Advances in Cellulose Nanomaterial-based Foams for Environmental Applications

The use of metal-oxide nanoparticles adsorbents is limited to fixed-bed columns in industrial-scale water treatment applications. This limitation is commonly attributed to the tendency of nanoparticles to aggregate, the use of non-sustainable and inefficient polymeric resins as supporting materials, or a lack of adsorption capacity. Foams and aerogels derived from cellulose nanomaterials have unique characteristics, such as high porosity and low density, which enables their use in a variety of environmental applications, including water treatment. However, the overall use of cellulose nanomaterial-based foams in various environmental sectors is limited due to the high cost of production associated with time- and cost-intensive manufacturing processes such as freeze-drying and supercritical CO2 drying. In addition, additive manufacturing is a prominent technology for accurately developing and controlling micro-to-macrostructures with continuous automation; however, the use of cellulose-based materials in additive manufacturing is also limited due to its complex processing route involved in different stages of manufacturing. Hence this dissertation initially assessed the feasibility of the synthesis and immobilization of magnesium-doped amorphous iron oxide nanoparticles (IONPs) on the surface of a freeze-dried and crosslinked cellulose nanofibril (CNF) aerogel for arsenic removal from water. The adsorption kinetics and isotherms were studied for both As(III) and As(V). Further work involved the use of urea as an additive to develop a microwave-assisted thawing procedure for creating CNF-based hybrid foams at a significantly shorter time and lower energy consumption than any previously reported methods. A freezing rate-dependent mechanism for foam formation was proposed, along with a new crosslinking pathway that was confirmed by FTIR and nitrogen content analyses. The foams\u27 mechanical properties were examined in both dry and wet conditions. In addition, the dissertation provides with an investigation for the 3D-printability of a CNF paste by optimizing the solid content of CNFs with the composition of urea and carboxymethyl cellulose (CMC). The amplitude-sweep tests and zeta potential analyses demonstrated conclusively that the rheological properties of the paste are significantly influenced by the addition of urea and CMC at various concentrations. Compression and tensile strengths were evaluated, and it was discovered that a higher CMC content positively affected interlayer adhesion along the printing direction, thereby increasing the compression and tensile strengths of the structures. Using the Fourier-transform infrared spectroscopy (FTIR), a comprehensive investigation of the chemical interactions between CNF, urea, and CMC was conducted. Consequently, this method provides an economically viable alternative for promoting sustainable nanomaterials in the field of additive manufacturing, thereby creating new opportunities for increasing production scale and efficiency

Analyzing the Predictability of Source Code and its Application in Creating Parallel Corpora for English-to-Code Statistical Machine Translation

Analyzing source code using computational linguistics and exploiting the linguistic properties of source code have recently become popular topics in the domain of software engineering. In the first part of the thesis, we study the predictability of source code and determine how well source code can be represented using language models developed for natural language processing. In the second part, we study how well English discussions of source code can be aligned with code elements to create parallel corpora for English-to-code statistical machine translation. This work is organized as a “manuscript” thesis whereby each core chapter constitutes a submitted paper. The first part replicates recent works that have concluded that software is more repetitive and predictable, i.e. more natural, than English texts. We find that much of the apparent “naturalness” is artificial and is the result of language specific tokens. For example, the syntax of a language, especially the separators e.g., semi-colons and brackets, make up for 59% of all uses of Java tokens in our corpus. Furthermore, 40% of all 2-grams end in a separator, implying that a model for autocompleting the next token, would have a trivial separator as top suggestion 40% of the time. By using the standard NLP practice of eliminating punctuation (e.g., separators) and stopwords (e.g., keywords) we find that code is less repetitive and predictable than was suggested by previous work. We replicate this result across 7 programming languages. Continuing this work, we find that unlike the code written for a particular project, API code usage is similar across projects. For example a file is opened and closed in the same manner irrespective of domain. When we restrict our n-grams to those contained in the Java API we find that the entropy for 2-grams is significantly lower than the English corpus. This repetition perhaps explains the successful literature on API usage suggestion and autocompletion. We then study the impact of the representation of code on repetition. The n-gram model assumes that the current token can be predicted by the sequence of n previous tokens. When we extract program graphs of size 2, 3, and 4 nodes we see that the abstract graph representation is much more concise and repetitive than the n-gram representations of the same code. This suggests that future work should focus on graphs that include control and data flow dependencies and not linear sequences of tokens. The second part of this thesis focuses cleaning English and code corpora to aid in machine translation. Generating source code API sequences from an English query using Machine Translation (MT) has gained much interest in recent years. For any kind of MT, the model needs to be trained on a parallel corpus. We clean StackOverflow, one of the most popular online discussion forums for programmers, to generate a parallel English-Code corpora. We contrast three data cleaning approaches: standard NLP, title only, and software task. We evaluate the quality of each corpus for MT. We measure the corpus size, percentage of unique tokens, and per-word maximum likelihood alignment entropy. While many works have shown that code is repetitive and predictable, we find that English discussions of code are also repetitive. Creating a maximum likelihood MT model, we find that English words map to a small number of specific code elements which partially explains the success of using StackOverflow for search and other tasks in the software engineering literature and paves the way for MT. Our scripts and corpora are publicly available

DAMPAK KEBIJAKAN ANTI TEMBAKAU TERHADAP STRATEGI NAFKAH PETANI TEMBAKAU MADURA (Studi Kasus Desa Panaguan Kecamatan Proppo Pamekasan)

