Dr D Y Patil Dental College & Hospital

Objective To assess the diagnostic accuracy and reliability of orthopantomogram (OPG) compared to cone beam computed tomography (CBCT) in evaluating the mandibular buccal shelf anatomy, with specific emphasis on the proximity to the inferior alveolar nerve canal (IANC), across various skeletal malocclusions and vertical growth patterns. Methods A cross-sectional study with a retrospective design was conducted using OPG and CBCT images of 90 patients obtained prior to treatment, stratified based on skeletal malocclusion into Class I, II, and III groups ( n = 30 each), and further subdivided based on facial divergence (hypodivergent, normodivergent, hyperdivergent). Linear measurements extending from the cementoenamel junction (CEJ) to the superior margin of the IANC were obtained at eight predefined anatomical landmarks. Independent t -test and one-way analysis of variance were used for data analysis, considering P < .05 as statistically significant. Results OPG and CBCT measurements showed no statistically significant differences across all malocclusion and growth patterns ( P > .05). A consistent decrease in CEJ to IANC distance from the mesial CEJ of the first molar to the distal CEJ of the second molar was observed, most pronounced in hyperdivergent individuals. Class III hypodivergent subjects exhibited the greatest bone height (21.19 ± 1.28 mm), whereas Class II hyperdivergent individuals showed the lowest bone height (15.68 ± 1.72 mm), indicating a closer proximity of the IANC. Conclusion OPG is a reliable alternative to CBCT for buccal shelf assessment, offering reduced radiation exposure and cost. Anatomical variations across skeletal and vertical growth patterns significantly impact screw placement safety, emphasizing the need for individualized site selection based on malocclusion and divergence patterns.

Oral lichenoid lesions (OLLs) can mimic oral lichen planus but are often linked to contact with dental amalgam. Replacement with biocompatible materials has been associated with lesion resolution, yet prior evidence lacked quantitative synthesis. Following PRISMA 2020 (PROSPERO: CRDXXXXXXXXXX2), eight databases (1986-2024) were searched for adult in vivo studies reporting OLL resolution after amalgam replacement. Two reviewers independently screened, extracted data, and assessed bias (ROBINS-I, Newcastle-Ottawa Scale, JBI). Random-effects meta-analysis estimated pooled odds ratios (OR) for clinical resolution; certainty was graded with GRADE. Of 2368 records, 35 studies were qualitatively synthesized and 11 entered meta-analysis (N=365). Amalgam was replaced with glass ionomer, ceramics, composite resin, or gold alloys. Replacement markedly increased odds of lesion resolution (OR=122.18; 95% CI: 55.64-268.26; p<0.001) with moderate heterogeneity (I2=42%). Risk of bias across observational studies was generally low to moderate. Funnel-plot asymmetry and Egger's test (p<0.001) suggested publication bias; overall certainty was moderate. Subgroup analyses indicated greater benefit when lesions had direct amalgam contact, when patch testing was positive, and when ceramics or gold were used. Sensitivity analyses supported strength of the primary findings. Amalgam replacement was linked with substantially higher odds of OLL resolution. However, between-study heterogeneity and indications of publication bias temper confidence in the pooled effect. Confirmatory, prospective, registered studies with standardized diagnostic criteria, outcome definitions, and material categories are needed to refine effect size estimates and strengthen certainty.

Translating spoken words into emotionally and contextually aligned video content remains an open challenge in generative AI. Subtle vocal patterns—such as pauses and pitch modulations—often obscure emotional cues, resulting in visuals that feel emotionally disconnected or flat. While several models excel at text-to-image generation, they struggle with interpreting speech-based inputs, often misreading paralinguistic cues and contextual intent. To address these limitations, this research introduces EchoVid, an audio-to-video synthesis model designed to prioritize contextual fidelity and emotional alignment. A scalable web interface built with React.js and TypeScript connects to Node.js backend with the MongoDB Atlas for near-real-time generation (≈ 1.2× input-duration latency at 512² frames on RTX A4000) interaction. Using PyAudio input, EchoVid guides Hugging Face’s Stable Diffusion v2.1 via emotion-aware prompts, with CNN-enhanced diffusion transformers supporting the video generation process. Preliminary results show that EchoVid can generate visuals that reflect both emotional tone (e.g., joyful imagery for upbeat speech) and context. The proposed EchoVid model is compared with MoCoGAN and Stable Video Diffusion variants based on metrics like, FVD, FID-VID and CLIPScore. Further this research introduces two novel evaluation metrics namely Temporal Semantic Stability (TSS) and Perceptual Flicker Index (PFI) that scores the semantic consistency and frame-to-frame change in the generated video. The results show that EchoVid outperforms the other models and can generate relatively better videos.

This paper analyses how small and medium-sized enterprises (SME) in the emerging economies are adopting Industry 4.0 with specific reference to cloud-to-edge transformation and its potential impact on productivity and employment. Although the use of new digital technologies has become common among large ventures, SMEs are usually constrained due to cost, capacity, infrastructures, and data management. The article builds a conceptual cloud-to-edge Industry 4.0 system that combines IoT-enabled systems, edge and fog nodes, and scalable cloud servers to aid in real-time analytics, automation, and data-driven decision-making under resource-limited conditions. The survey evidence, secondary datasets, and example case studies of manufacturing, service, and agro-industrial SMEs are mixed with the survey evidence in the study using the mixed-method research design. The effects of productivity are measured in terms of operation efficiency, decreased downtime, quality, and responsiveness of the supply chain, whereas the effects of employment are measured regarding creation of jobs, change in tasks, intensity of skill, and productivity of labour. The results show that the cloud-to-edge architectures contribute to much better visibility of the processes and predictive maintenance as well as latency reduction in comparison to cloud-only systems, which translate to productivity gains. The impact on employment is less obvious: there is a tendency to replace routine tasks with robots, but there are new jobs in the field of supervising systems, data processing and management, and high demand for reskilling. The paper identifies policy, infrastructure, and capability-building issues that should be in place to result in inclusive digital transformation.