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Over the last few years, social media has evolved into a medium for expressing personal views, emotions, and even business and political proposals, recommendations, and advertisements. We address the topic of identifying emotions from text data obtained from social media posts like Twitter in this research. We have deployed different traditional machine learning techniques such as Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forest, as well as deep neural network models such as LSTM, CNN, GRU, BiLSTM, BiGRU to classify these tweets into four emotion categories (Fear, Anger, Joy, and Sadness). Furthermore, we have constructed a BiLSTM and BiGRU ensemble model. The evaluation result shows that the deep neural network models(BiGRU, to be specific) produce the most promising results compared to traditional machine learning models, with an 87.53 % accuracy rate. The ensemble model performs even better (87.66 %), albeit the difference is not significant. This result will aid in the development of a decision-making tool that visualizes emotional fluctuations., Comment: Course Project

In recent days, the number of technology enthusiasts is increasing day by day with the prevalence of technological products and easy access to the internet. Similarly, the amount of people working behind this rapid development is rising tremendously. Computer programmers consist of a large portion of those tech-savvy people. Codeforces, an online programming and contest hosting platform used by many competitive programmers worldwide. It is regarded as one of the most standardized platforms for practicing programming problems and participate in programming contests. In this research, we propose a framework that predicts the performance of any particular contestant in the upcoming competitions as well as predicts the rating after that contest based on their practice and the performance of their previous contests., Comment: Camera Ready Version

This thesis explores the use of eco-friendly plasticizers to improve the fracture resistance of elastomers. The study investigates the behavior of modified bio-oils and their compatibility with elastomers. Tests were conducted to evaluate crack resistance, mechanical properties, and filler network. The findings indicate that elastomers with bio-oils lead to changes in the filler network and a reduction in modulus. The analysis of fracture mechanics suggests a limited contribution of the filler network. Structure-property correlations were established considering different parameters. The crack resistance of elastomers with different bio-oils before and after aging was also analyzed. The study emphasizes the impact of oil modification on fracture behavior. Appropriate modifications to fatty oils were found to enhance crack resistance. The research contributes to the development of eco-friendly and high-performance elastomers, providing insights for diverse industrial applications., Diese Arbeit untersucht den Einsatz umweltfreundlicher Weichmacher zur Verbesserung der Bruchfestigkeit von Elastomeren. Die Studie untersucht das Verhalten modifizierter Bioöle und deren Kompatibilität mit Elastomeren. Tests wurden durchgeführt, um die Rissbeständigkeit, mechanische Eigenschaften und Füllstoffnetzwerke zu bewerten. Die Ergebnisse zeigen, dass Elastomere mit Bioölen zu Veränderungen im Füllstoffnetzwerk und einer Reduktion des Moduls führen. Die Analyse der Bruchmechanik legt eine begrenzte Beitrag des Füllstoffnetzwerks nahe. Struktur-Eigenschafts-Korrelationen wurden unter Berücksichtigung verschiedener Parameter hergestellt. Die Rissbeständigkeit von Elastomeren mit verschiedenen Bioölen vor und nach der Alterung wurde ebenfalls analysiert. Die Studie betont die Auswirkungen der Ölmodifikation auf das Bruchverhalten. Geeignete Modifikationen von fetten Ölen wurden gefunden, um die Rissbeständigkeit zu verbessern. Die Forschung trägt zur Entwicklung umweltfreundlicher und leistungsfähiger Elastomere bei und bietet Einblicke für verschiedene industrielle Anwendungen.

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Background The aim of this study was to investigate the anti-obesity effects of DWP16001, a sodium-glucose cotransporter-2 (SGLT2 inhibitor), in naturally obese dogs. A total of 20 dogs were divided into four equal groups: one obese control (OC group), and three treated groups; DWP0.2 group, DWP0.5 group, and DWP1 group. OC group fed with food for maintenance and treated groups were fed with food for maintenance with 0.2 mg/kg DWP16001, 0.5 mg/kg DWP16001 and 1 mg/kg DWP16001, respectively. The food for maintenance was provided to dogs as 2 RER (Resting energy requirement) in kcal and DWP16001-supplemented food was administered once a day for 8 weeks. Results Body condition score, body weight, and fat thickness were significantly reduced (P th week the food consumption rate was 101.35 ± 2.56, 166.59 ± 4.72, 98.47 ± 1.44 and 123.15 ± 2.45% compared with initial food consumption rate. Body fat percentage, chest and waist circumference, blood glucose, and insulin were reduced compared to OC group but not significantly different from those of the OC group during experimental period. Serum alanine aminotransferase, alkaline phosphatase, creatine phosphokinase, and creatinine were significantly reduced in DWP0.2 group on 8 weeks. Serum cholesterol and triglycerides were reduced but not significantly. No specific adverse effects were observed throughout the experiment, and hematological parameters were unchanged. The results indicate that DWP16001 was not harmful to the dogs in our study and might have anti-obesity effects in naturally obese dogs. Conclusions The above results and discussion suggest that DWP16001 is safe and might have anti-obesity effects in naturally obese dogs.

Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas, namely model capabilities, training data, and model interpretation. We experimentally demonstrated the variability between different runs of a model and the low agreement among different models' outputs. We investigated models trained for specific types of vulnerabilities compared to a model that is trained on all the vulnerabilities at once. We explored the types of programs DL may consider "hard" to handle. We investigated the relations of training data sizes and training data composition with model performance. Finally, we studied model interpretations and analyzed important features that the models used to make predictions. We believe that our findings can help better understand model results, provide guidance on preparing training data, and improve the robustness of the models. All of our datasets, code, and results are available at https://doi.org/10.6084/m9.figshare.20791240., Comment: 12 pages, 14 figures. Accepted at ICSE 2023. Camera-ready version

The amide bond is one of the most important functional group in chemistry, biology and materials science. At the same time, NHCs (NHC = N-heterocyclic carbene) are among the most important ligands that are extensively utilized in organometallic chemistry, catalysis and organic synthesis, where their unique electronic and steric properties mimic the characteristics of tertiary phosphines. One of the main challenges in activating amide bonds is the high stability of amides resulting from nN�����*C=O (15���20 kcal/mol) resonance, which renders the amide N���C(O) bond very difficult to cleave. With respect to NHC ligands, ItBu (ItBu = 1,3-di-tert-butylimidazol-2-ylidene) represents the most important and versatile N-alkyl N-heterocyclic carbene available in organic synthesis and catalysis. This dissertation will (1) address the challenges in activating amide bonds enabled by the development of efficient ground-state-destabilization methods to facilitate N���C(O) bond activation for acyl addition and cross-coupling reactions via acyl-metal or tetrahedral intermediates; (2) demonstrate the synthesis, characterization and catalytic studies of more sterically-hindered analogues of ItBu, which represent the most bulky N-alkylN-heterocyclic carbenes prepared to date. The first part of this thesis will detail our studies on the effect of activating group on the structures, electronic properties and reactivity of activated amides. We developed a family of N-acyl-glutarimide amides that achieve full twist of the amide bond (�� = 89.1��) and show high reactivity in cross-coupling reactions. Furthermore, we developed N-acyl-��-valerolactams as a highly effective class of mono-N-acyl-activated amide precursors in cross-coupling that are characterized by a substantial distortion of the amide bond (��(��+��N) = 54.0��). This dissertation will also discuss the development of highly chemoselective transition-metal-free methods for transamidation, thioesterification, selenoesterification and synthesis of sulfoxonium ylides from amides by N���C(O) cleavage via tetrahedral intermediates. These exceedingly mild procedures show broad substrate scope and tolerate a large number of useful functional groups. Moreover, application to the novel synthesis of Tigan, an antiemetic drug, and late-stage derivatization of pharmaceuticals will be described. The second part of this thesis will detail the synthesis, structural characterization and catalytic studies of three classes of NHC ligands, namely ItOct, SItOct, ItOct-bimy, ItHept and SItHept. These N-alkyl NHC ligands are higher homologues of ItBu, while their unique steric and electronic properties make them potentially very attractive in various applications in chemical science, including organometallic chemistry, organic synthesis and catalysis. In this part, we will demonstrate that replacement of the t-Bu side chain with t-Oct or t-Hept side chains results in a significantly increased steric volume, while retaining the electronic properties inherent to N-alkyl NHC ligands, such as strong ��-donation and ��-acceptance. Simple and efficient large-scale syntheses of the imidazolium (SItOct, SItHept), imidazolinium (SItOct, SItHept) and benzimidazolium (ItOct-bimy) precursors will be described. Beneficial effects in using the sterically-demanding N-alkyl NHC ligands in catalysis using their complexes with Au, Cu, Ag and Pd will be presented. Considering the tremendous importance of ItBu, we anticipate that this new class of ItOct, ItHept and ItOct-bimy ligands will find wide application in organic synthesis.

