Parameterized monads in linguistics

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.This dissertation follows the formal semantics approach to linguistics. It applies recent developments in computing theories to study theoretical linguistics in the area of the interaction between semantics and pragmatics and analyzes several natural language phenomena by parsing them in these theories. Specifically, this dissertation uses parameterized monads, a particular theoretical framework in category theory, as a dynamic semantic framework to reinterpret the compositional Discourse Representation Theory(cDRT), and to provide an analysis of donkey anaphora. Parameterized monads are also used in this dissertation to interpret information states as lists of presuppositions, and as dot types. Alternative interpretations for demonstratives and imperatives are produced, and the conventional implicature phenomenon in linguistics substantiated, using the framework. Interpreting donkey anaphora shows that parameterized monads is able to handle the sentential dependency. Therefore, this framework shows an expressive power equal to that of related frameworks such as the typed logical grammar and the dynamic predicate logic. Interpreting imperatives via parameterized monads also provides a compositional dynamic semantic analysis which is one of the main approaches to analysing imperatives

Modelling supply chain sustainability : a case study in New Zealand forestry : a thesis presented in partial fulfilment of the requirements for the degree of Master of Supply Chain Management at Massey University, Palmerston North, New Zealand

Permission was obtained for the re-use of Figures 15, 16, 17 & 18, and for the Figure in Appendix B. Figure 8 was adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Supply chains play a key role in business nowadays, as they are facilitating about 80% of the world trade. Supply chain operations are making great impacts not only on the economy, but also on the society and the environment through all their activities along the chain. In supply chain management, supply chain network design is the backbone because it determines on a strategic level the quantity, location, capacity, and flows for all supply chain facilities. Businesses have been gradually shifting toward sustainability, but unpredictable happenings like the ongoing global COVID 19 disease are forcing enterprises to adapt more sufficiently to survive and grow. It is recently observed that there are some fundamental changes in a number of supply chain networks when many physical stores are replaced by online shopping websites due to the pandemic. An efficient tool is in real need in supporting the change in supply chain network. In making decision for a supply chain network redesign, several studies have documented that there is still a lack of holistic assessment when most published papers in the field only focused on the economic or environment aspect and very few addressed all three sustainability aspects including the social one, the quantifiable justification to support decision making is still inadequate, and the integrity of the decision making processed is challenged by the possibility of manipulation. On the other hand, some approaches like the Triple Bottom Line, the Discrete Event Simulation and the Multi-criteria Decision Making method are not utilised fully. This study set out to examine a modelling method, which is the combination of Triple Bottom Line, Discrete Event Simulation and Multi-criteria Decision Making, for the sustainability assessment of a supply chain network redesign, to evaluate the influences of the method on the holistic approach, quantifiable justification and integrity of the results. The research was designed as a formal ex post facto longitudinal simulation case study of a forestry supply chain redesign in New Zealand. The principal findings of this study are that the modelling method of Triple Bottom Line, Discrete Event Simulation and Multi-criteria Decision Making could provide a holistic assessment by addressing all three sustainability aspects. The method could demonstrate a quantifiable justification to support decision making by the showing the results in numerical form which could be ranked. The method could also secure the integrity of the decision making processed by the participation of stakeholders. In addition, the findings indicate that the Discrete Event Simulation could also be utilised in strategic decision making, not only in operational and tactical levels as reported by previous research. Therefore, this study should be of value for practitioners wishing to improve their daily supply chain operations, for managers plaining new strategy and investing new supply chain network design, for policy makers considering recommendation and/or requirement in assessment of publicly funded projects

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas

"Cultural additivity" and how the values and norms of Confucianism, Buddhism, and Taoism co-exist, interact, and influence Vietnamese society: A Bayesian analysis of long-standing folktales, using R and Stan

Every year, the Vietnamese people reportedly burned about 50,000 tons of joss papers, which took the form of not only bank notes, but iPhones, cars, clothes, even housekeepers, in hope of pleasing the dead. The practice was mistakenly attributed to traditional Buddhist teachings but originated in fact from China, which most Vietnamese were not aware of. In other aspects of life, there were many similar examples of Vietnamese so ready and comfortable with adding new norms, values, and beliefs, even contradictory ones, to their culture. This phenomenon, dubbed "cultural additivity", prompted us to study the co-existence, interaction, and influences among core values and norms of the Three Teachings--Confucianism, Buddhism, and Taoism--as shown through Vietnamese folktales. By applying Bayesian logistic regression, we evaluated the possibility of whether the key message of a story was dominated by a religion (dependent variables), as affected by the appearance of values and anti-values pertaining to the Three Teachings in the story (independent variables).Comment: 8 figures, 35 page

Lecturers' adoption to use the online Learning Management System (LMS): Empirical evidence from TAM2 model for Vietnam

Online training has been a common form of training all over the world for many years ago; however, it is only a side choice alongside offline training. Not only students but also lecturers prefer offline training to online training. However, in some cases of force majeure, specifically the nCov-19 flu pandemic, online training is considered the best way to teach. This study is based on the Technology Acceptance Model 2 (TAM2) to learn about the lecturers' adoption of using the learning management system (LMS) at universities in Vietnam. Mixed research methods are used to achieve the research objectives. Online group discussions, as well as online surveys, were conducted to collect data to analyze and test the hypotheses as well as the theoretical model. The results of the study are similar to the conclusions of TAM2. Thereby, the study proposes managerial implications to improve the lecturers' adoption