In the intensive care unit (ICU), managing traumatic brain injury (TBI) patients presents significant challenges due to the dynamic interaction between physiological and clinical markers. This study aims to uncover these subtle interconnections and identify the key ICU markers for the timely care of TBI patients using advanced machine-learning techniques. We combined correlation-based network analysis and graph neural network (GNN) techniques to explore relationships among electrocardiography (ECG) features, vital signs, pathology test results, Glasgow Coma Scale (GCS) scores, and demographics from 29 TBI patients admitted to the Gold Coast University Hospital (GCUH). Our findings highlighted that the final GCS index strongly correlated with arterial and diastolic blood pressure variations, patient demographics such as gender and age, and certain heart rate variability (HRV) features. Variability in diastolic blood pressure, GCS, and pNN50 (an HRV measure) demonstrated strong associations with several other physiological and clinical markers during the first 12 hours post-ICU admission. HRV features and variability in physiological signals during the first 12 hours in the ICU are important factors in assessing the severity of TBI patients.

Accurately predicting early mortality risk for traumatic brain injury (TBI) patients admitted to the intensive care unit (ICU) is crucial for optimizing patient care, allocating resources effectively, and reducing mortality rates. This study introduces an approach to predict mortality risk for TBI patients by analysing heart rate variability from the first 24 h of electrocardiogram (ECG) signals. A deep learning hybrid model was developed by integrating a weight predictor with a bidirectional long short-term memory (BiLSTM) unit. This hybrid architecture enhances predictive performance by weighting features and capturing patterns in HRV data. This study utilised TBI patient data from the Gold Coast University Hospital and Cerebral Haemodynamic Autoregulatory Information System (CHARIS) for model training and testing. The experimental results demonstrated that the proposed hybrid model achieved cross-validation metrics, including an accuracy of 0.933 (95

Journal Article Accepted manuscript Kuldeep Kumar's contribution to the Discussion of the "Discussion Meeting on the Analysis of citizen science data" Get access Kuldeep Kumar Kuldeep Kumar Bond University, Australia Email: [email protected] Search for other works by this author on: Oxford Academic Google Scholar Journal of the Royal Statistical Society Series A: Statistics in Society, qnaf014, https://doi.org/10.1093/jrsssa/qnaf014 Published: 11 February 2025 Article history Received: 02 October 2024 Accepted: 16 December 2024 Published: 11 February 2025

Dynamic mortality risk prediction in the intensive care unit (ICU) is crucial for supporting clinicians' decision-making, specifically in traumatic brain injury (TBI) patients. We aim to develop and evaluate a dynamic deep learning (DL) framework that can provide hourly updates of 30-day mortality risk prediction for TBI patients following ICU admission. Using demographics and timeseries physiological data, a recurrent neural network-based model was trained on data from 135 TBI patients admitted to the Gold Coast University Hospital (GCUH) in Australia. Model's performance was evaluated utilizing the area under the receiver operating characteristics (AUC), Matthews correlation coefficient (MCC), accuracy, and other metrics, performed calibration and decision curve analysis to interpret the model's output and determine its clinical usefulness. The Shapley additive explanation algorithm was utilized to clarify the contribution of features to the predictions. The proposed method showed predictive performance on the cross-validation dataset that improved over time: MCC 0.24 and AUC 0.713 for the prediction at 24 h after admission, 0.451 and 0.756 at 72 h, 0.519 and 0.803 at 120 h, and 0.748 and 0.946 before twelve hours to the outcome (either death or discharge), respectively. The model was further tested with a holdout test dataset with 34 TBI patients, achieving an average prediction accuracy of 0.851, AUC of 0.632, and MCC of 0.403, respectively, in the first 24-h interval. The proposed model demonstrates proof of principle with explainable results in predicting mortality risk, encouraging further development and validation in a clinical setting.

