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

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

Tabular and Deep Reinforcement Learning for Gittins Index

In the realm of multi-arm bandit problems, the Gittins index policy is known to be optimal in maximizing the expected total discounted reward obtained from pulling the Markovian arms. In most realistic scenarios however, the Markovian state transition probabilities are unknown and therefore the Gittins indices cannot be computed. One can then resort to reinforcement learning (RL) algorithms that explore the state space to learn these indices while exploiting to maximize the reward collected. In this work, we propose tabular (QGI) and Deep RL (DGN) algorithms for learning the Gittins index that are based on the retirement formulation for the multi-arm bandit problem. When compared with existing RL algorithms that learn the Gittins index, our algorithms have a lower run time, require less storage space (small Q-table size in QGI and smaller replay buffer in DGN), and illustrate better empirical convergence to the Gittins index. This makes our algorithm well suited for problems with large state spaces and is a viable alternative to existing methods. As a key application, we demonstrate the use of our algorithms in minimizing the mean flowtime in a job scheduling problem when jobs are available in batches and have an unknown service time distribution.

Economic Analysis of Cucumber Market Performance and their Constraints in Sultanpur District of Uttar Pradesh

In this paper examine the marketing efficiency, marketing cost, market margin, price spread and marketing constraints in Sultanpur district of Uttar Pradesh. A multistage stratified purposive cum random sampling technique was applied for the selection of district, block, villages and respondents. Total 100 growers were selected randomly through proportionate allocation method in the population during 2021-22. The primary data were collected through survey schedule with the help of personal interviews. The objectives were achieved by using shephered's formula for marketing efficiency and Garrett ranking technique for the identify the major marketing constraints. There are three types of marketing channels were observed in cucumber marketing i.e., Channel-I (producer → consumer), Channel-II (producer → retailer → consumer) and Channel-III (producer → wholesaler → retailer → consumer). Overall maximum produce of cucumber was sold by different group of farms through channel-III. Moreover, cucumber growers were also faced different types of marketing constraints. Major three marketing constraints faced by the growers were perishability nature of commodity, lack of storage facilities and grading problems, respectively. Suggestions of cucumber farmers to overcome these constraints are storage facilities in mandi should be provided to the farmers for their perishable products and entrusting government representatives with marketing duties in exchange for a fair fee

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

Therapeutic Potential of Bombax ceiba Linn. - A Review

Shalmali, botanically identified as Bombax ceiba Linn., a robust long-lived tree found abundantly growing across country, has been in the focus of pharmacotherapeutic applications in Ayurveda since time immemorial. Almost every plant part like root, stem bark, leaves, flowers, Gum exudate have been described with different type of pharmacological properties and therapeutic potential in classical texts of Ayurveda to treat a number of diseases. According to Ayurveda, it has stimulant, astringent, hemostatic, aphrodisiac, diuretic, antidiarrheal, emetic, demulcent, anti-dysenteric, and antipyretic properties. Numerous pharmacological properties of the Shalmali have been shown in both in vitro and in vivo research, including analgesic, immunomodulatory, anti-inflammatory, antioxidant, anti-acne, antimicrobial, hypotensive, hypolipidemic, and antihyperglycemic effects. Present review is an attempt to bring forth the pharmaceutical and nutraceutical potential of various plant parts of Shalmali

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

DiscoveryBench: Towards Data-Driven Discovery with Large Language Models

Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of provided datasets? To evaluate this question, we present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. The benchmark is designed to systematically assess current model capabilities in discovery tasks and provide a useful resource for improving them. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering, by manually deriving discovery workflows from published papers to approximate the real-world challenges faced by researchers, where each task is defined by a dataset, its metadata, and a discovery goal in natural language. We additionally provide 903 synthetic tasks to conduct controlled evaluations across task complexity. Furthermore, our structured formalism of data-driven discovery enables a facet-based evaluation that provides useful insights into different failure modes. We evaluate several popular LLM-based reasoning frameworks using both open and closed LLMs as baselines on DiscoveryBench and find that even the best system scores only 25%. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.Comment: Website: https://github.com/allenai/discoverybenc