Explore the vast range of titles available.

According to the total survey error theory, most survey errors are stemming from non-sampling error—errors such as response and non-response. This chapter discusses the importance of questionnaire design and specific question phrasing and how they affect response rate and quality of response. Providing a detail account of the various stages in preparing a valid and reliable questionnaire provides the readers with a helpful tool that will guide researchers through the complex and daunting task from single item phrasing to the presentation of an entire questionnaire.

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman–Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions.

Applications of microbiome research through metagenomics promise to generate microbiome manipulation strategies for improved larval survival in aquaculture. However, existing lacunae on the effects of sample preservation methods in metagenome profiles hinder the successful application of this technique. In this context, four preservation methods were scrutinized to identify reliable methods for fish larval microbiome research. The results showed that a total of ten metagenomics metrics, including DNA yield, taxonomic and functional microbiome profiles, and diversity measures, were significantly ( P  < 0.05) influenced by the preservation method. Activity ranking based on the performance and reproducibility showed that three methods, namely immediate direct freezing, room temperature preservation in absolute ethanol, and preservation at − 20 °C in lysis, storage, and transportation buffer, could be recommended for larval microbiome research. Furthermore, as there was an apparent deviation of the microbiome profiles of ethanol preserved samples at room temperature, the other methods are preferred. Detailed analysis showed that this deviation was due to the bias towards Vibrionales and Rhodobacterales . The microbial taxa responsible for the dissimilarity across different methods were identified. Altogether, the paper sheds light on the preservation protocols of fish larval microbiome research for the first time. The results can help in cross-comparison of future and past larval microbiome studies. Furthermore, this is the first report on the activity ranking of preservation methods based on metagenomics metrics. Apart from methodological perspectives, the paper provides for the first time certain insights into larval microbial profiles of Rachycentron canadum , a potential marine aquaculture species. Key points • First report on effects of preservation methods on fish larval microbiome profiles. • First report on activity ranking of preservation methods based on metagenomics metrics. • Storage methods influenced DNA yield, taxonomic and functional microbiome profiles.

The stochastic error distance (SED) introduced by Diebold and Shin (2017) ranks forecast models by divergence between distributions of the errors of the actual and perfect forecast models. The basic SED is defined by the variation distance and provides a representation of the mean absolute error, but by basing ranking on the entire error distribution and divergence, the SED moves beyond the traditional forecast evaluations. First, we establish connections between ranking forecast models by the SED, error entropy and some partial orderings of distributions. Then, we introduce the notion of excess error for forecast errors of magnitudes larger than a tolerance threshold and give the SED representation of the mean excess error (MEE). As a function of the threshold, the MEE is a local risk measure. With the distribution of the absolute error as a prior for the threshold, its Bayes risk is the entropy functional of the survival function, which is a known measure in the information theory and reliability. Notions and results are illustrated using various distributions for the error. The empirical versions of SED, MEE and its Bayes risk are compared with the mean squared error in ranking regression and autoregressive integrated moving average models for forecasting bond risk premia.

The stability of absolute gravimeters during carriage-falling body separation is crucial for improving gravitational acceleration measurement accuracy. Transmission speed accuracy of the transmission system and system vibration are core factors determining this stability, while steel belt pre-tightening force, free-fall segment acceleration, and start-up segment displacement are key parameters influencing both. In-depth analysis of their coupling clarified their roles, and two objective function models (for speed accuracy and vibration) were established, with fitting accuracies R2 = 0.8976 and R2 = 0.8395, respectively. Since traditional single-objective optimization fails to balance “improving speed accuracy” and “suppressing vibration”, this study proposes a multi-objective optimization method: two Nondominated Sorting Genetic Algorithm II (NSGA-II) parameter sets were designed, Hypervolume (HV) index quantified solution set quality, and Wilcoxon signed-rank test was combined to determine the optimal parameter set; comparing the Global Criterion Method and Weighted Sum Method, the former was superior (no dimensional bias) and more suitable for this study, finally screening out the optimal parameter combination. Experimental results showed that the measured transmission speed accuracy was 0.09132 m/s (16.94% lower than the orthogonal experiment’s optimal level); the measured system vibration was 0.022 m/s2, falling within the orthogonal experiment’s optimal range. Consequently, separation moment stability was significantly enhanced, with its standard deviation reduced by 45% pre-optimization. This method achieves global balance in transmission system dynamic performance, providing an effective parameter optimization strategy for improving absolute gravimeter measurement accuracy.

