Assessment of Crop Water Productivity of Maize in Sub-Trophical Conditions under Tube Wells Irrigation System

This research study was conducted to find the water productivity, relationship between water productivity and depth of water applied and yield of maize crop. The study was based on two years primary data collected in 2012 and 2013 and the questionnaire was used as a research tool for collection of data required for the study. The effective rain fall was estimated through CROPWAT (computer based programme) using the meteorological data obtained from WAPDA (Water and Power development authority), where the discharges of tubewells were determined by volumetric method. The number of irrigations, time taken by each turn and crop yield was recorded with the help of a questionnaire. Result showed that the average yield of maize was 4295 kg ha-1, while the average water productivity was 0.76 kg m-3. The crop water productivity of maize ranged from 0.05kgm-3 to 1.8kgm-3, whereas the yield ranged from 978.26kg/ha to 7500kg/ha .The water productivity of maize decreased from 1.8kgm-3to 0.89kgm-3 when the depth of applied water increased from 99mm to 543mm. The results further revealed that the inadequate knowledge of farmers regarding depth of water to be applied at proper time and the frequency of irrigation were the main reasons for the low water productivity. It can be recommended that irrigation management with minimum applied depth of water is an important step toward the maximum crop water productivity. Keywords: Tube wells, Yield, water productivity, Maize, CROPWAT

Classification of EEG Signal Using Wavelets and Machine Learning Techniques

Brain is the crucial organ which performs various functions of body. Electroencephalogram (EEG) is an efficient modality which helps to acquire brain signals from scalp. Analysis of EEG signals helps in diagnosis and treatment of brain diseases like epilepsy seizure, and various problems associated with brain disorders. These signals are contaminated with unwanted artifacts and complicate to analyze. The diagnosis requires flawless analysis of EEG signals. Different methods are proposed to examine with high accuracy. In this paper, Discrete Wavelet Transform is used to pre-process the EEG signal and decompose into five frequency bands and then, the features like mean, standard deviation, RMS, entropy, energy and relative energy are computed. These features are evaluated by machine learning classifier such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naive Bayes (NB). The results demonstrate the highest classification accuracy (99.5%), Specificity (100%), Sensitivity(100%) by SVM for normal  versus epileptic subjects

Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models

Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. To this end, we analyze social media data from two disaster events in two different countries to identify reported impacts to CIFs as well as their impact severity and operational status. We employ state-of-the-art open-source LLMs to perform computational tasks including retrieval, classification, and inference, all in a zero-shot setting. Through extensive experimentation, we report the results of these tasks using standard evaluation metrics and reveal insights into the strengths and weaknesses of LLMs. We note that although LLMs perform well in classification tasks, they encounter challenges with inference tasks, especially when the context/prompt is complex and lengthy. Additionally, we outline various potential directions for future exploration that can be beneficial during the initial adoption phase of LLMs for disaster response tasks.Comment: Accepted to appear at the 2024 ISCRAM conferenc

Immersive Technologies in Virtual Companions: A Systematic Literature Review

The emergence of virtual companions is transforming the evolution of intelligent systems that effortlessly cater to the unique requirements of users. These advanced systems not only take into account the user present capabilities, preferences, and needs but also possess the capability to adapt dynamically to changes in the environment, as well as fluctuations in the users emotional state or behavior. A virtual companion is an intelligent software or application that offers support, assistance, and companionship across various aspects of users lives. Various enabling technologies are involved in building virtual companion, among these, Augmented Reality (AR), and Virtual Reality (VR) are emerging as transformative tools. While their potential for use in virtual companions or digital assistants is promising, their applications in these domains remain relatively unexplored. To address this gap, a systematic review was conducted to investigate the applications of VR, AR, and MR immersive technologies in the development of virtual companions. A comprehensive search across PubMed, Scopus, and Google Scholar yielded 28 relevant articles out of a pool of 644. The review revealed that immersive technologies, particularly VR and AR, play a significant role in creating digital assistants, offering a wide range of applications that brings various facilities in the individuals life in areas such as addressing social isolation, enhancing cognitive abilities and dementia care, facilitating education, and more. Additionally, AR and MR hold potential for enhancing Quality of life (QoL) within the context of virtual companion technology. The findings of this review provide a valuable foundation for further research in this evolving field

The Buncefield Accident

The Process failure that occurred at Buncefield site, Hertfordshire, UK was one of the landmark incidents in the process safety concerns of vapor cloud explosion. The vapor cloud that formed was due to overfilled large storage tank, containing unleaded fuel. The overflow of the tank was the result of a failed level indicating system and lack of operator‟s attention at the site. A legal investigation on the incident was carried out by Buncefield Major Incident Investigation Board (BMIIB), which presented the causes for the explosion and the recommendations for future prevention. The report briefly discusses the series of steps that led to the major incident. Prior to the Buncefield, a massive explosion on such scale was not predicted; hence the Buncefield incident breaches the worst case scenario that was predicted for vapor cloud explosion. The report also provides the explanation regarding why the explosion breaches the worst case scenario for predicted strength of the vapor cloud explosion. Moreover similar accidents are also presented along with the recommendations presented by Buncefield Major Incident Investigation Board