Alzheimer's disease (AD) is a prevalent neurodegenerative condition impacting the elderly population. Despite its widespread occurrence, the precise etiological factors remain elusive, emphasizing the critical need for early detection and intervention to mitigate disease progression. This study aims to development of an Alzheimer's Risk Index, a novel tool designed to evaluate socio-demographic factors, lifestyle, medical history, behavioural patterns, and neuropsychological factors contributing to Alzheimer's risk and cognitive decline. In this study, we used participants aged 55 to 91 from the ADNI database and employed a unique method using Factor Analysis (FA) in the index development This study's cutting-edge approach to index development using ADNI database and PCA provides valuable insights into potential associations between the Alzheimer's Risk Index and the prevalence of AD. This index is expected to be easily accessible to the general population as a guidance for seeking medical diagnosis of AD early detection.

Trading decision-making is significantly influenced by psychological resistance that emerges under dynamic market conditions. Fear and greed states provide a quantifiable representation of these behavioral dynamics, serving as the basis for sentiment modeling. This study proposes a novel set of buy-and-sell pressure induced lagged features and integrates them with machine learning to predict multiclass fear–greed states in the Bitcoin market. To address severe class imbalance across five sentiment categories, we employ a one-step-ahead rolling window backtesting procedure. The predictive performance of extreme gradient boosting (XGBoost), support vector regression (SVR), and long short-term memory (LSTM) networks is systematically evaluated. Results demonstrate that SVR combined with the proposed lagged features achieves the highest performance, yielding an area under the curve (AUC) of 0.93 and outperforming both XGBoost and LSTM. These findings underscore the effectiveness of feature engineering based on buy-and-sell pressure in enhancing sentiment forecasting for volatile cryptocurrency markets. Beyond predictive accuracy, the framework offers practical applicability, enabling data-driven trading strategies and integration into automated trading systems for continuous market monitoring and decision execution.

The choice of marketing channels holds significant implications for the economic welfare and stability of smallholder dry chilli farmers in India. This study aims to investigate the impact of participating in modern marketing channels on the economic welfare of smallholder dry chilli farmers. Dry chilli marketing in Andhra Pradesh encompasses both traditional and modern channels. Traditional avenues include Agricultural Produce Market Committees, while modern options involve linking with retail malls and utilizing the Kalgudi e-market online platform. The first stage of multivariate endogenous switching regression model (MESRM) reveals significant determinants influencing farmers' participation in modern channels. Factors like access to extension services, education, technical support from ANGRAU and the Department of Agriculture, engagement with retail malls and e-markets, access to market information, and membership in Farmers' Producer Organizations encourage farmers to adopt modern channels. The subsequent MESRM stage reaffirms these factors' positive impact on household welfare across various marketing channels. The study's focal point, Average Treatment Effects, highlights substantial income improvements for participants in modern marketing channels. The counterfactual analysis reveals that smallholder farmers engaging in modern marketing channels would have experienced lower gross economic welfare if they had not participated. These findings underscore modern channels' vital role in enhancing smallholder farmers' economic well-being. So, Government entities and agricultural institutions should prioritize developing linkages between farmers and retail malls. Ensuring robust digital infrastructure, including reliable internet connectivity and user-friendly online platforms, is essential to empower farmers in navigating modern channels effectively. Furthermore, policymakers should consider hybrid marketing strategies that seamlessly blend traditional and modern channels to cater to the diverse preferences of consumers. By acknowledging these findings and implementing corresponding policies, stakeholders can contribute to the growth and prosperity of smallholder dry chilli farmers, fostering sustainable development in the agricultural sector.

