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Daily and annual maximum wind speed quantiles can be estimated using extreme value theory for any metrological site of interest. These estimates are of vast importance for modelling and predicting maximum wind speed. This paper develops an efficient modelling paradigm of extreme winds by analysing daily and annual maximum wind speed data via frequentist and Bayesian methodologies. For this purpose, the generalized extreme value (GEV) model is used for yearly maxima, and the generalized Pareto distribution (GPD) is used for daily exceedance over a high threshold. In frequentist inference, the parameters of both models are estimated using the maximum likelihood and linear moments method. In contrast, the Bayesian Markov Chain Monte Carlo procedure with the Metropolis–Hasting algorithm is used. In addition, the informative priors for both models are constructed empirically using historical records of wind speed data from five other weather stations of Pakistan and one belonging to India. The results show that the Bayesian modelling provides apparent benefits in terms of improved accuracy in the estimation of the parameters as well as return levels of both distributions. Furthermore, the Bayesian analysis expresses that posterior inference might be affected by the choice of priors used to formulate the informative priors. Overall, based on assessment measures, the GPD fitted through Bayesian informative priors provides an efficient estimation strategy in terms of precision than other frameworks when uncertainty in parameters and return levels are taken into account. Our methodology can be implemented easily to other regions by considering the prior information from the border area stations of other countries (e.g., China, Afghanistan, India, and Iran). Moreover, the return level estimates of the GPD based on informative Bayesian priors are very beneficial in policymaking and wind energy generation engineering for the Thatta region of the country.

The 3M National Teaching Fellowship (3MNTF) has been part of the Canadian higher education landscape for the past 31 years and has grown to be a community of 328 fellows, with up to ten more being added each year. As part of the 30-year anniversary of the Fellowship, we initiated a study to understand its impacts on the higher education community by examining the effect that the Fellowship has had on individual winners, the influence that fellows have been able to exert in their institutions after being awarded the 3M Fellowship, and the influence that the 3M National Teaching Fellowship program has had nationally and internationally. To identify the various impacts of the 3MNTF, we conducted focus groups with the 2012 cohort, 3M retreat facilitators and coordinators, and the representative from 3M Canada, as well as the new fellows from the 2013 cohort. In 2014, we conducted two focus groups with senior university administrators and educational developers. In 2015, we developed and administered an online survey to faculty, administrators, educational developers, and students at a number of Canadian universities. We found that the 3M is one of the most recognizable teaching awards in Canada's higher education landscape. The structure of the fellowship has helped to shape local and international teaching awards, while individual fellows often provide mentorship to future leaders in education.

Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of wheat where PD was −55.01% with 50% nitrogen–phosphorus–potassium (NPK) and 50% animal manure applied under the CT treatment. However, after calibration by adjusting default values of soil and crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (e.g., average PD ranged from −2.81% to −6.17%). DNDC also predicted the effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha−1). DNDC simulated SOC trends for rice–wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice–wheat systems.

This paper summarizes the discourse on student ratings of teaching in higher education. It reconceptualizes student ratings within a larger process of promoting quality in teaching and student learning. As students engage in productive dialogue with teachers and administrators, metrics drawn from decontextualized surveys are admittedly a vital resource. Our paper contends, however, that student ratings can only become a tool for enhancement when they feed reflective conversations about improving the learning process and when these conversations are informed by the scholarship of teaching and learning. We illustrate this view with a case study of an Engineering Faculty that uses three interconnected initiatives to support an evolving conversation on quality among teachers in partnership with students. The role of student engagement in enhancement efforts is discussed as well as potential challenges to implementation. Our purpose is to spark wider discussion of a systems perspective on student ratings that supports a coherent discourse towards positive change.

The 3M National Teaching Fellowship is a national teaching award program that has recognized over 300 teachers at more than 80 Canadian universities for their teaching excellence and outstanding educational leadership. Despite its rich, 30 plus-year history, its impact has remained largely anecdotal. In this study, we build on Hannah and Lester's (2009) original, multilevel approach that looks at interactions between the individual, network, and systems levels to explore the impact of the 3M National Teaching Fellowship program on furthering and enriching teaching and learning in higher education. Through the analysis of a large collection of program artefacts corroborated with in-depth interviews with 11 fellows (key informants), we were able to gain a deeper understanding of the ways in which the program has had impact at the individual (micro), departmental (meso), institutional (macro), and national/international (mega) levels. In this article, we outline our scholarly exploration of the program's influence and explore its role as a high-impact community of practice in higher education.

