Getting Back The Lost

Getting Back the Lost is an animated graduate thesis film with a total running time of 7 minutes and 13 seconds. The film is about a girl seeking hope in a ruined and destroyed environment. Nowadays, the global environmental problem, which is due to human activities, is becoming increasingly serious. Human beings have overused natural resources in a continuous and reckless way, without caring about their harmful and detrimental effects on this earth. Getting Back the Lost is a story that takes place in a former land of oil extraction and production. It is a place that represents what the rest of the world is like – desolate, dangerous, and highly polluted. It is devoid of others, except for the main character of the story, a girl who lives alone in the only place that has some nature left. There’s only one tree standing in the whole area that protects a tree house where the girl lives as she is performing experiments on the planting of seeds. In the beginning of the story, the girl, who is almost robot-like, is trying to find a way to plant seeds in the polluted ground around her to save her small piece of land. Her experiments are failing. The plants she grows are dying. She also finds she has run out of seeds to plant new vegetation and so she needs to obtain more. The girl ventures beyond her safe area to explore the destroyed world around her with the hope that she will find more seeds somewhere. As she travels further and further away from her home, she journeys onto the “ruined land” on the other side of the mountains where petroleum fields and factories are deserted and in ruins. She comes upon a site that shows traces of recent human occupation, and she finds seeds and collects them. The girl rushes away from this place because of an unexpected earthquake. During her way back home, the seeds she has collected in a bottle fall out, leaving a trail of seeds behind her. When she arrives at the door of her home, she realizes the seeds in the bottle are gone. She looks back in the direction she ran and sees the seeds spread out on the ground. She feels extremely sad when she finds this. But the seeds on the ground suddenly sink into the earth and magically grow into small plants, and then the whole area becomes green. Getting Back the Lost is a 3D animation with a 2D graphic style. It is produced primarily in Autodesk Maya, The Foundry Mari, The Foundry Nuke, Adobe Photoshop and Adobe Premiere Pro. This paper outlines the entire film creation process from the idea development through the final post production stage. It describes all my intentions, obstacles, failures and successes, as well as the technical specifics of the process

Sentiment Analysis of Twitter Data to Explore Customers’ Feedback Towards US Airline Services

Among the competitive airline industries, understanding the customers' opinions and improving them accordingly is very important for improving customer satisfaction to retain old customers and attract new customers. This research aims to carry out sentiment analyses on Twitter data relevant to the US airlines to determine the drivers behind the sentiment of each customer review. The identification of the drivers allows appropriate recommendations to be suggested to each airline to improve their services and the utilisation of machine learning methods enables the generation of an accurate model that could predict and classify the sentiments of customer reviews. The research process begins with downloading customer reviews related to tweets data from the Kaggle dataset, which includes 14,640 tweets relate to six major US Airlines. With the use of preliminary charts, according to the results, negative sentiment accounts for the majority of each airline’s tweet reviews. Hence, the utilisation of Word Cloud and other visualization tools had led to the discovery of the reasons for negative tweets and the findings of this research revealed negative sentiment drivers of the US airlines which include customer services, late flights, cancelled flights, lost or damaged luggage, and flight booking problems, etc. Then, based on these reasons, related recommendations can be provided to improve the customer satisfaction of each airline. Then, to effectively monitor and classify customers' feedback, a model has been built in this project to achieve accurate prediction and efficient classification of customer reviews. At first, the 80-20 rule was used to split the data. After that, two text feature extraction methods (Bag of words and TF-IDF) were selected. The results generated from each of the text feature extraction methods were used separately in four classifiers (Support Vector Machine, LightGBM, Naïve Bayes, and Random Forest) to build models that predict tweet sentiments. In the end, each extractor-classifier combination was tested for accuracy and was compared against each other. From the comparison, the most accurate predicted model is the SVM classifier based on TF-IDF feature extraction. In conclusion, performing sentiment analysis to customer reviews on Twitter of airlines can provide valuable insights to airline owners on understanding the factors that influence customer reviews and help the airline owners to make data-driven strategic decisions. Keywords: Sentiment Analysis, Twitter, Customer Feedback, Data Mining, Machine Learning Method

Multi-task learning for aspect level semantic classification combining complex aspect target semantic enhancement and adaptive local focus