This study offers a comprehensive analysis of the impact of extreme weather events, intensified by climate change, on India's real Gross National Product. Focusing on hydro-geological factors crucial for India's Gross National Product, the Auto Regressive Distributed Lag model reveals significant insights. Notably, the study reveals a significant positive correlation between the lagged real GNP (GNP(-1)) and current real GNP, emphasizing the persistence of economic growth trends. Gross Fixed Capital Formation emerges as a key determinant, with a 0.50% increase in economic growth corresponding to a 1% rise in GFCF. Forest land and rainfall exhibited substantial positive associations, contributing to a noteworthy 1.674 and 0.099% with economic growth. Conversely, CO2 emissions, rising temperatures (both Maximum Temperature and Minimum Temperature) exerted a significant negative influence on real GNP, emphasizing the need for sustainable emission reduction strategies. ARDL Bounds Test, revealed existence of a long-run relationship between real GNP and the selected variables. An ECM-Long Run Test underscores the lasting impact, with a 32.4% adjustment towards long-run equilibrium. The study establishes a bi-directional causality between real Gross National Product and carbon dioxide emissions, emphasizing the interconnectedness of economic growth and emissions. This underscores the urgency for addressing climate change alongside sustainable economic development. The findings serve as a crucial guide for evidence-based policymaking, urging proactive strategies to navigate climate challenges and ensure a prosperous, sustainable future for India. The findings contribute to the existing literature, emphasizing the multifaceted nature of factors shaping India's sustained economic development.

Despite the fact that crop residues are abundant in plant nutrients, farms burn a lot of them—roughly 90 metric tons—mostly to make room for new crops to be sown. However, a manpower shortage prevents farmers from burning crop residues. They simply burn off the residue in order to dispose of it (NPMCR, 2019). Raindrop impact is absorbed by crop residue, and wind is deterred from blowing across the soil surface. This lessens wind and water erosion as well as the separation of soil particles. Additionally, there is less surface crusting through the soil's surface, which enhances infiltration and lowers runoff. According to Cox et al. (2004), crop leftovers serve as substitute hosts for a variety of insects, pests, and illnesses. In light of the aforementioned, we work to gather information and provide a range of technical and policy options for managing agricultural straw in order to outlaw burning it, increase soil fertility, and stop environmental damage. Important suggestions include incorporating crop residues into soil, following the ICAR's advice on crop rotation or in the soil care given to farmers, encouraging competitive alternatives for using crop residues in small-scale industries like straw paper, cardboard, and packaging materials, and establishing biomass power plants under a public-private partnership model to guarantee farmers financial returns and maintain soil fertility and food production while halting environmental degradation in the nation.

Vegetables are important source of farm income, assures more farm employment and marketing of vegetables has significant importance due to perishability, seasonality, bulkiness and high post-harvest losses in transportation and storage. In India, Andhra Pradesh have vegetables area of 228.73 thousand hectares (2.08%) and production of 6084.7 thousand tonnes (4.30%) (Agricultural Statistics at a Glance, 2021). The main aim of present study is to assess the marketing channel choice of okra farmers in the Guntur district of Andhra Pradesh with a sample of 120 farmers. Results of Multinomial Logistic Regression Model (MLRM) revealed that the farming experience, education and number of middle men, gender, area under crop and distance to market were significant for the farmers who are selling their produce to local wholesaler channel and household size, price of the commodity and number of middlemen, gender, area under crop and distance to market were significant in case of local vendor channel. Gender, area, prompt payment of sales proceeds, price of the commodity, distance to market education and access to credit were significant for the farmers who are selling their produce to retail malls and distance to market and own transport facility, gender, household size and farming experience were significant in case of rythu bazars. Study also revealed that the low bargaining power of farmers, low price of the product especially in the harvesting season, poor infrastructure of marketing channel, poor handling and storage facilities were the major constraints faced by the farmers in marketing of vegetables. Study suggested that proper care has to take to maintain the vegetables availability throughout the year, proper storage and transport facilities to reduce the wastage and post-harvest losses and FPOs and NGOs may strengthen farmer linkages (forward & backward) were important measures for the better marketing of vegetables.