Human affects are complex paradox and an active research domain in affective computing. Affects are traditionally determined through a self-report based psychometric questionnaire or through facial expression recognition. However, few state-of-the-arts pieces of research have shown the possibilities of recognizing human affects from psychophysiological and neurological signals. In this article, electroencephalogram (EEG) signals are used to recognize human affects. The electroencephalogram (EEG) of 100 participants are collected where they are given to watch one-minute video stimuli to induce different affective states. The videos with emotional tags have a variety range of affects including happy, sad, disgust, and peaceful. The experimental stimuli are collected and analyzed intensively. The interrelationship between the EEG signal frequencies and the ratings given by the participants are taken into consideration for classifying affective states. Advanced feature extraction techniques are applied along with the statistical features to prepare a fused feature vector of affective state recognition. Factor analysis methods are also applied to select discriminative features. Finally, several popular supervised machine learning classifier is applied to recognize different affective states from the discriminative feature vector. Based on the experiment, the designed random forest classifier produces 89.06% accuracy in classifying four basic affective states., Comment: 18 pages, 7 figures

Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.

Crown rot disease (Fusarium pseudograminearum) of wheat causes significant yield losses across Australia. However, the genetic control of crown rot resistance and tolerance is complex and traditional breeding strategies have proven ineffective. Marker Assisted Recurrent Selection (MARS) was examined as a strategy to more effectively pyramid the many minor genes controlling plant response to crown rot. Populations were developed from three-way crosses to (i) optimize screening procedures for crown rot, (ii) examine the relationship between resistance and tolerance, (iii) identify quantitative trait loci (QTL) linked to resistance and tolerance, (iv) pyramid QTLs using MARS and (v) confirm response to selection. Disease severity in greenhouse pot tests was less reliable than field screening using managed inoculum; hence field screening was used for all subsequent evaluations. In the MARS approach, materials from each population were genotyped and phenotyped by F4 for crown rot response and QTLs linked to crown rot resistance and/or tolerance identified. Consistent QTL, mapped to unique chromosomal regions, were then recombined in crossing to pyramid markers. Following two cycles of recombination, the progeny from both cycles of MARS, the base population and the original parents were compared for resistance and/or tolerance depending on the population. Resistance and tolerance (determined as reduced yield loss in the presence of crown rot) were independently inherited. A significant and positive response to selection was observed with some recombinants significantly more resistant and higher yielding than their parents. The genotypes identified and developed in this study combine multiple sources of resistance and tolerance and these materials and their associated markers can be used by the Australian wheat industry to improve wheat yield in the presence of crown rot.

As GPS cannot reliably reach and cover any covered area or indoor location, many past studies and research considered the application of Signals of Opportunity (SoOp) such as Wi-Fi, Bluetooth, RFID, Cellular Networks, and FM signals for indoor positioning and object tracking. However, not all SoOp can be part of the indoor positioning experiment due to some limitations; for example, not all signals are cost-effective; some are less accurate in finding position; some require additional hardware for deployment. In this study, a new technique of indoor navigation is introduced exploiting AM radio signals and the received signal strength (RSS) fingerprinting method. This research investigates the fingerprinting technique with the deterministic approach via three algorithms such as Nearest Neighbour (NN), K-Nearest Neighbour (KNN), and K- Weighted Nearest Neighbour (KWNN) for measuring the mean distance error (MDE) as the prime indicator of localization accuracy. The result demonstrates that the Nearest Neighbour algorithm performed best for K=1 and the lowest mean distance error in the ground and the first floor is less than 3m.

In communication-denied or contested environments, Line-of-Sight (LoS) communication (e.g free space optical communication using infrared or visible light) becomes one of the most reliable and efficient ways to send information between geographically scattered mobile units. In this paper, we consider the problem of planning optimal locations and trajectories for a group of autonomous vehicles to see a set of units that are dispersed in an environment with obstacles. The contributions of the paper are the following: 1) We propose centralized and distributed algorithms to verify that the vehicles and units form a connected network through LoS; 2) We present an algorithm that can maintain visibility-based connectivity, if possible, by relocating a single vehicle; and 3) We study the computational, Comment: Submitted in Robotics and Autonomous Systems

Ketogenic diet is protective in models of ischemic stroke and neurodegenerative diseases. Currently, clinical trials are testing the efficacy of this diet in neurodegenerative diseases but its mode of action is still unclear. The ketone body β-hydroxybutyrate (BHB) is the endogenous agonist of the hydroxy-carboxylic acid receptor 2 (HCA2, GPR109A) which is expressed in various immune cells; therefore, we tested the potential involvement of this receptor in a mouse model of ischemic stroke. The protective effect of ketogenic diet and BHB was lost in Hca2-/- mice, demonstrating that HCA2 receptors are responsible for neuroprotection. Similarly, nicotinic acid, a HCA2 agonist, reduced the infarct size via a HCA2-mediated mechanism. Immunohistochemical analysis of immune cells in Hca2mRFP transgenic mice revealed HCA2 expression in monocytes/macrophages. Bone marrow transplantation demonstrated that HCA2 on monocytes/macrophages is required for the protective effect. Activation of HCA2 receptors induced a neuroprotective phenotype of monocytes/macrophages that depended on PGD2 production by COX-1 and the hematopoietic PGD2 synthase. Our data reveal that HCA2 activation by dietary or pharmacological means instructs monocytes/macrophages to carry a neuroprotective signal to the brain which can be used therapeutically.

