Emotion detection from handwriting and drawing samples using an attention-based transformer model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).Peer reviewe

Synthesis of substituted 1,3-oxathianes and 1,3-oxathiolanes

43-4

The Comparison of Outcome in Treating Proximal Ureteric Stones of Size 10 mm to 15 mm Using Extracorporeal Shock Wave Lithotripsy as Compared to Ureterorenoscopic Manipulation Using Holmium Laser

Urinary stone disease or nephrolithiasis, the third most common disease of the urinary tract, is a major health issue due to its high prevalence, occurrence, and recurrence. The hallmark of a stone that obstructs the ureter or renal pelvis is excruciating, intermittent pain that radiates from the flank to the groin or to the inner thigh. Stone size influences the rate of spontaneous stone passage. Our aim was to compare the efficacy & the frequency of stone-free patients after intervention at 1 week after extracorporeal shock wave lithotripsy (ESWL) and ureterorenoscopic (URS) manipulation for proximal ureteric stone (10–15 mm size). This randomized control trial was done in the department of Urology, KRL Hospital Islamabad from 18th Nov 2019 to 18th May 2020. After meeting the inclusion criteria, 100 patients were enrolled and were divided into two groups. The first group was treated with ESWL and the other with URS. Then, procedures were done. Follow-up was noted after 1 week in the stone clinic. The average age of the patients was 39.71 ± 10.17 years. Efficacy in the ESWL group was found in 68% cases while in the URS group, efficacy was noticed in 76% cases (P > 0.05). Male patients were three times at a higher risk of recurrence as compared to females. This study concluded that both ESWL and URS are equally effective statistically in terms of the frequency of stone-free patients at 1 week for proximal ureteric stone (10–15 mm size)

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

© 2024 Tech Science Press. All rights reserved. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to reduce the dimensionality and redundancy of features and select only the relevant ones; transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets, and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model. Four National Aeronautics and Space Administration (NASA) and four Promise datasets are used in the study, showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values when different classifiers were combined. It reveals that using an amalgam of techniques such as those used in this study, feature selection, transfer learning, and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing, useful end mode.Peer reviewe

Response of Wastewater-Based Epidemiology Predictor for the Second Wave of COVID-19 in Ahmedabad, India: A Long-term Data Perspective

Wastewater-based epidemiology (WBE) monitoring can play a key role in managing future pandemics because it covers both pre-symptomatic and asymptomatic cases, especially in densely populated areas with limited community health care. In the present work, wastewater monitoring was employed in Ahmedabad, India, after the successful containment of the first wave of COVID-19 to predict resurgence of the disease in the expected second wave of the pandemic. Here we show wastewater levels of COVID-19 virus particles (i.e., SARS-CoV-2) positively correlated with the number of confirmed clinical cases during the first wave, and provided early detection of COVID-19 presence before the second wave in Ahmedabad and an WBE-based city zonation plan was developed for health protection. A eight-month data of Surveillance of Wastewater for Early Epidemic Prediction (SWEEP) was gathered, including weekly SARS-CoV-2 RNA wastewater analysis (n=287) from nine locations between September 2020 and April 2021. Across this period, 258 out of 287 samples were positive for least two out of three SARS-CoV-2 genes (N, ORF 1ab, and S). Monitoring showed a substantial decline in all three gene markers between October and September 2020, followed by an abrupt increase in November 2020. Similar changes were seen in March 2021, which preceded the second COVID-19 wave. Measured wastewater ORF-1ab gene copies ranged from 6.1 × 102 (October, 2020) to 1.4 × 104 (November, 2020) copies/mL, and wastewater gene levels typically lead confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identifying local disease hotspots within a city and guiding rapid management interventions.This work is funded by UNICEF, Gujarat. We acknowledge the help received from GPCB and AMCN

Enhanced combined assimilative and bound phosphorus uptake in concurrence with nitrate removal in pre-anoxic cyclic sequencing batch reactor

Needless to specify, controlling nitrogen and phosphorus discharge from wastewater treatment plants is synonymous with the prevention of eutrophication of surface waters, as one of the major issues related to water security. The present study investigates the performance of a pre-anoxic sequencing batch reactor (SBR) working on the basis of intermittent aeration, operated at varied carbon (bCOD) to nitrogen (C/N) ratio of 3, 7.5, and 10, and readily biodegradable (rbCOD) to slowly biodegradable (sbCOD) ratio of 0.1, 0.25, and 0.5. The findings revealed that an enhanced nitrogen removal was observed, together with higher C/N and rbCOD to sbCOD ratios. The results also show a consistent increase in total phosphorus removal with an increase in nitrogen removal. The phosphorus uptake of sludge varied from 0.02 – 0.045 mgP/mgVSS (avg. 0.031 ± 0.004), which resulted in enrichment levels of 0.88 – 1.68 times the stoichiometric value of 0.0267 mgP/mgVSS (avg. 1.45 ± 0.14). On an average basis, the assimilative total phosphate (TP) content was increased by 0.008 gTP/gNO -/3 -N removal rate. The excess phosphorus removal was due to the formation of poorly soluble polyvalent phosphate compounds, which was found based on dry analysis, which persisted as bound phosphate in the sludge

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Comparison of Caudal Block and Nalbuphine for Pain Management in Children

Background: To compare pain scores in patients undergoing inguinal herniotomy after caudal block and intravenous nalbuphine.Methods: In this randomized controlled trial a total of 100 patients of age 4-12 years undergoing inguinal herniotomy were included. They were divided into two groups (50 patients in each group); Group A included Caudal block and Group B included patients who received Nalbuphine. Patients in group A were given caudal block while in group B were given nalbuphine 0.1-0.3mg/kg. Pain scores were calculated at 0,1,2,4 and 8 hours. Requirement for rescue analgesia was calculated. All the data were analyzed by SPSS version 16.Results: The mean age of patients were found as 4.15 ± 3.32 years in group A and 4.88 ± 3.18 years in group B. All the other demographics were comparable in both groups. The mean pain scores were less in nalbuphine group at 0,1,2 and 4 hours, however it was significant at 0 and 1 hour. The requirement of rescue analgesia was less in group B than group A (14% vs 34%, p <0.05). The only side effect was observed was vomiting in 12% of patients in group B while in none of patient in group A.Conclusion: Nalbuphine is better than caudal block for pain management in children undergoing inguinal herniotomy