The dynamics of international trade play a pivotal role in shaping economic growth and development for nations worldwide. This significance is particularly pronounced in the context of India’s agrarian economy. With a substantial portion of its population dependent on agriculture, engaging in global trade presents a myriad of advantages. As a member of the World Trade Organization (WTO), India benefits from a framework that fosters transparent and fair-trade relations, enabling dispute resolution and favourable negotiations. India’s agro-climatic diversity grants it a competitive edge in cultivating various agricultural products, predominantly rice. Enabling policies, like Minimum Support Price and subsidies, incentivize farmers, while adherence to international quality standards enhances the acceptance of Indian rice abroad. The gravity model employed in this study to analyze India’s rice trade with major importing countries, offers valuable insights into trade dynamics. When delving into specific determinants of trade, the Heckman selection equation proves useful. Factors like Gross Domestic Product (GDP), per capita income, trade history, and exchange rates consistently impact partner selection. The likelihood of choosing a trading partner is influenced by economic compatibility, historical trade relations and WTO membership. Additionally, shared borders and regional affiliations play a role, while economic recessions tend to decrease partner selection due to reduced demand. Examining trade quantity reveals nuanced dynamics. Historical trade interactions, economic indicators and WTO membership consistently influence trade volumes. Larger GDPs, per capita incomes, and populations of trading partners enhance trade prospects, while disparities in income and exchange rate fluctuations impact trade negatively. Importantly, distance remains a key factor affecting trade volume, as logistical complexities and transportation costs influence trade decisions. These findings shed light on trade dynamics, enabling evidence-based policy decisions to enhance trade relationships, boost competitiveness and propel India’s rice exports to new heights.

Abstract Purpose Traumatic brain injury (TBI) is one of the most common cause of mortality and disability globally. Intensive care unit (ICU) management poses significant challenges for medical practitioners, primarily because of the complex interplay between biomarkers and hidden interactions. This study aimed to uncover subtle interconnections between biomarkers and identify the key factors contributing to TBI characteristics and ICU severity scores. Methods A total of 29 patients with TBI who were admitted to the ICU were selected and analysed using monitoring electrocardiography (ECG), vital signs, Glasgow Coma Scale (GCS) and electronic medical records. This study utilized a methodology that integrates correlation-based network analysis and graph neural network (GNN) techniques to uncover hidden relationships between various biomarkers and identify the most critical monitoring biomarkers for patients with TBI within the first 12 hours of ICU stay. Results The analysis revealed significant associations within the dataset. Specifically, MeanRR exhibited notable connections with alterations in systolic blood pressure and heart rate variations. Moreover, the final GCS showed a strong correlation, including long-term correlation with heart rate variability (HRV) feature alpha2, variability in atrial blood pressure means and diastolic blood pressure, gender, and age. Variability of diastolic blood pressure, GCS ICU scoring values, and pNN50 (an HRV measure) demonstrated strong association with other biomarkers during the first 12 hours following ICU admission. Conclusion HRV as an electronic biomarker and the variability in physiological variables during first 12 hours in the ICU are equally important factors for TBI severity assessment and can offer valuable insights into the patient's health prognosis.

Background: Alzheimer’s disease (AD) is a particular type of dementia that currently lacks a definitive treatment and cure. It is possible to reduce the risk of developing AD and mitigate its severity through modifications to one’s lifestyle, regular diet, and alcohol-drinking habits. Objective: The objective of this study is to examine the daily dietary patterns of individuals with AD compared to healthy controls, with a focus on nutritional balance and its impact on AD. Methods: This study incorporated multiple-factor analysis (MFA) to evaluate dietary patterns and employed Random Forest (RF) classifier and Sparse Logistic Regression (SLR) for Variable Importance analysis to identify food items significantly associated with AD. Results: MFA revealed trends in the data and a strong correlation (Lg = 0.92, RV = 0.65) between the daily consumption of processed food and meat items in AD patients. In contrast, no significant relationship was found for any daily consumed food categories within the healthy control (HC) group. Food items such as meat pie, hamburger, ham, sausages, beef, capsicum, and cabbage were identified as important variables associated with AD in RF and SLR analyses. Conclusions: The findings from MFA indicated that the diversity or equilibrium of daily diet might play a potential role in AD development. RF and SLR classifications exhibit among the processed foods, especially deli meats and food made with meat items, are associated with AD.

Financial statement fraud is a costly problem for society. Detection models can help, but a framework to guide variable selection for such models is lacking. A novel Fraud Detection Triangle (FDT) framework is proposed specifically for this purpose. Extending the well-known Fraud Triangle, the FDT framework can facilitate improved detection models. Using Benford's law, we demonstrate the posited framework's utility in aiding variable selection via the element of surprise evoked by suspicious information latent in the data. We call for more research into variables that measure rationalisations for fraud and suspicious phenomena arising as unintended consequences of financial statement fraud.

