Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

Let T: D(T) → H2 be a densely defined closed operator with domain D(T) ⊂ H1. We say T to be absolutely minimum attaining if for every non-zero closed subspace M of H1 with D(T) ∩ M ≠ {0}, the restriction operator T|M: D(T) ∩ M → H2 attains its minimum modulus m(T|M). That is, there exists x ∈ D(T) ∩M with ‖x‖ = 1 and ‖T (x)‖ = inf{‖T (m)‖: m ∈ D(T) ∩ M: ‖m‖ = 1}. In this article, we prove several characterizations of this class of operators and show that every operator in this class has a nontrivial hyperinvariant subspace. One such important characterization is that an unbounded operator belongs to this class if and only if its null space is finite dimensional and its Moore-Penrose inverse is compact. We also prove a spectral theorem for unbounded normal operators of this class. It turns out that every such operator has a compact resolvent. © D l, Zagreb.

Department of Mathematics

World University root node

Operators and Matrices

SPECTRAL REPRESENTATION OF ABSOLUTELY MINIMUM ATTAINING UNBOUNDED NORMAL OPERATORS

Introduction: We examined the feasibility and acceptability of digital screening and brief intervention (d-SBI) for alcohol misuse in college students; the effectiveness of d-SBI was our secondary outcome. We also explored the barriers and facilitators of d-SBI. Methods: The study design is a mixed-methods, pilot and cluster randomized trial. Five colleges from a northern city in India were randomly allocated to d-SBI and control groups. One hundred and ninety-one students were screened and 25 (male = 23 and female = 2) participants (age 19.62 ± 2.58 years) fulfilled eligibility. All participants completed follow-up assessments at 3 months. In-depth interviews were done with 11 participants. Alcohol Use Disorder Identification Test (AUDIT) based screening brief intervention was provided on a web portal- or mobile application in the d-SBI group. The control group received digital screening and brief education. Direct questions and usage statistics assessed the measurement acceptability of the intervention. We compared the change in AUDIT scores in the intervention groups over 3 months post-intervention. Thematic analyses of transcripts of interviews were done by inductive coding. Results: Most participants reported that d-SBI was user-friendly (80%), advice was appropriate (80%) and perceived it to be useful (72%). Ninety-six percent of users, who logged in, completed screening. There was a significant decrease in AUDIT scores both in d-SBI (p <.001) and control groups (p <.001). Time and group significantly affected the mean AUDIT score, but time × group interaction was non-significant. Thematic analysis revealed six overarching themes. Conclusions: Digital SBI for alcohol misuse is acceptable, feasible and possibly effective among college students from low-resource settings. © 2023 John Wiley & Sons Australia, Ltd.

Department of Data Science

Asia-Pacific Psychiatry

Digital screening and brief intervention for alcohol misuse in college students: A pilot, mixed-methods, cluster randomized controlled trial from a low-resourced setting

Background and Objective – In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model's certainty, identifying cases that require attention and establishing trust in its predictions. Consequently, the significance of a well-calibrated model becomes paramount in the medical imaging domain, where accurate and reliable predictions are of utmost importance. While there has been a significant effort towards training modern deep neural networks to achieve high accuracy on medical imaging tasks, model calibration and factors that affect it remain under-explored. Methods – To address this, we conducted a comprehensive empirical study that explores model performance and calibration under different training regimes. We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes. Multiple calibration metrics were employed to gain a holistic understanding of model calibration. Results – Our study reveals that factors such as weight distributions and the similarity of learned representations correlate with the calibration trends observed in the models. Notably, models trained using rotation-based self-supervised pretrained regime exhibit significantly better calibration while achieving comparable or even superior performance compared to fully supervised models across different medical imaging datasets. Conclusion – These findings shed light on the importance of model calibration in medical image analysis and highlight the benefits of incorporating self-supervised learning approach to improve both performance and calibration. © 2023 Elsevier B.V.

Computer Methods and Programs in Biomedicine

Understanding calibration of deep neural networks for medical image classification

The state-of-the-art recourse generation methods solely rely on the user's profile (feature vector). However, two users having the same profile may still have different preferences. Consequently, the recourse generated from a single profile may not have the same appeal to both the users. For example, one rejected loan applicant may prefer changes in Savings Amount, whereas, another - being a financial expert - may prefer changes in Investment Amount. Taking into account these preferences in feature-change can be very helpful in generating more user-satisfying recourses. To this end, we propose a simple user-preference representation and design a method to generate a recourse that adheres to the user preference. We empirically demonstrate the effectiveness and ease of the proposed method at generating recourses satisfying user preferences. © 2023 Owner/Author.

