Summary of Common conducive environments per group of crop pests and diseases in the studied AEZs. by Wilson Nguru (14352167)

Subjects:

Grouping of pests and diseases based on optimal temperature. by Wilson Nguru (14352167)

Subjects:

The agroecological zones of Zambia. by Wilson Nguru (14352167)

Subjects:

Future crop pest and disease habitat suitability distribution by 2050 under RCP 8.5. by Wilson Nguru (14352167)

Subjects:

The administrative map of Zambia showing its provinces. by Wilson Nguru (14352167)

Subjects:

Crop pests and diseases grouped by AEZ type in which they are likely to occur. by Wilson Nguru (14352167)

Subjects:

Current crop pest and disease habitat suitability distribution in Zambia for Group A and B. by Wilson Nguru (14352167)

Subjects:

Future crop pest and disease habitat suitability distribution by 2030 under RCP 4.5. by Wilson Nguru (14352167)

Subjects:

Pests associated with selected crops and the environments conducive to their growth and development. by Wilson Nguru (14352167)

Subjects:

Trendlines of the projected changes in habitat suitability over the years for Group A and Group B crop pests and diseases under RCPs 2.6, 4.5, and 8.5. by Wilson Nguru (14352167)

Subjects:

Future crop pest and disease suitability distribution by 2050 at RCP 4.5. by Wilson Nguru (14352167)

Subjects:

Conducive environments for growth of selected pests and diseases in Zambia. by Wilson Nguru (14352167)

Subjects:

Pests and diseases of Zambia occurrence and impact. by Wilson Nguru (14352167)

Subjects:

Significantly decreased adhesion to host cells by the Δ<i>tad123</i> mutant (A) and time-dependent recovery (B). by Tra-My Duong-Nu (7300970)

Results of RF regression model validation and significance of influencing factors (desert area change and spatiotemporal characteristics of ERI). by Mingyue Sun (4638535)

ROC Results Predicted for Out of the Sample. by Fangyi Yang (18867853)

“…The model estimation results indicate that passenger patience significantly influences drop-off decisions. All models—static, dynamic, and Cox proportional hazards—achieved prediction accuracies exceeding 70%, with the dynamic model outperforming others when ample sample data is available. …”

Results of linear regression models predicting children’s internalizing and externalizing problems at T2 and T3. by Irina Jarvers (10704223)

Subjects:

Baseline patient characteristics and clinical factors. by Feras Haskiah (14204097)

Significant relationships between QOL measures on infusion day and day 7. by Jordan K. Abbott (8934239)

Subjects:

Association between severity and potential risk factors among 123 COVID-19 patients in multiple multinomial logistic regression analysis. by Yingji Chen (5258498)

Subjects: