Exploring the effect of fertilizer application on yield and decoding CO2 flux under flooded paddy conditions towards sustainable agriculture

The impact of organic and inorganic nutrient management on the emission of carbon dioxide (CO2), soil properties such as available nutrients, microbial population and soil organic carbon (SOC) were investigated in paddy (Oryza sativa L.) field (at 8o 46’ N Latitude and 77o 42?’ E Longitude) under flooded condition during late pishanam season in 2023-2024. The treatments were designed to observe the effects of applying fertilizer based on the soil test crop response (100 % STCR-based NPK) that had been modified by organic amendments, which include absolute control (unfertilized), inorganic fertilizers, sole application of organic amendments (Farm yard manure, Green leaf manure, Vermicompost and Poultry manure) and combined these organic amendments with inorganic fertilizers. The main objective of this study is to understand the intricate relationship between fertilizers and carbon flux in paddy soils, which is crucial for developing sustainable agricultural practices that minimize environmental harm while ensuring food security. The observation of the experimental field study reported that the combined application of poultry manure at the rate of 5 tonnes per hectare with 100% STCR-based inorganic fertilizer recorded maximum yield and yield attributes. The treatment combination of poultry manure + inorganic fertilizer enhanced in sequestrating the soil organic carbon (0.67%) resulted in higher grain yield (5972 kg ha-1) and also observed that this combination will limit the emission of CO2 to the atmosphere. Therefore, it could be a better choice for carbon storage and higher productivity in a sustainable rice cropping system

Acute-on-Chronic Liver Failure (ACLF): The ‘Kyoto Consensus’-Steps From Asia

Acute-on-chronic liver failure (ACLF) is a condition associated with high mortality in the absence of liver transplantation. There have been various definitions proposed worldwide. The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set in 2004 on ACLF was published in 2009, and the APASL ACLF Research Consortium (AARC) was formed in 2012. The AARC database has prospectively collected nearly 10,500 cases of ACLF from various countries in the Asia-Pacific region. This database has been instrumental in developing the AARC score and grade of ACLF, the concept of the \u27Golden Therapeutic Window\u27, the \u27transplant window\u27, and plasmapheresis as a treatment modality. Also, the data has been key to identifying pediatric ACLF. The European Association for the Study of Liver-Chronic Liver Failure (EASL CLIF) and the North American Association for the Study of the End Stage Liver Disease (NACSELD) from the West added the concepts of organ failure and infection as precipitants for the development of ACLF and CLIF-Sequential Organ Failure Assessment (SOFA) and NACSELD scores for prognostication. The Chinese Group on the Study of Severe Hepatitis B (COSSH) added COSSH-ACLF criteria to manage hepatitis b virus-ACLF with and without cirrhosis. The literature supports these definitions to be equally effective in their respective cohorts in identifying patients with high mortality. To overcome the differences and to develop a global consensus, APASL took the initiative and invited the global stakeholders, including opinion leaders from Asia, EASL and AASLD, and other researchers in the field of ACLF to identify the key issues and develop an evidence-based consensus document. The consensus document was presented in a hybrid format at the APASL annual meeting in Kyoto in March 2024. The \u27Kyoto APASL Consensus\u27 presented below carries the final recommendations along with the relevant background information and areas requiring future studies

Residence Automation System Using Internet of Things

Home automation using Internet of Things is full of automation and the human being’s life is getting easier as almost everything is systematic by replacing the conventional/manual systems. Recent days, the internet is the mixed part of every human life to gather the knowledge of technology. Internet of things provides a source the devices to connect, sensed by sensors and controlled by remote across a grid infrastructure. Various kind of sensors are designed to meet the need of Home automation such as flame sensor, gas sensor, touch sensor, soil moisture sensor, Infrared sensor where it is connected with Arduino board and it automatically controls the environment. Infrared Sensor is used to find the movement of objects and is used in home automation for example we can use infrared sensor in car shed to monitor the car activities. Based on the car motion we can use infrared sensor. Infrared sensor is used to sense the car movement based on the movement light is automatically changed into on condition and if there is no movement the light turns into off condition. The Arduino board have microcontroller to control all the process occurred in home and it can be sensed by the various sensor and it can be alert to the owner by mail or any other process. The results are obtained with the help of Arduino Software and Arduino UNO for all the sensors.</jats:p

Building Domain-Specific Lexicons: An Application to Financial News

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- -- 153122Natural Language Processing (NLP) has gained attention in the recent years. Previous research (such as WordNet and Cyc) has focused on developing an all purpose (generalised) polarised lexicons. However, these lexicons do not provide much information in different domains such as Finance and Medical Sciences. Using these lexicons for text classification could affect the prediction accuracy. Therefore, there is a need for building domain- and context-specific lexicons. To achieve this, in this work, a label based propagation based word embedding algorithm has been proposed to obtain positive and negative lexicons. The proposed algorithm works on the principle of graph theory and word embedding. The proposed algorithm is tested on Dow Jones news wires text feed to classify the Financial news as hot and non-hot. Three classifiers, namely, Logistic Regression, Random Forest and XGBoost, employing polarised lexicons, seed words and random words were used. The performance of classifiers in all cases was evaluated using accuracy. Lexicons generated using the proposed approach were effective in classifying the Financial news articles as hot and non-hot compared to classifiers using seed words and random words. Proposed label propagation with word embedding algorithm generates context-specific lexicons, which aids in helps in better representation of text in natural processing tasks and avoids the problem of dimensionality. © 2019 IEEE.CRDPJ-499983-16, OCE VIP II 26280This research is supported in part by the following grants: NSERC CRDPJ-499983-16; OCE VIP II 26280; and TMX