The objectives of this study is to identify and to know policies released by government which is related with demarcation of cigarette circulation and explaining maintenance strategy of tobacco’s farmer in Panaguan village, Proppo district. Research method of this study is descriptive qualitative method. Observation, interview, library research, and documentation are used as technique of collecting data. The result of this study indicates that government has released some policies of cigarette circulation demarcation. However, those policies have given negative impact for cigarette industry and its perpetrator. They have caused scale degradation of tobacco absorption from warehouse. That condition begets tobacco’s price not stable. Moreover, development of Indonesia’s cigarette industry descends progressively every year. Maintenance strategy applied by Panaguan village’s inhabitant is intensification and efficiency strategy, tumpang sari strategy, double maintenance patterns, commodity manipulation strategy, migration, and also exploit local institut

Causal Inference in Microbiomes Using Intervention Calculus

Inferring causal effects is critically important in biomedical research as it allows us to move from the typical paradigm of associational studies to causal inference, and can impact treatments and therapeutics. Association patterns can be coincidental and may lead to wrong inferences in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in health and diseases. Hence knowledge of genuine causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and interactions are essential for understanding disease mechanisms and making treatment recommendations. In this paper, we investigate fundamental causal inference techniques to measure the causal effects of various entities in a microbiome. In particular, we show how to use these techniques on microbiome datasets to study the rise and impact of antibiotic-resistance in microbiomes. Our main contributions include the following. We introduce a novel pipeline for microbiome studies, new ideas for experimental design under weaker assumptions, and data augmentation by context embedding. Our pipeline is robust, different from traditional approaches, and able to predict interventional effects without any controlled experiments. Our work shows the advantages of causal inference in identifying potential pathogenic, beneficial, and antibiotic-resistant bacteria. We validate our results using results that were previously published

Evaluation of Pedestrian Level of Service of Selected Footpath Segments of Dhaka City Using Multi-criteria Decision Making Approach

Walking is considered to be the most important mode of travel across the world particularly for a shortdistance trip Since 19 6 of the trips are made by the foot in Dhaka it is necessary to ensure a friendly walking environment in the footpath for the welfare of pedestrians of Dhaka This study aims to make a comparative analysis of Pedestrian Level of Service PLOS of selected footpath segments along Segun Bagicha road Toynbee Circular Road Mirpur Road and Baily road Pedestrian Level of Service has been determined based on ten factors path width pedestrian volume crossing facilities availability of buffer distance from vehicular traffic surface quality comfort walking environment the existence of street light The weight of each factor has been determined through the Multi-criteria analysis approach Analytical Hierarchy Process Path width Pedestrian volume and the existence of buffer are the first second and third most important factors PLOS has been determined based on the indexed value of factors and weight of factors All the sections were found to have poor PLOS The findings of the study will be helpful for transport policymakers to improve the condition of these factors to ensure a better walking condition for pedestrians of selected footpath section

Tools and Techniques Adapted for Teaching Software Engineering Topics Remotely during the COVID-19 Pandemic

To stop the spread of the COVID-19 virus, educational institutions abruptly switched from in-person to online, remote mode of teaching without giving educators the necessary tools and training. In this paper, we focus on the Software Engineering Education & Training (SEET) courses at the university levels and address questions like: What tools and techniques did they adapt to handle the modality transition challenges? What lessons they learned and what would they do differently the next time? What are the students' perspective on these, etc.? We interviewed 16 SEET educators from different countries around the world; followed by surveys of more than 300 educator and student participants. Our empirical study found some common themes of challenges, as well as suggestions on tools and techniques to overcome them

Modelling and Forecasting the Consumer Price Index in Bangladesh through Econometric Models

Persistent economic growth along with high Consumer Price Index (CPI) and low inflation is the major aim of the economic theory. This paper uses annual time series data on CPI from the period 1986 to 2018 and find the best econometric time series model for forecasting the CPI in Bangladesh. In this study different Autoregressive integrated moving average (ARIMA) model are used. To find the best ARIMA model we have used here Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). This study presents ARIMA (2, 2, 0) model to forecast the CPI in Bangladesh based on the lowest values of AIC, AICc and BIC than other ARIMA models. Based on the selected ARIMA (2, 2, 0) model we forecast the CPI in Bangladesh from period 2019 to 2025. The results of the study show that the CPI in Bangladesh is to continue an upward trend with respect to time

Phenotyping of mungbean (Vigna radiata L.) genotypes against salt stress and assessment of variability for yield and yield attributing traits

Salt tolerance is a complex polygenic trait that is genotype specific and tolerance can depend upon a plants developmental stage. To evaluate reproductive stage specific salt tolerance as well as investigate the inherent variability of mungbean (Vigna radiata L.) genotypes with respect to seed yields and yield-related traits, a pot culture experiment was conducted using 26 mungbean genotypes and exposure to salt stress (EC = 8.0 dS/m) applied at the reproductive stage, just before the opening of the first flowers. The experiment involved maintaining 100% field capacity for three weeks and used a randomized complete block design with three replicates. Data were collected, included days to maturity, plant height (cm), number of pod-bearing branches per plant, number of pods per plant, pod length (cm), number of seeds per pod, 100-seed weight (g) and seed yield per plant (g). Salt stress led to a significant (p<0.001) decrease in seed yield per plant, with yields of the genotypes BMX 11116, BMX 11176, BMX 11140, BMX 11111 and BMX 11163 being the least impacted by exposure to salt. Principal component analysis revealed that the first two components explained 63.5% of the total variation among the mungbean genotypes. Seed yield per plant showed a significant positive correlation with days to maturity, number of pod-bearing branches per plant, number of pods per plant, pod length (cm), number of seeds per pod, and 100-seed weight (g). Cluster analysis grouped the 26 genotypes into five distinct clusters, where the tolerant genotypes placed in cluster I. Based on their stress tolerance indices BARI Mung-6, BMX 11176, BMX 11116, and BMX 11140 were categorized as tolerant genotypes, were selected for further study under direct field conditions and are recommended for the genetic improvement of salt stress tolerance in mungbean