In 2016 the 3M National Teaching Fellowship reached a milestone in celebrating the 30th anniversary of the first fellowship in 1986. The fellowship is the premier award of the Society for Teaching and Learning in Higher Education and each year up to 10 fellows are announced. Thus far, there has not been a systematic review or evaluation of the fellowship program. This study is a starting point to engage the various stakeholders and gather their perspectives regarding the fellowship program. This paper highlights the findings of a national questionnaire that targeted 3M National Teaching Fellows, administrators, educational developers, faculty, and students. The findings provide a snapshot of the awareness of the fellowship, the impact of fellows on their institutions and beyond, and recommendations for the future of the program.

The present study takes a novel quantitative approach to examine whether the change mechanisms hypothesized by conceptual change theories are involved in reconceptualizing teaching from a teacher-centred to a student-centred approach. We assessed changes in approaches to teaching, measured with the Approaches to Teaching Inventory-Revised (ATI-R), in emerging higher education faculty (N = 99) after a professional learning seminar. Latent variable analyses revealed coherent and interpretable changes in the underlying factor structure of the ATI-R, with students demonstrating more differentiated teacher-centred and student-centred concepts after the intervention. These changes are interpreted as a structural revision of approaches to teaching, consistent with conceptual change theories. Our observations not only support the importance of conceptual change in approaches to teaching but point to change mechanisms so that professional learning programmes may explicitly target them mechanisms to effect these desired changes.

In survey sampling, we aspire to obtain sound and consistent responses, which are not achieved while dealing with sensitive issues. Frequently, respondents give elusive responses to sensitive questions, so we employ randomized response techniques that facilitate finding an appropriate proportion of socially sensitive characteristics. In the present study, we proposed a hidden logit estimation method using Huang, Warner, and Mangat’s randomized response techniques. This study depicts that the estimates become closer to the standard logits as the values of p increase. We found that the hidden logit estimates obtained by the Huang randomized response technique were nearer to the parametric values, in contrast to the other existing techniques, and we demonstrate an increase in accuracy as well. The simulation-based AIC and SIC values are used to assess model performance. We found that the Huang model is the best model for the proposed hidden logit method. This paper contributes towards the application of logistic models in the case of sensitive or socially stigmatized issues.

The software development process mostly depends on accurately identifying both essential and optional features. Initially, user needs are typically expressed in free-form language, requiring significant time and human resources to translate these into clear functional and non-functional requirements. To address this challenge, various machine learning (ML) methods have been explored to automate the understanding of these requirements, aiming to reduce time and human effort. However, existing techniques often struggle with complex instructions and large-scale projects. In our study, we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier (FNRC). By combining the traditional random forest algorithm with the Accuracy Sliding Window (ASW) technique, we develop optimal sub-ensembles that surpass the initial classifier's accuracy while using fewer trees. Experimental results demonstrate that our FNRC methodology performs robustly across different datasets, achieving a balanced Precision of 75% on the PROMISE dataset and an impressive Recall of 85% on the CCHIT dataset. Both datasets consistently maintain an F-measure around 64%, highlighting FNRC's ability to effectively balance precision and recall in diverse scenarios. These findings contribute to more accurate and efficient software development processes, increasing the probability of achieving successful project outcomes. [ABSTRACT FROM AUTHOR]

Whitespotted bambooshark, Chiloscyllium plagiosum (Anonymous [Bennett], 1830), is a major shark species caught in waters off Sabah, Malaysia. Despite the massive landing amount, its biological and stock status information is limited. In 2015–2016, the elasmobranch data collection was conducted in Southeast Asia, including Sabah. A yield-per-recruit (YPR) and spawning-per-recruit (SPR) analyses were performed to assess this data-deficient species’ stock status. The growth parameter, average maximum length, L∞, and growth rate, K, for males and females were 81.13 cm and 0.21 year-1 , and 84.30 cm and 0.18 year-1 , respectively. Limit and target biological reference points, maximum fishing mortality and fishing mortality corresponding to 10 % of YPR slope (Fmax and F0.1, respectively) for YPR, and fishing mortality corresponding to 20 % and 30 % of spawning stock remained (F20% and F30%, respectively) for SPR, were calculated. The results suggest neither growth nor recruit overfishing was occurring. Monitoring and surveillance of existing management measures are necessary to ensure sustainable utilisation of the stock.