OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment Analysis (ABSA) delves into understanding sentiments specific to distinct elements within textual content. It aims to analyze user-generated reviews to determine a) the target entity being reviewed, b) the high-level aspect to which it belongs, c) the sentiment words used to express the opinion, and d) the sentiment expressed toward the targets and the aspects. While various benchmark datasets have fostered advancements in ABSA, they often come with domain limitations and data granularity challenges. Addressing these, we introduce the OATS dataset, which encompasses three fresh domains and consists of 20,000 sentence-level quadruples and 13,000 review-level tuples. Our initiative seeks to bridge specific observed gaps: the recurrent focus on familiar domains like restaurants and laptops, limited data for intricate quadruple extraction tasks, and an occasional oversight of the synergy between sentence and review-level sentiments. Moreover, to elucidate OATS's potential and shed light on various ABSA subtasks that OATS can solve, we conducted in-domain and cross-domain experiments, establishing initial baselines. We hope the OATS dataset augments current resources, paving the way for an encompassing exploration of ABSA.Comment: Initial submissio

SeqVItA: Sequence Variant Identification and Annotation Platform for Next Generation Sequencing Data

The current trend in clinical data analysis is to understand how individuals respond to therapies and drug interactions based on their genetic makeup. This has led to a paradigm shift in healthcare; caring for patients is now 99% information and 1% intervention. Reducing costs of next generation sequencing (NGS) technologies has made it possible to take genetic profiling to the clinical setting. This requires not just fast and accurate algorithms for variant detection, but also a knowledge-base for variant annotation and prioritization to facilitate tailored therapeutics based on an individual's genetic profile. Here we show that it is possible to provide a fast and easy access to all possible information about a variant and its impact on the gene, its protein product, associated pathways and drug-variant interactions by integrating previously reported knowledge from various databases. With this objective, we have developed a pipeline, Sequence Variants Identification and Annotation (SeqVItA) that provides end-to-end solution for small sequence variants detection, annotation and prioritization on a single platform. Parallelization of the variant detection step and with numerous resources incorporated to infer functional impact, clinical relevance and drug-variant associations, SeqVItA will benefit the clinical and research communities alike. Its open-source platform and modular framework allows for easy customization of the workflow depending on the data type (single, paired, or pooled samples), variant type (germline and somatic), and variant annotation and prioritization. Performance comparison of SeqVItA on simulated data and detection, interpretation and analysis of somatic variants on real data (24 liver cancer patients) is carried out. We demonstrate the efficacy of annotation module in facilitating personalized medicine based on patient's mutational landscape. SeqVItA is freely available at https://bioinf.iiit.ac.in/seqvita

Shrinkage and Consolidation Characteristics of Chitosan-Amended Soft Soil: A Sustainable Alternate Landfill Liner Material

Kuttanad is a region that lies in the southwest part of Kerala, India, and possesses soft soil, which imposes constraints on many civil engineering applications owing to low shear strength and high compressibility. Chemical stabilizers such as cement and lime have been extensively utilized in the past to address compressibility issues. However, future civilizations will be extremely dependent on the development of sustainable materials and practices such as the use of bio-enzymes, calcite precipitation methods, and biological materials as a result of escalating environmental concerns due to carbon emissions of conventional stabilizers. One such alternative is the utilization of biopolymers. The current study investigates the effect of chitosan (biopolymer extracted from shrimp shells) in improving the consolidation and shrinkage characteristics of these soft soils. The dosages adopted are 0.5%, 1%, 2%, and 4%. One-dimensional fixed ring consolidation tests indicate that consolidation characteristics are improved upon the addition of chitosan up to an optimum dosage of 2%. The coefficient of consolidation increases up to seven times that of untreated soil, indicating the acceleration of the consolidation process by incorporating chitosan. The shrinkage potential is reduced by 11% after amendment with 4% chitosan and all the treated samples exhibit zero signs of curling. Based on the findings from consolidation and shrinkage data, carbon emission assessments are carried out for a typical landfill liner amended with an optimum dosage of chitosan. In comparison to conventional stabilizers like cement and lime, the results indicate that chitosan minimized carbon emissions by 7.325 times and 8.754 times, respectively

