant Interviews (KIIs) with community leaders and camp officials, Story Book (SB) sessions with Rohingya children and in-depth Interviews (IDIs) with caregivers of children who participated in the SB sessions, as well as surveying communal toilets. Self-wetting by children was common and resulted in them feeling embarrassed, upset and uncomfortable, and frightened to use the toilet at night; many children also indicated that they would be punished by their caregivers for self-wetting. Key informants indicated that caregivers have difficulty handling children’s self-wetting due to a limited amount of clothing, pillows, and blankets, and difficulty cleaning these items. It was evident that the available toilets are often not appropriate and/or accessible for children. Children in the Rohingya camps appear to self-wet due to both the medical condition of UI and because the sanitation facilities are inappropriate. They are teased by their peers and punished by their caregivers. Although WASH and protection practitioners are unable to drastically alter camp conditions or treat UI, the lives of children who self-wet in these camps could likely be improved by increasing awareness on self-wetting to decrease stigma and ease the concerns of caregivers, increasing the number of child-friendly toilets and increasing the provision of continence management materials

Search for the standard model Higgs boson in the H to ZZ to 2l 2nu channel in pp collisions at sqrt(s) = 7 TeV

A search for the standard model Higgs boson in the H to ZZ to 2l 2nu decay channel, where l = e or mu, in pp collisions at a center-of-mass energy of 7 TeV is presented. The data were collected at the LHC, with the CMS detector, and correspond to an integrated luminosity of 4.6 inverse femtobarns. No significant excess is observed above the background expectation, and upper limits are set on the Higgs boson production cross section. The presence of the standard model Higgs boson with a mass in the 270-440 GeV range is excluded at 95% confidence level.Comment: Submitted to JHE

Compressed representation of a partially defined integer function over multiple arguments

In OLAP (OnLine Analitical Processing) data are analysed in an n-dimensional cube. The cube may be represented as a partially defined function over n arguments. Considering that often the function is not defined everywhere, we ask: is there a known way of representing the function or the points in which it is defined, in a more compact manner than the trivial one