Automated drug dispenser based on pressure ejection of medications

Various types of automated drug dispensers exist in the market. However, they usually involve extraction of medications from their packaging and their temporary storage in internal bins. In this paper, we propose a different approach which can bypass this step through pressure ejection of medications (especially capsules) from their packaging strips. Further, it is proposed that a relevant consensus between various pharmaceutical manufacturers for standardization of the size and packaging of medications can allow for increased automation in the dispensation of medications to patients without altering the logistics of the existing manual dispensation of medications

Qubit(s) transfer in helical spin chains

Qubit(s) transfer through a helical chain is studied. We consider the transfer of a single state and Bell states across a multiferroic spin chain and the possibility of an electric field control of the fidelity of the single state and the Bell pairs. We analyze pure and imperfect multiferroic spin chains. A scheme for an efficient transfer of spin states through a multiferroic channel relies on kicking by appropriate electric field pulses at regular interval. This electric field pulse sequence undermines the effect of impurity on the fidelity and improves the state transfer through the helical chain.Comment: 7 pages, 10 figure

A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification

Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. Our model outperforms the prior work by 9.53% F1-Score

Recognition Character Sanskrit Using Convolution Neural Network

This research presents a pioneering approach using Convolutional Neural Networks (CNNs) for character recognition in Sanskrit, a language renowned for its intricate script and diverse character set. Addressing challenges posed by Sanskrit's complex script and historical variations in writing styles, we developed a CNN-based model that undergoes meticulous preprocessing to enhance image quality and normalize writing styles. Trained on a substantial dataset of annotated Sanskrit characters, our model showcases remarkable accuracy in recognizing Sanskrit characters, even amidst noise and diverse writing styles. This achievement holds significant implications for digitizing ancient manuscripts, aiding linguistic research, and preserving cultural heritage. Automating Sanskrit character recognition accelerates the analysis of Sanskrit texts, offering insights into linguistic evolution, cultural practices, and historical narratives. Moreover, this research lays a foundation for advancing character recognition techniques in complex scripts and languages, fostering opportunities for preserving and exploring diverse cultural heritages worldwide

Role of Encoders and PLC in Electrical Control Drives of CNC Machines and Automation

Today is the age of automation with the applications of Electrical Power and Drives, invariably, in all modern industries,CNC machines, transportation system, Metro Trains, Automobile Vehicles and domestic applications. PLCs and microcontrollers are inbuilt combination of various Digital Integrated Circuits. Most of the automobile vehicles(medium and heavy) are rapidly changing the mechanical drives and engine parts with electrical and electronic control devices. This includes,ignition control, combustion, engine cooling and emergency signals.Electrical drives are playing vital role in speed variation with reduced power consumption for heavy duty motor. In this paper,efforts have been made, to focus on the role and application of Encoder and PLC, in CNC Machines and Automation

FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering

Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disruption in the share market, panic and anxiety in society, and even death during crises. Therefore, disinformation should be identified promptly and, if possible, mitigated. With approximately 3.2 billion images and 720,000 hours of video shared online daily on social media platforms, scalable detection of multimodal disinformation requires efficient fact verification. Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. Salient features of the dataset include: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii) associated images, (iv) stable diffusion-generated additional images (i.e., visual paraphrases), (v) pixel-level image heatmap to foster image-text explainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake news stories.Comment: arXiv admin note: text overlap with arXiv:2305.0432

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

A long-term comparative assessment of human health risk to leachate-contaminated groundwater from heavy metal with different liner systems

The handling and management of municipal solid waste (MSW) are major challenges for solid waste management in developing countries. Open dumping is still the most common waste disposal method in India. However, landfilling also causes various environmental, social, and human health impacts. The generation of heavily polluted leachate is a major concern to public health. Engineered barrier systems (EBSs) are commonly used to restrict potentially harmful wastes by preventing the leachate percolation to groundwater and overflow to surface water bodies. The EBSs are made of natural (e.g., soil, clay) and/or synthetic materials such as polymeric materials (e.g., geomembranes, geosynthetic clay liners) by arranging them in layers. Various studies have estimated the human health risk from leachate-contaminated groundwater. However, no studies have been reported to compare the human health risks, particularly due to the leachate contamination with different liner systems. The present study endeavors to quantify the human health risk to contamination fromMS