Aspect-based sentiment analysis (ABSA) is a fine-grained and diverse task in natural language processing. Existing deep learning models for ABSA face the challenge of balancing the demand for finer granularity in sentiment analysis with the scarcity of training corpora for such granularity. To address this issue, we propose an enhanced BERT-based model for multi-dimensional aspect target semantic learning. Our model leverages BERT's pre-training and fine-tuning mechanisms, enabling it to capture rich semantic feature parameters. In addition, we propose a complex semantic enhancement mechanism for aspect targets to enrich and optimize fine-grained training corpora. Third, we combine the aspect recognition enhancement mechanism with a CRF model to achieve more robust and accurate entity recognition for aspect targets. Furthermore, we propose an adaptive local attention mechanism learning model to focus on sentiment elements around rich aspect target semantics. Finally, to address the varying contributions of each task in the joint training mechanism, we carefully optimize this training approach, allowing for a mutually beneficial training of multiple tasks. Experimental results on four Chinese and five English datasets demonstrate that our proposed mechanisms and methods effectively improve ABSA models, surpassing some of the latest models in multi-task and single-task scenarios

Subsurface Engineering Induced Fermi Level De-pinning in Metal Oxide Semiconductors for Photoelectrochemical Water Splitting

Photoelectrochemical (PEC) water splitting is a promising approach for renewable solar light conversion. However, surface Fermi level pinning (FLP), caused by surface trap states, severely restricts the PEC activities. Theoretical calculations indicate subsurface oxygen vacancy (sub-O-v) could release the FLP and retain the active structure. A series of metal oxide semiconductors with sub-O-v were prepared through precisely regulated spin-coating and calcination. Etching X-ray photoelectron spectroscopy (XPS), scanning transmission electron microscopy (STEM), and electron energy loss spectra (EELS) demonstrated O-v located at sub similar to 2-5 nm region. Mott-Schottky and open circuit photovoltage results confirmed the surface trap states elimination and Fermi level de-pinning. Thus, superior PEC performances of 5.1, 3.4, and 2.1 mA cm(-2) at 1.23 V vs. RHE were achieved on BiVO4, Bi2O3, TiO2 with outstanding stability for 72 h, outperforming most reported works under the identical conditions

Relationship Between Syrinx Resolution and Cervical Sagittal Realignment Following Decompression Surgery for Chiari I Malformation Related Syringomyelia Based on Configuration Phenotypes

Objective Combined with different configuration types of syringomyelia, to analyze the correlation between syrinx resolution and changes in cervical sagittal alignment following Foramen magnum and Magendie dredging (FMMD) for syringomyelia associated with Chiari I malformation (CM-I), and to further explore the respective relationship with clinical outcome. Methods A consecutive series of 127 patients with CM-I and syringomyelia who underwent FMMD in our center met the inclusion criteria of this study. Their clinical records and radiologic data were retrospectively reviewed. The Japanese Orthopedic Association (JOA) scoring system and the Chicago Chiari Outcome Scale (CCOS) were used to evaluate the surgical efficacy. The phenotypes of syringomyelia and the clinical characteristics of the patients were analyzed according to grouping by cervical curvature at baseline. Results The preoperative straight or kyphotic cervical alignment is more common in the moniliform syrinx. After surgery, the syrinx resolution and cervical sagittal realignment in the moniliform group are more obvious, and the corresponding prognosis is relatively better. Spearman correlation analysis showed that the ΔS/C ratio (the change ratio of syrinx/cord) was positively correlated with the CCOS (p = 0.001, r = 0.897) and ΔC2–7A (the change of lower cervical angle) (p = 0.002, r = 0.560). There was also a correlation between the ΔJOA score (the change rate of the JOA score) and ΔC2–7A (p = 0.012, r = 0.467). Conclusion After decompression surgery, syrinx resolution may coexist with the changes in the subaxial lordosis angle, especially for syrinx in moniliform type, and the relationship between syrinx resolution and cervical sagittal realignment might be valuable for evaluating the surgical outcome

Sentiment Analysis of Twitter Data to Explore Customers’ Feedback Towards US Airline Services

