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Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.

Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.

Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan

Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.

Figure 4 in Impact of treated sewage on meiobenthic nematodes: a case study from the Tunisian Refining Industries Company

Figure 4. Non-metric MDS ordination of square-root transformed abundances of nematode species from control microcosms (C) and those exposed to treated (TW) and untreated water (UW).

Figure 3. The k in Impact of treated sewage on meiobenthic nematodes: a case study from the Tunisian Refining Industries Company

Figure 3. The k-dominance curves plotted from nematode assemblages from control microcosms © and those exposed to treated (TW) and untreated water (UW).

Figure 2 in Impact of treated sewage on meiobenthic nematodes: a case study from the Tunisian Refining Industries Company

Figure 2. Shannon diversity index (H') and Evenness (J') of nematode assemblages from control microcosms (C) and those exposed to treated (TW) and untreated water (UW). Different letters indicate significant differences (p ≤ 0.05); for details see text.

opencc-zeroJun 2024View details →

Impact of public health expenditure on malnutrition among Peruvians during the period 2010-2020: A panel data analysis

Background: <a name="_Hlk170909708"></a>The study analyzes the impact of public health spending on malnutrition among Peruvians, using data from the National Household Survey, the Central Reserve Bank of Peru, the National Institute of Statistics and Informatics and the Ministry of Economy and Finance from 2010. -2020. Previous studies have revealed the existing relationship of health spending with the reduction of malnutrition. Methods: A quantitative approach is considered, with an explanatory type of research using panel data methodology considering the bidimensionality of the data, which allows quantifying this effect for the Peruvian case using the National Household Survey, data from the Central Reserve Bank of Peru, as well as information from the National Institute of Statistics and Informatics and the Transparency Portal of the Ministry of Economy and Finance in the period 2010-2020. Results: The results show that public expenditure on health has a negative relationship with malnutrition; the rural sector has a positive relationship with malnutrition given the limitations present for access to adequate food. Similarly, the unemployment rate shows a positive relationship with malnutrition, given that being unemployed leads to a higher cause of malnutrition in the population, and the gross domestic product has a negative relationship with malnutrition, given that greater economic growth produces an impact on reducing malnutrition, with the greatest impact being on the rural population and the gross domestic product. Conclusions: In the analysis period 2010-2020 in Peru, based on the panel data analysis, the impact of public health expenditure on reducing malnutrition is observed in 10 departments, achieving a reduction in malnutrition; while in 14 departments, this indicator has not been reduced.

Q-MARE database on pre-industrial climate and human impacts on marine ecosystems

A systematic literature review was carried out using two bibliographic databases the Web of Science (WoS; www.webofknowledge.com; Clarivate) and Scopus (www.scopus.com; Elsevier). In the former searches were completed by searching the “core collection” using the “topic” field (which searches the paper titles, abstracts, author keywords and keywords plus; the latter determined by a Clarivate algorithm using synonymy), and in Scopus the abstract, title and keyword fields were searched. Searches were completed between July and November 2023.

opencc-zeroJun 2024View details →

Evaluation of the Impact of the Educational Revolution in Peru and the Gender Wage Gap, 2017-2021

Background: Women's educational attainment and their generation of value through education has increased the prospects for achieving economic equality between men and women. However, women continue to earn lower wages than men, reflecting growing inequality in several countries. Therefore, the objective of the study is to estimate the impact of education on the gender wage gap in Peru over the period 2017-2021. Methods: Quantitative, explanatory study aimed at identifying the impact of education on the gender wage gap in Peru during the period 2017-2021. The research design is non-experimental and uses a time series that analyses the influence of the latent variable of education on the gender wage gap. This is a continuous variable to estimate the Tobit model. Results: The results show that the gender gap in Peru exhibited a decreasing trend between men and women during the period 2017-2020, with an average reduction of 10% until 2020 due to the health crisis. The highest average salary was achieved by men in 2019, reaching S/2289.97 soles, while women reached an average salary of S/1368.85 soles. In the post-pandemic scenario for 2021, the gender gap increased by 3%, with men earning an average salary of S/1999.63 soles and women earning an average salary of S/1281.16 soles. The analysis from 2017-2021 shows that years of education had a positive impact on the gender wage gap in Peru based on the Tobit model estimation. Conclusions: During the analysis period of 2017-2021, years of education had a positive impact on the gender wage gap in Peru, with the greatest impact occurring during the health crisis. The probability of women's incomes improving with an increase in years of education was 2.35%, while for men, the highest impact was in 2018, with a probability of income improvement of 2.16% in terms of marginal effect.

opencc-by-4.0Dec 2015View details →

Fig. 2 in Impact of food diversity on biological parameters of Apolygus lucorum (Hemiptera: Heteroptera: Miridae)

Fig. 2. Number (± SE) of Helicoverpa armigera eggs preyed upon by Apolygus lucorum for each instar. Bars topped by different lowercase letters represent means that are statistically different among different foods (1-way ANOVA followed by Tukey test, P <0.05).

opencc-by-4.0Mar 2015View details →

Fig. 1 in Impacts of azadirachtin and chlorantraniliprole on the developmental stages of pirate bug predators (Hemiptera: Anthocoridae) of the tomato pinworm Tuta absoluta (Lepidoptera: Gelechiidae)

