Experimental characterisation of fatigue damage in single Z-pins

Z-pins have been shown to significantly improve delamination resistance and impact strength of carbon fibre reinforced (CFRP) composites. In this paper, an experimental investigation of the influence of different fatigue parameters (mean opening/sliding displacement, amplitude, frequency, number of cycles) on the through-thickness reinforcement (TTR) is presented. For mode I, it is shown that the degradation on pin behaviour during fatigue is mostly affected by the applied displacement amplitude. The degradation is primarily caused by surface wear. Due to the brittleness of the Z-pins, mode II fatigue does not have a significant effect for very small sliding displacements. Exceeding a critical displacement causes the pin to rupture within the very first cycles

Lily Perennializing in Ithaca, Louisiana and Holland

In May 2006, we published a newsletter (number 10) where we first reported on our experiences with multi-year flowering of a variety of “cut flower” hybrid lilies at Cornell’s outdoor trialing site, Bluegrass Lane, in Ithaca NY. In 2002-2003, we planted a range of LA, Asiatic and Oriental hybrid lilies to investigate perennialization. While not a formal trial, the 2006 newsletter showed that a range of hybrids, primarily bred for cut flower use, performed very well in outdoor garden situations in upstate New Yor

Predicting complex system behavior using hybrid modeling and computational intelligence

“Modeling and prediction of complex systems is a challenging problem due to the sub-system interactions and dependencies. This research examines combining various computational intelligence algorithms and modeling techniques to provide insights into these complex processes and allow for better decision making. This hybrid methodology provided additional capabilities to analyze and predict the overall system behavior where a single model cannot be used to understand the complex problem. The systems analyzed here are flooding events and fetal health care. The impact of floods on road infrastructure is investigated using graph theory, agent-based traffic simulation, and Long Short-Term Memory deep learning to predict water level rise from river gauge height. Combined with existing infrastructure models, these techniques provide a 15-minute interval for making closure decisions rather than the current 6-hour interval. The second system explored is fetal monitoring, which is essential to diagnose severe fetal conditions such as acidosis. Support Vector Machine and Random Forest were compared to identify the best model for classification of fetal state. This model provided a more accurate classification than existing research on the CTG. A deep learning forecasting model was developed to predict the future values for fetal heart rate and uterine contractions. The forecasting and classification algorithms are then integrated to evaluate the future condition of the fetus. The final model can predict the fetal state 4 minutes ahead to help the obstetricians to plan necessary interventions for preventing acidosis and asphyxiation. In both cases, time series predictions using hybrid modeling provided superior results to existing methods to predict complex behaviors”--Abstract, page iv

Faculty Perceptions of Self-efficacy in Remote Teaching during COVID-19 Pandemic Lockdown in a Selected University in Sub-Saharan Africa

The major purpose of this study was to establish the faculty perceptions of self-efficacy in remote teaching during COVID-19 Pandemic lockdown in a selected University in Sub-Saharan Africa. A quantitative survey method was used to collect data, analyse, and interpret results. The modified 24-items, 5 point Likert Teachers’ Self-Efficacy Scale (TSES) developed by Tschannen-Moran and Woolfolk Hoy (as cited in Butucha, 2010) was used to gather data. It consisted of possible responses ranging from 1 = nothing to 5 = a great deal. Participants were 69 male (68.3%) and 32 female (31.7%) faculty members drown from a total of 145 across all schools in the selected university. The survey instrument assessed self-efficacy in three areas; instructional strategies, classroom management and student engagement. Data was gathered immediately after the faculty had completed their remote teaching and administered the final examinations online while still in the CIOVID-19 pandemic Lockdown.  Since all faculty members were in the lockdown, online survey was sent out to them in google forms. Using excel for Windows version 10, a statistical analysis of the data was done. Descriptive statistics such as frequency, percentage, mean and standard deviation were generated to answer the research question. Results revealed that the faculty believed that they can do quite a bit in student engagement (M=4.03, SD=0.81), instructional strategies (M=3.98, SD=0.80) and classroom management (M=3.93, SD =0.79), in teaching remotely during the COVID 19 lockdown. The study recommends  that since technology use in the classroom is now inevitable, the faculty should make intentional efforts to move from doing quite a bit to a great deal in online instructional strategies, classroom management and student engagement

Solar Desalination

This study evaluated the feasibility of utilizing low-grade heat sources such as solar energy and waste heat from industrial processes for desalination. The premise of the approach is that saline waters can be desalinated by evaporation and condensation of fresh water at near-ambient temperatures at low pressures. Low pressures can be achieved naturally in the head space of water columns of height equal to the local barometric head. By connecting the head space of such a saline water column to that of a distilled water column, and by maintaining the temperature of the former about 15-20 degrees C above that of the latter, fresh water can be evaporated from the saline column and condensed in the distilled water column. In this study, it is proposed to use thermal energy storage (TES) system to heat the head space of the saline water column. The TES can be maintained at the desired temperature using solar energy and/or waste heat from thermal power plants, refrigeration plants or air conditioning units. This paper presents an integrated process model developed to evaluate the feasibility of combining solar energy with an absorption refrigeration system (ARS) to provide the energy to the TES. Results of this study show that the heat rejected by an existing ARS of cooling capacity of 3.25 kW (~1 ton of refrigeration) is adequate to produce desalinated water at a rate of 5 kg/hr, with an additional energy input of 150 kJ/kg of desalinated water. The total solar panel area required for this application was 25 m2 . Performance curves and guidelines for preliminary design of such an integrated system are presented