Among the competitive airline industries, understanding the customers' opinions and improving them accordingly is very important for improving customer satisfaction to retain old customers and attract new customers. This research aims to carry out sentiment analyses on Twitter data relevant to the US airlines to determine the drivers behind the sentiment of each customer review. The identification of the drivers allows appropriate recommendations to be suggested to each airline to improve their services and the utilisation of machine learning methods enables the generation of an accurate model that could predict and classify the sentiments of customer reviews. The research process begins with downloading customer reviews related to tweets data from the Kaggle dataset, which includes 14,640 tweets relate to six major US Airlines. With the use of preliminary charts, according to the results, negative sentiment accounts for the majority of each airline’s tweet reviews. Hence, the utilisation of Word Cloud and other visualization tools had led to the discovery of the reasons for negative tweets and the findings of this research revealed negative sentiment drivers of the US airlines which include customer services, late flights, cancelled flights, lost or damaged luggage, and flight booking problems, etc. Then, based on these reasons, related recommendations can be provided to improve the customer satisfaction of each airline. Then, to effectively monitor and classify customers' feedback, a model has been built in this project to achieve accurate prediction and efficient classification of customer reviews. At first, the 80-20 rule was used to split the data. After that, two text feature extraction methods (Bag of words and TF-IDF) were selected. The results generated from each of the text feature extraction methods were used separately in four classifiers (Support Vector Machine, LightGBM, Naïve Bayes, and Random Forest) to build models that predict tweet sentiments. In the end, each extractor-classifier combination was tested for accuracy and was compared against each other. From the comparison, the most accurate predicted model is the SVM classifier based on TF-IDF feature extraction. In conclusion, performing sentiment analysis to customer reviews on Twitter of airlines can provide valuable insights to airline owners on understanding the factors that influence customer reviews and help the airline owners to make data-driven strategic decisions. Keywords: Sentiment Analysis, Twitter, Customer Feedback, Data Mining, Machine Learning Method

Urban Green Space Planning and Design Based on Big Data Analysis and BDA-UGSPD Model

Green cities are described as the environmental influences by expanding recycling, decreasing waste, increasing housing density, lowering emissions while intensifying open space, and boosting sustainable local businesses. Green infrastructures (GI) are progressively related to urban water management for long-term transitions and immediate solutions towards sustainability. Urban green spaces (UGS) play a vital role in conserving urban environment sustainability by giving various ecology services. In this study, big data analytics-based urban green space planning design (BDA-UGSPD) has been introduced. Luohe city and the Shali River area have been chosen as the study area owing to the high number and a considerable assortment of UGS. Monitoring has been conducted in the Shali river to evaluate water quality for irrigation for agriculture. The Master Plan Scenario had a compact green space system, and the urban land use layout has been categorized by systematization and networking, and it did not consider the service capacity of green spaces. The Planning Guidance Scenario initialized constraint states, which provide more rigorous and effective urban spaces. It enhanced the service functions of the green space model layout. The simulation findings illustrate that the proposed BDA-UGSPD model enhances the land-use classification accuracy ratio by 92.0%, probability ratio by 90.6%, decision-making ratio by 95.0%, climate change adaptation ratio by 94.5%, water quality assessment ratio by 95.9%, and reduces the root mean square error ratio by 9.7% compared to other popular approaches

Association between MTR A2756G polymorphism and susceptibility to congenital heart disease: A meta-analysis.

The association between methionine synthase (MTR) A2756G (rs1805087) polymorphism and the susceptibility to congenital heart disease (CHD) has not been fully determined. A meta-analysis of case-control studies was performed to systematically evaluate the above association. Studies were identified by searching the PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and WanFang databases from inception to June 20, 2021. Two authors independently performed literature search, data extraction, and quality assessment. Predefined subgroup analyses were carried out to evaluate the impact of the population ethnicity, source of healthy controls (community or hospital-based), and methods used for genotyping on the outcomes. A random-effects model was used to combine the results, and 12 studies were included. Results showed that MTR A2756G polymorphism was not associated with CHD susceptibility under the allele model (odds ratio [OR]: 0.96, 95% confidence interval [CI]: 0.86 to 1.07, P = 0.43, I2 = 4%), heterozygote model (OR: 0.95, 95% CI: 0.84 to 1.07, P = 0.41, I2 = 0%), homozygote model (OR: 1.00, 95% CI: 0.64 to 1.55, P = 0.99, I2 = 17%), dominant genetic model (OR: 0.95, 95% CI: 0.84 to 1.07, P = 0.41, I2 = 0%), or recessive genetic model (OR: 0.94, 95% CI: 0.62 to 1.43, P = 0.32, I2 = 13%). Consistent results were found in subgroup analyses between Asian and Caucasian populations in studies with community and hospital-derived controls as well as in studies with PCR-RFLP and direct sequencing (all P values for subgroup differences > 0.05). In conclusion, current evidence does not support an association between MTR A2756G polymorphism and CHD susceptibility