Fig. 1. Longevity (days ± SE) of nymphs of Amphiareus constrictus exposed to azadirachtin and chlorantraniliprole via three routes of exposure (ingestion, residue contact and direct spray). *Comparison between two bars is statistically significant (t-test, P <0.05).

opencc-by-4.0Mar 2015View details →

Fig. 4 in Impacts of azadirachtin and chlorantraniliprole on the developmental stages of pirate bug predators (Hemiptera: Anthocoridae) of the tomato pinworm Tuta absoluta (Lepidoptera: Gelechiidae)

Fig. 4. Proportion of nymphs of Blaptostethus pallescens that reached the adult stage afer early exposure to azadirachtin and chlorantraniliprole via three routes of exposure (ingestion, residue contact and direct spray). *Comparison between two bars is statistically significant (survival analysis/log rank test, P <0.05).

opencc-by-4.0Jun 2015View details →

Fig. 2 in Impact of fluctuating and constant temperatures on key life history parameters of Sipha flava (Hemiptera: Aphididae)

Fig. 2. Effects of either fluctuating or constant temperatures on the longevity (days) and the reproductive capacity (offspring per female) of Sipha flava, i.e., Panel A, effect on the longevity of Sipha flava subjected to Trt. #1 (uncontrolled greenhouse with a fluctuating temperature regime) and Trt #2 (simulated mean hourly temperatures of the greenhouse with a fluctuating temperature regime); Panel B, effect on fecundity of Sipha flava subjected to the same treatments as in Panel A; Panel C, effect on the longevity of Sipha flava subjected to Trt #2 (simulated mean hourly temperatures of the greenhouse with a fluctuating temperature regime), Trt #4 (a constant mean temperature of 22.5 °C), Trt #3 (simulated mean temperatures of 27 °C during photophase and 18 °C during scotophase); Panel D, effect on fecundity of Sipha flava subjected to the same treatments as in Panel C. Mean longevity and fecundity values followed by different lowercase letters are significantly different based on ANOVA followed by the Tukey test.

opencc-by-4.0Jun 2015View details →

Fig. 1 in Impact of fluctuating and constant temperatures on key life history parameters of Sipha flava (Hemiptera: Aphididae)

Fig. 1. Simulated hourly mean temperatures in the uncontrolled greenhouse every day during the 53 d period when the effects of various temperatures on the longevity and reproductive capacity of Sipha flava were determined.

A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction

This file contains human-robot interaction data acquired during an  experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland. <div> <div> <div> <div> The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee. Duration: Total of 5 hours and 7 minutes. Participants: 1777 individuals tracked. Environments: <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> Data Types: <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> Key Events: <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>

opencc-by-4.0Jan 2024View details →

The Impact of Oxygen Surface Coverage and Carbidic Carbon on the Activity and Selectivity of Two-Dimensional Molybdenum Carbide (2D-Mo2C) in Fischer–Tropsch Synthesis

Datasets categorized per figure and contain data in x,y format. for the DFT part: 35 elementary steps were studied. Each step is marked RX_NEB_InitialState_FinalState, and corresponds to the neb calculation for the identification of the transition state. In each file, POSCAR_00 corresponds to the initial structure and POSCAR_09 to the final structure, in both cases after geometry optimization. In each state, a file with the vibration calculation for the calculation of the Gibbs Energy is included.  The Gibbs energies of the initial, transition and final states in table format are provided in Figure 4 - panel b - Gibbs Energies_Initial_Transition_Final_states_35_elementary_reactions The calculation of Gibbs energies of the gas phase molecules in the empty unit cell, used as reference states are provided in Figure 4 - Reference_state_Gases_empty_unit_cell The computations for the comparison of the different sites (HMo, Hc, atop and bridge) are provided in Table S6 - Comparison_adsorption_sites The computations on the model with a partial oxygen coverage (O.67 O ML) are provided in: Figure S29 - mo2c-ctx-3x3_067OML_C_CH_CCH Figure S29 - mo2c-ctx-3x3_067OML_CH3_H_CH4 Figure S29 - mo2c-ctx-3x3_067OML_CO_C_O Figure S29 - mo2c-ctx-3x3_067OML_CO_O_CO2

opencc-by-4.0Jun 2024View details →

Supplement code and data for "A global multi-indicator assessment of the environmental impact of livestock products"

This data and code supplements the publication "A global multi-indicator assessment of the environmental impact of livestock products" by Giorgio A. Bidoglio, Florian Schwarzmueller, Thomas Kastner available here: https://doi.org/10.1016/j.gloenvcha.2024.102853 Version 1.1 add the code to the visualization tool available online at https://livestockimpactassessment.shinyapps.io/multi-indicator_impact_assessment_of_livestock_products/

Data study "The Impact of Augmented Reality on Biodiversity Learning in a Pedagogical Scenario Based on Analogical Reasoning: An Experimental Study"

This data was collected in 2023 as part of a study on the impact of location-based AR on biodiversity education.

Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"

Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in Earth's Future Dear reader, reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in Earth's Future. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below. The research data covers the following files: <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> Meta-data files are provided with data files in the different subfolders for clarification. iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State. Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/ The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep). Literature: Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092. Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/ Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737 Contact one of the authors if you have further questions: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)