India is prominently recognized as the foremost producer of paddy, cultivating this crop over 47.83 million hectares and generating 135.75 million tonnes of paddy, thus playing a substantial role in the worldwide paddy output. However, there is an anticipated decline in paddy production yields due to the projected effects of climate change, estimated to range from 10% to 30% by 2030. In recent times, adaptation to climate change has become a major concern to farmers, policy makers and researchers. Climate resilient rice production practices need to be enhanced at the farm level in order to aid rural residents in improving their household food security. Against this backdrop, the proposed research seeks to fill this crucial knowledge gap by developing and constructing adaptation index tailored specifically for paddy growers in India. Through a literature review and discussions with experts, we have identified indicators and sub-indicators using the indicator approach method. These indicators will help us understand how paddy growers are adapting to climate change and implementing climate resilient practices. The relevancy rating score was obtained from 30 experts in the concerned area. Based on the relevancy score, 8 indicators and 23 sub-indicators of 0.80 and above were considered for inclusion in the adaptation index. To compute the index values for each of the identified indicators, their relative importance in the adaptation practices was worked out by assignment of index values to indicators through Principal component analysis (PCA) based on the high factor loadings exceeded 0.5 of sub-indicators were considered and the findings revealed that disease and management had highest index value of 3.461, followed by methods of paddy establishment (2.195), crop rejuvenation techniques (2.10), altered planting dates (2.049), water saving and management techniques (1.987), nursery management (1.562), paddy varieties (1.342) and spacing (1.214).

This study delves into dynamics and determinants of agricultural exports from India. India's agricultural export basket is heavily reliant on a limited range of commodities, including basmati rice, buffalo meat, spices, tea, coffee, and marine products. Such concentration poses risks, making the sector vulnerable to price fluctuations, changes in global demand, and challenges in accessing specific markets. Furthermore, the declining ratio of export value to import value in recent years indicates an unfavourable trade imbalance. To address these challenges and foster sustainable growth in the agricultural export sector, policymakers must gain a comprehensive understanding about the determinants for agricultural exports. So, this study utilizes panel data encompassing 40 agricultural export items over an 11-year period. The researchers employ the system Generalized Method of Moments (GMM) estimation and the findings showed positive and significant impact of past export performance on current export decisions. Moreover, the study highlights the positive and significant influences of gross output of agriculture, value-added activities in agricultural sector, gross domestic product, trade openness, foreign direct investment, water use efficiency, corruption index and exchange rate dynamics on quantum of agricultural exports. However, higher consumer prices have a negative effect on export quantities, emphasizing the importance of price competitiveness in international markets. The findings of this study provide valuable insights for diversifying the export basket, enhancing productivity, value addition, and sustainability. Addressing challenges related to trade imbalances and price competitiveness is crucial for driving growth in India's agricultural sector, benefiting farmers, the economy, and the nation as a whole. This study sheds light on the dynamics and determinants of agricultural exports from India, offering valuable insights for policymakers and stakeholders. By analyzing a panel dataset of 40 agricultural export items using the system Generalized Method of Moments (GMM) estimation, the study uncovers key factors influencing export performance. The findings underscore the importance of past export momentum, agricultural output, economic factors, water use efficiency, corruption levels, population size, and consumer prices in shaping agricultural exports. Importantly, the study highlights the need for diversifying India's agricultural export portfolio to mitigate risks associated with concentration on a few commodities, address trade imbalances, and enhance export competitiveness. The recommendations provided, including focusing on sustainable growth, enhancing productivity, value addition, trade policy reforms, attracting foreign investment, and investing in skill development, are crucial for fostering long-term growth and stability in India's agricultural export sector. Despite certain limitations, this study lays a solid foundation for future research and policy interventions aimed at ensuring sustainable development and resilience in India's agricultural exports.