SRM Dental College

Celiac disease (CD) is highly prevalent in the wheat eating population of India. While HLA-DQ genotype is the strongest genetic determinant of CD, other genetic factors are likely contribute to genesis of CD. In this case-control study, we determined the frequencies of single nucleotide polymorphisms (SNP) in non-HLA candidate genes that influence the development of CD. DNA obtained from peripheral blood of 376 patients with CD and 736 controls was used for the studies. Fifty-one candidate gene polymorphisms were identified for evaluation. Competitive allele-specific polymerase chain reactions were designed to genotype biallelic SNPs at the selected loci. The genotype and allelotype was determined using KASP genotyping technology in a real-time PCR instrument. Eighteen of the 51 SNPs tested associated strongly with CD. The strongest association was found with the HLA DQ2.5 haplotype. HLA DQ2.2 haplotype and HLA-DQ8 haplotype were also strongly associated with CD. Non-HLA genes that were significantly associated with CD included those associated with T cell receptor signaling and T cell activation - CD247, CD80, CD28, PRKCQ, TNFAIP3, UBASH3 and ARHGAP31; genes involved in inflammatory cell migration – CCR3 and CCR4; those involved in epithelial protection – GATD3, PARK7 and INAVA (C1orf106); and those influencing RNA – DDX6 and PUS10. We concluded that the non-HLA genes associated with CD in this Indian population were mostly those associated with molecules involved in the T cell receptor signaling pathway downstream to HLA-DQ2/8, which lead to immune and subsequent inflammatory activation.

This paper addresses the stabilization problem for a class of fractional-order memristive reaction–diffusion Cohen–Grossberg neural networks with time-varying delays under an intermittent boundary control framework. Two scenarios are considered: systems without parametric uncertainties, for which asymptotic stability is established, and systems with uncertainties, for which robust asymptotic stability is ensured. By constructing appropriate Lyapunov functionals and employing Wirtinger-type inequalities, the fractional Razumikhin approach, and key properties of the Mittag–Leffler function, sufficient stability conditions are derived in terms of linear matrix inequalities with reduced conservatism. Furthermore, the effects of time-varying delays and control activation intervals on the stabilization performance are systematically investigated. The effectiveness and advantages of the proposed control methodology are validated through numerical simulations.

In the present digital era, meetings have become an integral part of organizational communication, collaboration, and strategic decision-making processes. Organizations across corporate, educational, and governmental sectors conduct frequent meetings to discuss ideas, monitor progress, allocate responsibilities, and make critical decisions. However, documenting meetings manually is a time-consuming and error-prone process, often resulting in incomplete, inconsistent, or inaccurate records. Such limitations reduce the effectiveness of meetings and negatively impact productivity and accountability. Recent advancements in Artificial Intelligence (AI) have enabled the automation of complex tasks involving speech and language understanding. Technologies such as Speech Recognition, Natural Language Processing (NLP), and Machine Learning (ML) have made it possible to automatically transcribe spoken conversations and analyze large volumes of unstructured data. These technologies provide an opportunity to transform traditional meeting documentation practices into intelligent, automated processes. This paper presents an AI-based meeting summary and action items generation system designed to automatically convert meeting audio into structured textual summaries and clearly defined action items. The proposed system captures meeting audio, performs speech-to-text conversion, analyzes the transcript using NLP techniques, and generates concise summaries highlighting key discussion points and decisions. In addition, the system automatically extracts action items, identifies responsible individuals, and associates relevant deadlines, thereby improving accountability and follow-up. The proposed system adopts a modular and scalable architecture, allowing seamless integration with existing digital platforms and supporting both physical and virtual meetings. By reducing manual effort and minimizing human errors. The paper discusses the system architecture, working methodology, technologies employed, advantages, applications,

Background: Dental radiography significantly contributes to the generation of radiographic waste such as lead foils and silver-containing fixer solutions. Improper disposal of these materials may pose serious risks to human health and the environment. Dental students, as future practitioners, play a vital role in ensuring safe and responsible waste management practices. Aim: To assess the knowledge, attitude, and awareness of dental students regarding dental radiographic waste management. Materials and Methods: A cross-sectional questionnaire-based study was conducted among 200 dental students including undergraduate students, interns, and postgraduate students. A structured self-administered questionnaire was used to collect demographic information and assess knowledge, attitude, and awareness regarding radiographic waste management. Data were analyzed using descriptive statistics. The Shapiro–Wilk test was used to assess data normality, and the Mann–Whitney U test was used for group comparison. Results were expressed as frequencies, percentages, mean, and standard deviation. Results: The total knowledge scores ranged from 13 to 18 with a mean score of 16.32 ± 1.20, indicating an overall satisfactory level of knowledge among the participants. Most participants demonstrated good awareness regarding environmental and health hazards associated with radiographic waste (90.0%) and correct disposal of fixer solutions (89.0%). However, knowledge regarding proper disposal of lead foils was comparatively lower (44.5%), and only 25.0% of participants had received formal training in radiographic waste management. Conclusion: Dental students demonstrated satisfactory knowledge and positive attitudes toward radiographic waste management. However, procedural gaps and limited formal training highlight the need for strengthened educational interventions and institutional reinforcement to promote environmentally responsible dental practice.

Abstract The use of wireless charging technology for Electric Vehicles (EVs) is gaining increasing significance due to its ability to charge batteries safely and conveniently without the need for physical connectors. This paper presents the design and implementation of the transmitter-side power conversion system for a wireless EV charging station. The proposed system architecture integrates a front-end Power Factor Correction (PFC) rectifier, an Interleaved Resonant DC-DC converter, and a three-level Neutral Point Clamped (NPC) inverter, providing a highly efficient and stable power transmission path for battery charging. The Interleaved Resonant DC-DC converter, designed with a Zero Voltage Switching (ZVS) mechanism, minimizes switching losses and enhances overall efficiency. Its operation under Constant Current (CC) and Constant Voltage (CV) control modes ensures smooth and reliable mode transition through an auxiliary controller incorporated alongside a conventional PI controller. The inverter stage adopts a Modified Space Vector Pulse Width Modulation (MSVPWM) strategy that significantly reduces Total Harmonic Distortion (THD) in both voltage and current waveforms, thereby maintaining compliance with power quality standards. The complete system's performance has been evaluated through both simulation and experimental validation. MATLAB/Simulink-based analysis demonstrates stable converter dynamics with a regulated output voltage of 325 V and negligible transient disturbances during mode transitions. A 0.2 kW laboratory prototype was developed to confirm the simulation results using PIC24F16KA102 and FPGA-SPARTAN-7 controllers for power and switching signal generation, respectively. Experimental measurements indicate that the proposed Interleaved Resonant DC-DC converter achieved an efficiency of approximately 95.35% at rated load, which aligns closely with theoretical predictions. The three-level NPC inverter produced a 320 V output with voltage and current THD levels of 4.8% and 5.6%, respectively. A 2:1 air-core transformer designed for high-frequency operation (100-200 kHz) effectively transfers power to the EV receiver side. The influence of coupling coefficient and air-gap variation on transfer efficiency was systematically investigated, revealing an optimal separation distance of 10-20 mm. The overall results confirm the efficacy of the proposed design for high-efficiency wireless EV charging. Future work will concentrate on optimizing the receiver-side converters to further enhance system efficiency and dynamic performance.