Effect of Chemically Modified Banana Fibers on the Mechanical Properties of Poly-Dimethyl-Siloxane-Based Composites

The study presents the mechanical properties of polymer-based composites reinforced with chemically modified banana fibers, by alkalization in different concentrations of sodium hydroxide (NaOH). The fiber weight fraction has a great effect on the mechanical properties of the composites. Stiff composites were obtained at 6 wt% fiber fractions with Young’s modulus of 254.00 ±12.70 MPa. Moreover, the yield strength was 35.70 ±1.79 MPa at 6 wt% fiber fractions. However, the ultimate tensile strength (UTS) and toughness of the composites were obtained at 5 wt% fiber fractions. Statistical analyses were used to ascertain the significant different on the mechanical properties of the fibers and composites. The implication of the results is then discussed for potential applications of PDMS-based composites reinforced with chemically modified banana fibers

Political considerations in the choice of medium of instruction

Recent debates in Ghana over the English-only educational policy have brought into sharp focus ideological and political concerns, making necessary a reexamination of this all too familiar but still contested terrain. In this paper we theorize about the factors that shaped the policy, but which remained as subtexts in the Minister’s announcement. We observe that global participation and maintenance of political power could have been the motivation for the change. We suggest that this outward-orientation of the government, which policy makers placed above educational principles and pedagogical practice, is not new inpolicymaking in Ghana or in any similarly-positioned African nation. We argue for a reconsideration of the policy for ideological reasons.RésuméLe débat récent sur la politique de l'usage unique de l'anglais comme langue d'enseignement a mis en exergue des préoccupations d'ordre politique et idéologique et a nécessité une réévaluation de ce terrain si bien connu et pourtant tant controversé. Il s'agit dans cet article de formuler des hypothèses sur les facteurs ayant façonné cette politique, lesquels facteurs toutefois ne sont restés que des textes sous-jacents dans l'annonce faite par le ministre. Tout en faisant remarquer que la participation à la mondialisation et le maintien du pouvoir politique pourraient être à la base de ce changement, nous postulonsque cette approche d’ouverture extravertie adoptée par le gouvernement, et privilégiée par les décideurs aux dépens des principes d'enseignement et de la pratique pédagogique, n'est guère nouvelle dans la prise de décisions ni au Ghana ni dans tout autre pays africain ayant adopté pareille posture. Nous nous prononçons en faveur d'un nouvel examen de ladite politique pour des raisons idéologiques

A machine learning approach for predicting hurricane evacuee destination location using smartphone location data

Abstract Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoroughly exploited by existing methods. The model’s predictive power is comprehensively evaluated through a tenfold cross validation, holdout validation using Hurricane Ida (2021), and comparison with the traditional gravity model. Results suggest that the new model substantially outperforms the traditional gravity model across all performance indicators. Analysis of feature importance in the machine learning model indicates that in addition to distance and population, hurricane characteristics are important in evacuee destination choices