Dynamic Simulation of Green Ammonia Synthesis Plant

The carbon emissions from human activities are causing significant harm to the planet, leading to increased temperatures, melting of polar ice caps, rising sea levels, and other negative impacts on the environment. One promising solution is the use of green hydrogen as a fuel source, which could have a much lower carbon footprint than traditional fossil fuels. The production of hydrogen can be achieved through various methods, including the electrolysis of water, which splits water molecules into hydrogen and oxygen. To mitigate these effects and ensure a sustainable future, countries are taking various measures to reduce their carbon footprint, including increasing the use of clean energy sources and improving energy efficiency. Hydrogen storage and transportation pose major challenges since it is the one of the lightest gases leading to low energy densities.Ammonia is emerging as a hydrogen carrier due to its high gravimetric storage densities of hydrogen. It is produced through the combination of hydrogen and nitrogen using the Haber-Bosch process. Ammonia can then be used as a clean and efficient fuel for various applications, such as transportation and power generation. Fluctuations in the hydrogen feed flow rate, resulting from variations in renewable energy sources can significantly impact the pressure and operating temperature within the system.}Morocco holds significant potential for renewable energy development due to its favorable geographic location and natural resources. The geographic location situated close to Europe makes Morocco well positioned for exporting green hydrogen to European markets. The chosen location for the ammonia plant is Boujdour in Morocco due to its excellent wind capacity factor of 67%.Modern ammonia production plants employ control systems to maintain stable pressure. When there is a reduction in hydrogen feed flow rate, these reductions result in severe pressure reductions which would lead to metal fatigue and damage the entire production unit. Hence, these control systems respond by adjusting parameters to sustain pressure within the system. Aspen Plus Dynamics has been used in the present thesis work to model the dynamics of the ammonia synthesis plant. The varying hydrogen feed flow rate is a consequence of renewable energy fluctuations, which is served as the basis for modeling three distinct scenarios involving a 20%, 50%, and 70% reduction in hydrogen feed flow rate. Three distinct control strategies were developed where each control strategy, based on controlling the cooling duty of the condenser, manipulating the brake power of the recycle compressor, and regulating the nitrogen feed flow rate, demonstrated effective stabilization of the system's pressure, even during dynamically changing input conditions. Both linear and step reduction in hydrogen feed flow rate have been considered to gain understanding of the dynamic the behaviour of the system.Significant outcomes were found when a reduction in hydrogen feed flow rate is imposed on all three control strategies. For a 20% reduction in hydrogen feed flow rate, the condenser's duty reduced from -1.2 MW to -1.05 MW, while the brake power of recycle compressor reduced from 12.5 kW to 5.5 kW. Furthermore, the stoichiometric ratio of H2:N2 changed from 3 to 2.8. These changes successfully stabilized the pressure in the ammonia synthesis plant under varying hydrogen input flow rate...Mechanical Engineering | Energy, Flow and Process Technolog

A visible light-controllable Rho kinase inhibitor based on a photochromic phenylazothiazole

Rho-associated coiled-coil-containing protein kinase (ROCK) is a serine-threonine kinase whose inhibitors are useful for the regulation of the actomyosin system. Here, we developed a photoswitchable ROCK inhibitor based on a phenylazothiazole scaffold. The reversible trans-cis isomerization by visible light stimuli enabled us to manipulate ROCK activities in vitro and in cells

A General Summarization Matrix for Scalable Machine Learning Model Computation in the R Language

Data analysis is an essential task for research. Modern large datasets indeed contain a high volume of data and may require a parallel DBMS, Hadoop Stack, or parallel clusters to analyze them. We propose an alternative approach to these methods by using a lightweight language/system like R to compute Machine Learning models on such datasets. This approach eliminates the need to use cluster/parallel systems in most cases, thus, it paves the way for an average user to effectively utilize its functionality. Specifically, we aim to eliminate the physical memory, time, and speed limitations, that are currently present within packages in R when working with a single machine. R is a powerful language, and it is very popular for its data analysis. However, R is significantly slow and does not allow flexible modifications, and the process of making it faster and more efficient is cumbersome. To address the drawbacks mentioned thus far, we implemented our approach in two phases. The first phase dealt with the construction of a summarization matrix, Γ, from a one-time scan of the source dataset, and it is implemented in C++ using the RCpp package. There are two forms of this Γ matrix, Diagonal and Non-Diagonal Gamma, each of which is efficient for computing specific models. The second phase used the constructed Γ Matrix to compute Machine Learning models like PCA, Linear Regression, Na¨ıve Bayes, K-means, and similar models for analysis, which is then implemented in R. We bundled our whole approach into a R package, titled Gamma.Computer Science, Department o