ACM International Conference Proceeding Series

Algorithmic Recourse based on User's Feature-order Preference

Meta-learning (ML) utilizes extracted meta-knowledge from data to enable models to perform well on unseen data that they have not encountered before. Typically, this meta-knowledge is acquired from randomly sampled task batches and a critical assumption in the meta-learning is that all tasks in a batch equally contribute to the meta-knowledge. However, this assumption may not always hold true. In this study, we explore the impact of weighting tasks in a batch based on their contribution to meta-knowledge. We achieve this by introducing a learnable “task attention module” that can be integrated into any episodic training pipeline. We demonstrate that our approach improves the quality of the meta-knowledge obtained on standard meta-learning benchmarks such as miniImagenet, FC100 and tieredImagenet, as well as on noisy and cross-domain few-shot benchmarks. Additionally, we conduct a comprehensive analysis of the proposed task attention module to gain insights into its operation. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

SN Computer Science

Leveraging Task Variability in Meta-learning

Developing low-cost and reliable sensor systems for the detection of trace amounts of toxic gases is an important area of research. Ammonia (NH3) is a commonly produced industrial chemical and a harmful colorless pungent gas released from various manufacturing and processing industries. Continuous exposure to NH3 vapor causes serious menace to human health, microorganisms, and the ecosystem. Exposure to relatively higher concentrations of NH3 severely affects the respiratory system and leads to kidney failure, nasal erosion ulcers, and gastrointestinal diseases. Excessive accumulation of NH3 in the biosphere can cause various metabolic disruptions. As a consequence of this, therefore, suitable sensing methods for selective detection and quantification of trace amounts of NH3 are of utmost need to protect the environment and living systems. Given this, there have been significant research advances in the preceding years on the development of fluorescence chemosensors for efficient sensing and monitoring of the trace concentration of NH3 both in solution and vapor phases. This review article highlights several fluorescence chemosensors reported until recently for sensing and quantifying NH3 in the vapor phase or ammonium ions (NH4+) in the solution phase. The wide variety of fluorescence chemosensors discussed in this article are systematically gathered according to their structures, functional properties, and fluorescence sensing properties. Finally, the usefulness and existing challenges of using the fluorescence-based sensing method for NH3 detection and the future perspective on this research area have also been highlighted. © 2023 The Royal Society of Chemistry.

Department of Chemistry

Chemical Communications

Recent advances in fluorescence chemosensors for ammonia sensing in the solution and vapor phases

Although tocilizumab treatment in severe and critical coronavirus disease 2019 (COVID-19) patients has proven its efficacy at the clinical level, there is little evidence supporting the effect of short-term use of interleukin-6 receptor blocking therapy on the B cell sub-populations and the cross-neutralization of SARS-CoV-2 variants in convalescent COVID-19 patients. We performed immunological profiling of 69 tocilizumab-treated and non-treated convalescent COVID-19 patients in total. We observed that SARS-CoV-2-specific IgG1 titers depended on disease severity but not on tocilizumab treatment. The plasma of both treated and non-treated patients infected with the ancestral variant exhibit strong neutralizing activity against the ancestral virus and the Alpha, Beta, and Delta variants of SARS-CoV-2, whereas the Gamma and Omicron viruses were less sensitive to seroneutralization. Overall, we observed that, despite the clinical benefits of short-term tocilizumab therapy in modifying the cytokine storm associated with COVID-19 infections, there were no modifications in the robustness of B cell and IgG responses to Spike antigens.

Biological Sciences and Engineering

Fulltext

iScience

Tocilizumab-treated convalescent COVID-19 patients retain the cross-neutralization potential against SARS-CoV-2 variants

The detection of hydrogen peroxide (H2O2) holds significance in the food industry. In this work, carbon cloth (CC) was modified with a novel composite of Nb2CTx MXene and Prussian blue (PB) for the electrochemical detection of H2O2. Nb2CTx was drop casted on CC followed by the electrochemical deposition of PB using chronoamperometry at 0.7 V. It was found that uniform coverage of PB occurred on the underlying Nb2CTx surface. The electrochemical deposition time was optimized by varying the deposition times (240 s, 360 s, 480 s and 600 s) and the material obtained after deposition for 480 s was found to produce better sensing characteristics towards the detection of H2O2. The Nb2CTx /PB480 modified CC was able to detect H2O2 linearly from 1 µM to 10 µM and from 10 µM to 100 µM with a limit of detection of 200 nM. The sensor was also able to showcase good selectivity to H2O2 even in the presence of interfering molecules like dopamine, ascorbic acid, uric acid and sodium chloride. In addition, the material exhibited good storage stability, which was observed by the retention of 97 % of activity even after a storage time of 1 week. This material can be applied towards the sensing of H2O2 in milk since pH of the buffer used in the work matches the pH of the milk sample that was used. © 2022 Elsevier B.V.