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton–Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.

Ozone (O3) decomposition in the troposphere is a very important process which prevents excessive O3 accumulation in the air. It is particularly significant on warm summer days which are marked by a high risk of photochemical smog. We used Spearman’s rank correlation test to determine relationships between the drop in O3 concentrations over time (-ΔO3), nitrogen oxide (NO), nitrogen dioxide (NO2), and total nitrogen oxide (NOx) concentrations and meteorological factors (1-h average) in low-polluted urban area in Olsztyn (north-eastern Poland). Nitrogen oxide concentrations were measured continuously by the chemiluminescence method, and O3 concentrations were determined by the UV photometric method. The obtained results suggest that the rate of decomposition of tropospheric O3 is affected mostly by the presence of NOx, high temperature, and air humidity (positive correlation) as well as by wind speed (negative correlation). Maximum correlation coefficient values were reported between –ΔO3 and air temperature, –ΔO3 and absolute air humidity when NOx concentrations were low (below 1.0 microgram per cubic meter), reaching 0.271 and 0.243, respectively. These results indicate that O3 also reacted with air components other than NO and NO2. Precipitation at average temperature of < 0 °C did not significantly contribute to a drop in O3 concentrations at night-time. In the warm season, precipitation slowed down the rate of O3 decomposition, mostly because NOx were scrubbed by rain. An analysis of seasonal and daily –ΔO3 fluctuations revealed that –ΔO3 values were highest in the summer and shortly after sunset in the diurnal cycle.

Background: The likelihood of developing Posttraumatic Stress Disorder (PTSD) depends on the interaction of individual risk factors and cumulative traumatic experiences. Hence, the identification of individual susceptibility factors warrants precise quantification of trauma exposure. Previous research indicated that some traumatic events may have more severe influences on mental health than others; thus, the assessment of traumatic load may be improved by weighting event list items rather than calculating the simple sum score. Objective: We compared two statistical methods, Random Forests using Conditional Interference (RF-CI) and Least Absolute Shrinkage and Selection Operator (LASSO), based on their ability to rank traumatic experiences according to their importance for predicting lifetime PTSD. Methods: Statistical models were initially fitted in a sample of N 1  = 441 survivors of the Northern Ugandan rebel war. The ability to correctly predict lifetime PTSD was then tested in an independent sample of N 2  = 211, and subsequently compared with predictions by the simple sum score of different traumatic event types experienced. Results: Results indicate that RF-CI and LASSO allow for a ranking of traumatic events according to their predictive importance for lifetime PTSD. Moreover, RF-CI showed slightly better prediction accuracy than the simple sum score, followed by LASSO when comparing prediction results in the validation sample. Conclusion: Given the expense in time and calculation effort by RF-CI and LASSO, and the relatively low increase in prediction accuracy by RF-CI, we recommend using the simple sum score to measure the environmental factor traumatic load, e.g., in analyses of gene × environment interactions.

The objective of this work was to investigate the molecular origin of the differences in the thermal expansivity of four ROY polymorphs (Y, R, OP, and ON) using variable temperature single crystal X-ray diffractometry (VT-SCXRD). Thermal expansivity was found to be directly influenced by the crystal packing and the number and type of directional interactions, such as hydrogen bonds, involved in packing. Polymorphs with layered molecular packing, i.e., ON, OP, and R, show higher volume expansivity, where the axial component of the expansion is the largest in the directions perpendicular to the hydrogen-bonded layers and the smallest along the layers. Polymorph Y shows the least volume expansivity, which corresponds to the presence of a denser hydrogen-bonded network structure in the crystal, and absence of apparent molecular layers. The largest overall expansivity is observed for polymorph ON that lacks intermolecular hydrogen bonds and exhibits a layered packing pattern along two axes. The differences in the thermal expansivity of the ROY polymorphs lead to violations of the density rule in polymorph stability prediction due to crossover in crystal density with change in temperature, which means the rank order of crystal density of polymorphs is temperature-dependent. Thus, at absolute zero, the most thermodynamically stable polymorph Y is predicted to not have the highest density, which violates the density rule. Likewise, for all enantiotropic polymorphs undergoing the density crossover phenomenon, the density rule is valid only within the temperature range bracketed by the temperatures of density crossover (Td) and thermodynamic transition (Tt). For all monotropic polymorphs, the density rule is valid only above Td.

Background The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation). Methods Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes. Results We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisher’s exact test FDR <5%). Conclusion AIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.