Search Result

As potentially pathogenic organisms were previously isolated from different inanimate objects, it was decided to investigate, in this study, the extent of contamination of the press buttons or touch screens of public vending machines, that have now become so diverse and distributed in almost all public areas in any community. Twelve vending machines located in different places in public areas in the Ras-Beirut area were included in the study. Standard microbiology laboratory techniques were conducted and several bacterial specieswere isolated from the tested sites of the machines in the following frequencies: S. aureus (67%), coagulase-negative staphylococci. (33 %), Enterobacter cloacae (25%),Klebsiella pneumoniae (17 %), Citrobacterfreundii(17%),Serratiaficaria(17%),Rahnellaaquatilis(17%), Escherichia vulneris(8 %), Pantoeasp. (8 %), and Serratiaplymuthica(8 %). Vending machine buttons or touch screenswere proved to act as vehicles of potential pathogens, unintentionally having a significant impact on the general health of the community. The results of this study suggest the need for improved sanitization of the vending machine buttons or touch screens. However, in order to minimize the exposure to pathogens and transmission of any microbial infections among users of the vending machines, it is also recommended that proper individual hygienic precautionary measures be always taken.

This model is developed for making of Kshar-Sutra (a medicated thread) which is routinely used in the treatment of Fistula in Ano & Haemorrhoids. Even today when the modern surgery is at it’s peak, fistula-in-ano is still a challenge to the surgeons for its incidence of recurrence and post operative complications. Kshar-sutra is an Ayurvedic surgical treatment which involves management of Anorectal disorders for treating fistula-in-ano & Haemorrhoids. Use of Kshar-Sutra in fistula in Ano, this technique being used traditionally in India from last 10 centuries. So in rest of the world, there is no chance of making of Kshar-Sutra and such kind of model or machines. Traditional method of preparation of Kshar-Sutra is so hectic / difficult and time consuming. Generally in traditional method, it took at least 7 days to make in summer season and more in other seasons, but due to this model/ instrument, same thing can be perform only within 2 hours in any season. This model saves time, raw material, maintains sterility, freshness with more efficacy and produce more quantity at a time. Various kind of treatment methods are available today still treatment of Fistula in Ano is very complicated. Fistula in Ano is very notorious for its recurrence. But Kshar- Sutra therapy is very efficient in the treatment of Fistula in Ano. Not only it cures but also avoids recurrence. But there are scarcity of Kshar- Sutra. Everybody can not make it. Preparation of Kshar-Sutra is very difficult task. Till today, surgeons restored kshar-sutra in large quantity, but after some period, the efficacy of old kshar-sutra is remains questionable. But this instant automatic kshar-sutra machine makes it easy. Not only this but also overcomes all problems of preparation method of Kshar sutra.

This study introduces an IoT and deep learning-based framework designed to enhance biomedical waste management through real-time monitoring and efficient waste classification. By utilising IoT sensors in conjunction with advanced machine learning models and autoencoders, the system automates waste tracking, timely collection, and the categorisation of hazardous materials. IoT sensors enable continuous data collection on waste generation, supporting pattern analysis to optimise waste management processes. This approach addresses critical challenges in mismanagement, including the prevention of spills, control of odours, and the handling of hazardous waste types. By leveraging real-time data, healthcare facilities can improve the segregation and disposal of BMW, minimising risks to human health and the environment. The integration of these technologies contributes to a cleaner, more sustainable waste management ecosystem, ensuring regulatory compliance and enhancing operational efficiency. Ultimately, the proposed system demonstrates how IoT and AI-driven solutions can revolutionise waste management in healthcare settings, mitigating environmental impacts and improving overall public health outcomes.

Vulnerable Road Users (VRUs), such as pedestrians and cyclists, are among the most exposed participants in road traffic environments, and their reliable perception remains a key challenge for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Automotive radar operating in the high- frequency 77 GHz band provides robust sensing capabilities with high range and angular resolution, making it well suited for VRU detection under adverse weather and lighting conditions. This paper presents a real-time artificial intelligence–based frame- work for VRU classification using 77 GHz automotive radar measurements. Radar signal processing techniques are applied to extract discriminative kinematic and micro-Doppler features that capture the distinctive motion characteristics of pedestrians and cyclists. These features are used to train a Support Vector Machine (SVM) classifier, selected for its strong generalization capability and low computational complexity, which is critical for real-time automotive applications. The proposed system is evaluated using real-world radar data collected in urban traffic scenarios, demonstrating reliable separation between pedestrian and cyclist classes across varying speeds and motion patterns. The results indicate that the proposed approach provides an effective and computationally efficient solution for real-time VRU classification, supporting its integration into safety-critical ADAS and autonomous driving systems.