Microchemical Journal

Electrochemical deposition of Prussian blue on Nb2CTx MXene modified carbon cloth for the non-enzymatic electrochemical detection of hydrogen peroxide

The demand for noninvasive wearable sensors for on-site monitoring has surged, particularly in the realm of personalized healthcare. This study introduces a pioneering approach employing a MXene-integrated single-stranded carbon yarn-based sensor patch utilizing a minimalist three-thread electrode setup for real-time monitoring of dopamine levels from artificial human sweat. The ability to immediately sew, weave, or stitch sensor platforms onto any type of clothing makes wearable thread-based sensor patches extremely promising and it can be positioned in close proximity to the skin. Flexible conductive carbon yarn (CCY) was modified by Ti3C2Tx MXene by a facile dip-coating method which was used as sensing electrodes. Leveraging MXene’s high surface area and exceptional conductivity, the sensor’s architecture achieved heightened sensitivity and selectivity in dopamine detection and reproducibility in a broad linear range of 1 nM-1 μM with a detection limit of 316 pM, along with real sample tests showing the promise of this platform for real-time physiological monitoring of human performance/fitness under stress as well as diagnostic monitoring through sweat analysis. The three-thread electrode configuration, strategically embedded within the patch, ensures ease of use and comfortable, noninvasive on-body application, offering a promising avenue for continuous, real-time monitoring of dopamine levels in individuals with neurological disorders or relevant medical conditions. This study highlights the potential of MXene-integrated wearable sensor patches with a minimalist electrode setup, advancing the field of wearable sensing technologies for personalized healthcare and point-of-care diagnostics. © 2024 American Chemical Society.

ACS Applied Electronic Materials

MXene-Integrated Single-Stranded Carbon Yarn-Based Wearable Sensor Patch for On-Site Monitoring of Dopamine

The increase in pharmaceutical contamination has become a pressing environmental issue that poses a threat to public health on a global scale and has a far-reaching impact on the existence of every corner of the world. Its extensive consumption and improper disposal lead to various adverse effects on humans, animals and the environment. Herein, we present a novel approach for the electrochemical sensing of dimetridazole (DMZ) using a graphitic carbon nitride-rhenium disulfide (CN-ReS2) composite. Dimetridazole is an antimicrobial medication used in animal feed and used as a drug in human and veterinarian medicine to treat protozoal infections. Considering its negative impacts, it is highly essential to detect the presence of DMZ in our bodies and the environment. It is noteworthy that this is the first time, CN-ReS2 is being reported for electrochemical sensing applications. The CN-ReS2 composite was synthesized using a one-step hydrothermal method and the material characterization revealed that ReS2 forms flower-like structures on the flaky sheets of CN. The CN-ReS2 composite demonstrated enhanced electrochemical activity towards the reduction of dimetridazole in addition to its high operational and storage stability. The electrocatalytic activity of the composite was optimized by the precise tuning of the CN and ReS2 weight ratios and it was found that 3% ReS2 (CN-ReS2-3) shows an enhanced electrochemical response. The developed sensor displayed a promising limit of detection of 19.8 nM in a wide linear range of 50-1000 nM and with a good sensitivity of 0.246 μA nM−1 cm−2. The developed sensor also showed rapid detection (within 5 s), excellent selectivity, storage stability, repeatability, reproducibility and flexibility in addition to real sample applications. A prototype of the sensor was also made and the response was evaluated with a conventional three-electrode sensor. The results of this study suggests that CN-ReS2 has great potential for use as an immobilization platform for highly sensitive and selective electrochemical sensing applications. © 2023 RSC.

Materials Advances

Unveiling the capability of graphitic carbon nitride-rhenium disulfide nanocomposite as an electrochemical sensing platform for the detection of dimetridazole from human serum samples