Anti-diabetic drug utilization patterns in a government hospital in Saudi Arabia

Purpose: To evaluate the prescription patterns of anti-diabetic drugs in a government hospital in Saudi Arabia.Methods: Retrospective prescription information and medical records of patients who visited outpatient clinics during the last one year were used. The prescriptions were grouped into three: appropriate, partially appropriate and inappropriate. A total of 504 prescriptions were evaluated, while the male to female ratio was 3:1.Results: The mean anti-diabetic drug per prescription was 2.08 ± 0.85. The most common prescriptions were metformin, sulfonylurea and insulin. More than two-thirds of the patients were on combination therapy. No prescriptions were found for thiazolidinediones, glucagon-like peptide-1 (GLP-1) analogues and α-glucosidase inhibitors. Metformin/sulfonylurea was the most common combination. The patients that received insulin with an oral agent accounted for 8 % of the total prescriptions. While 62 % of the patients reached fasting blood glucose goal of ≤ 126 mg/dl, there was no correlation between normoglycemia and total number of drugs, gender or age group. Moreover, age, sex, initial glucose concentration, and total drugs had no effect on final glucose levels.Conclusion: Prescription patterns of anti-diabetic drugs are in accordance with internationalguidelines but some shortcomings were observed probably due to poor prescription writing.Keywords: Diabetes mellitus, Pharmacoepidemiology, Metformin, Interventions, Prescriptio

The Buncefield Accident

The Process failure that occurred at Buncefield site, Hertfordshire, UK was one of the landmark incidents in the process safety concerns of vapor cloud explosion. The vapor cloud that formed was due to overfilled large storage tank, containing unleaded fuel. The overflow of the tank was the result of a failed level indicating system and lack of operator‟s attention at the site. A legal investigation on the incident was carried out by Buncefield Major Incident Investigation Board (BMIIB), which presented the causes for the explosion and the recommendations for future prevention. The report briefly discusses the series of steps that led to the major incident. Prior to the Buncefield, a massive explosion on such scale was not predicted; hence the Buncefield incident breaches the worst case scenario that was predicted for vapor cloud explosion. The report also provides the explanation regarding why the explosion breaches the worst case scenario for predicted strength of the vapor cloud explosion. Moreover similar accidents are also presented along with the recommendations presented by Buncefield Major Incident Investigation Board

IAA production and maize crop growth promoting potential of endophyte Aspergillus niger (AO11) under salt stress

Maize is cultivated under a broad range of soil conditions and environments. Maize is slightly vulnerable to salt stress and therefore it is seriously affected by soil salinity all over the world. Recognizing the responses of maize to salt stress and making a good strategy to overcome this problem could aid to develop solutions in saline areas to improve maize productivity. We investigated in this research the impacts, tolerance and salt stress management in corn. Many endophytic fungi can produce the Indole-3-acetic acid (IAA) is known for their role in plant growth and development both with and without salt stress conditions. The current study was focused on the production of IAA by endophytic fungi (Aspergillus niger) and maize seeds germination and promotion of seedling growth and vigor. In order to evaluate the defense response of maize plant, in relation to A. niger, an experiment was designed with three replications of treatments (control, salt stressed, salt stressed inoculated with A. niger, and only A. niger inoculated plants. It was determined that A. niger has the ability to produce the IAA in NaCl and KCl stress peaking 53 μg/ml and was not significantly by alternating the nitrogen and carbon sources in the nutrient broth but increasing the tryptophan concentration raised its production level. High concentration stress of sodium chloride and potassium chloride decrease maize plant seeds germination percentage, shoot and root length also affected the fresh and dry weight of maize. A. niger improves salt resistance in maize and also increased the germination percentage up to 30%, also improved the chlorophyll level and it was proved an effective approach for improving maize germination and growth under salt stress

The synergistic and complementary effects of supply chain justice and integration practices on supply chain performance: A conceptual framework and research propositions

In recent years, firms have been using their supply chain integration (SCI) as a competitive weapon in the intensive, globalised competitive arena. The contingent perspective in supply chain management maintains that it is necessary to observe the interaction between SCI practices and supply chain justice. A critical issue to be resolved is whether this fit leads to synergistic and complementary effects on supply chain performance. In order to contribute to this research problem, we analysed supply chain justice instances in order to determine the importance of supply chain justice, as well as highlights complementary role in SCI and its influences on supply chain performance. A conceptual framework has been developed and five propositions established to verify the contents of a theoretical study. Accordingly, balancing the adoption of SCI practices and supply chain justice will lead to the generation of greater benefits relative to the effect of both independent driving forces on supply chain performance. Furthermore, the proposed framework has been analysed in order to examine its applicability in the South African context. The study thereby suggests the empirical research guidelines and the paper concludes with a discussion of future research

Analyzing Textual Data for Fatality Classification in Afghanistan's Armed Conflicts: A BERT Approach

Afghanistan has witnessed many armed conflicts throughout history, especially in the past 20 years; these events have had a significant impact on human lives, including military and civilians, with potential fatalities. In this research, we aim to leverage state-of-the-art machine learning techniques to classify the outcomes of Afghanistan armed conflicts to either fatal or non-fatal based on their textual descriptions provided by the Armed Conflict Location & Event Data Project (ACLED) dataset. The dataset contains comprehensive descriptions of armed conflicts in Afghanistan that took place from August 2021 to March 2023. The proposed approach leverages the power of BERT (Bidirectional Encoder Representations from Transformers), a cutting-edge language representation model in natural language processing. The classifier utilizes the raw textual description of an event to estimate the likelihood of the event resulting in a fatality. The model achieved impressive performance on the test set with an accuracy of 98.8%, recall of 98.05%, precision of 99.6%, and an F1 score of 98.82%. These results highlight the model's robustness and indicate its potential impact in various areas such as resource allocation, policymaking, and humanitarian aid efforts in Afghanistan. The model indicates a machine learning-based text classification approach using the ACLED dataset to accurately classify fatality in Afghanistan armed conflicts, achieving robust performance with the BERT model and paving the way for future endeavors in predicting event severity in Afghanistan.Comment: 6 pages, 4 figures, 2 table