The involvement of ethylene, (C₂H₄) in the formation and persistence of legume root nodules, and its interaction with carbon dioxide, (CO₂) have been studied in three different legume species, namely, pea (Pisum sativum L.), bean (Phaseolus vulgaris L.) and lentil (Lens culinaris Medik.). This work was undertaken because of the possible connection between tolerance to ethylene in the root zone and tolerance to waterlogging. Two different techniques for root aeration were employed: a 'constant flow - through' system and a 'closed -vessel' (recirculatory) system. In the constant flow -through system, legume roots were exposed to an air -stream containing 0, 0.11, 0.33, and 1 ppm of C₂H₄. In the closed -vessel system C₂H₄ and /or CO₂ produced by the roots were allowed to accumulate around them. Exogenous C₂H₄ at 1 ppm significantly inhibited root -nodule formation in pea and bean. Nodule fresh weight (individual and total), nitrogenase activity and leghaemoglobin content per plant were significantly reduced. Total N accumulation in the shoots and shoot dry weight per plant were also significantly lower than in the other treatments. However, root dry weight and primary root length were not significantly affected in either species. Significant positive correlations were observed between leghaemoglobin content and nodule fresh weight per plant, nitrogenase activity and leghaemoglobin, total N accumulation in the shoots and nitrogenase activity, and root dry weight and total nodule fresh weight. Shoot N concentration, on the other hand, was negatively correlated with shoot dry weight and total nodule fresh weight. No comparable effect of 1 ppm C₂H₄ on the lentil cultivar, which is known to be moderately waterlogging tolerant, was observed. Similar results were observed in the closed- vessel experiments, where C₂H₄ (produced by the plant roots) reached concentrations of 0.6 -0.8 ppm. Carbon dioxide (0.6 -1 %) in the root atmosphere of pea and bean plants caused an increase in individual and total nodule fresh weight and the nitrogenase activity of the nodules. However, increased CO₂ in the root atmosphere did not significantly increase total N accumulation and shoot dry weight in pea. Again, no significant effect was observed with lentil. No significant interactions between C₂H₄ and CO₂ were observed in either experimental system. Comparisons of a range of bean cultivars in sealed vessels showed large differences in the amounts of C₂H₄ produced by the root systems. Abnormal shoot growth and defoliation occurred when the endogenous C₂H₄ was allowed to accumulate, suggesting that it was at least partly responsible for the effects. The relationship between sensitivity to C₂H₄ of different species and cultivars, and tolerance to waterlogging is discussed.

The proliferation and transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or the (COVID-19) disease, has become a threat to worldwide biosecurity. Therefore, early diagnosis of COVID-19 is crucial to combat the ongoing infection spread. In this study we propose a flexible aptamer-based electrochemical sensor for the rapid, label-free detection of SARS-CoV-2 spike protein (SP). A platform made of a porous and flexible carbon cloth, coated with gold nanoparticles, to increase the conductivity and electrochemical performance of the material, was assembled with a thiol functionalized DNA aptamer via S-Au bonds, for the selective recognition of the SARS-CoV-2 SP. The various steps for the sensor preparation were followed by using scanning electron microscopy, cyclic voltammetry and differential pulse voltammetry (DPV). The proposed platform displayed good mechanical stability, revealing negligible changes on voltammetric responses to bending at various angles. Quantification of SARS-CoV-2 SP was performed by DPV and chronopotentiometry (CP), exploiting the changes of the electrical signals due the [Fe(CN)6]3-/4- redox probe, when SARS-CoV-2 SP binds to the aptamer immobilized on the electrode surface. Current density, in DPV, and square root of the transition time, in CP, varied linearly with the log[ SARS-CoV-2 SP], providing lower limits of detection (LOD) of 0.11 ng/mL and 37.8 ng/mL, respectively. The sensor displayed good selectivity, repeatability, and was tested in diluted human saliva, spiked with different SARS-CoV-2 SP concentrations, providing LODs of 0.167 ng/mL and 46.2 ng/mL for DPV and CP, respectively.