Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance.

BACKGROUND: European epidemic intelligence (EI) systems receive vast amounts of information and data on disease outbreaks and potential health threats. The quantity and variety of available data sources for EI, as well as the available methods to manage and analyse these data sources, are constantly increasing. Our aim was to identify the difficulties encountered in this context and which innovations, according to EI practitioners, could improve the detection, monitoring and analysis of disease outbreaks and the emergence of new pathogens. METHODS: We conducted a qualitative study to identify the need for innovation expressed by 33 EI practitioners of national public health and animal health agencies in five European countries and at the European Centre for Disease Prevention and Control (ECDC). We adopted a stepwise approach to identify the EI stakeholders, to understand the problems they faced concerning their EI activities, and to validate and further define with practitioners the problems to address and the most adapted solutions to their work conditions. We characterized their EI activities, professional logics, and desired changes in their activities using NvivoⓇ software. RESULTS: Our analysis highlights that EI practitioners wished to collectively review their EI strategy to enhance their preparedness for emerging infectious diseases, adapt their routines to manage an increasing amount of data and have methodological support for cross-sectoral analysis. Practitioners were in demand of timely, validated and standardized data acquisition processes by text mining of various sources; better validated dataflows respecting the data protection rules; and more interoperable data with homogeneous quality levels and standardized covariate sets for epidemiological assessments of national EI. The set of solutions identified to facilitate risk detection and risk assessment included visualization, text mining, and predefined analytical tools combined with methodological guidance. Practitioners also highlighted their preference for partial rather than full automation of analyses to maintain control over the data and inputs and to adapt parameters to versatile objectives and characteristics. CONCLUSIONS: The study showed that the set of solutions needed by practitioners had to be based on holistic and integrated approaches for monitoring zoonosis and antimicrobial resistance and on harmonization between agencies and sectors while maintaining flexibility in the choice of tools and methods. The technical requirements should be defined in detail by iterative exchanges with EI practitioners and decision-makers.

Design of a multi-category drug information integration platform for intelligent pharmacy management: A needs analysis study.

A drug store was never just an area to fill personal solution. Patients considered drug specialists to be counsels, somebody who could help them pick an over-the-counter treatment or understanding the portion and directions for a solution. Drug stores, similar to the remainder of the medical services business, are going through changes. Nowadays, one of the main highlights of any structure is the board. The executives give the refinement needed to wrap up any responsibility in a particular way. The executive framework of a drug store can be utilized to deal with most drug store related errands. This report has provided data on the best way to fabricate and execute a Pharmacy Management System. The primary objective of this system is to expand exactness, just as security and proficiency, in the drug shop. This undertaking is focused on the drug store area, determined to offer engaging and reasonable programming answers to assist them with modernizing to rival shops (helping out other equal modules in a similar examination program). This study will clarify the system's thoughts concerning the board issues and arrangements of a drug store. Likewise, this study covers the main parts of the Pharmacy application's investigation, execution, and look.

ChatGPT: Literate or intelligent about UN sustainable development goals?

Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children.

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Childhood is a critical period for brain development. Difficult experiences during this developmental phase may contribute to reduced intelligence and poorer mental health later in life. Genetics and environmental factors also play roles. For example, having family support or a higher family income has been linked to better brain health outcomes for children. Delusions or hallucinations, or other psychotic-like experiences during childhood, are linked with poor mental health later in life. Children who experience psychotic-like episodes between the ages of nine and eleven have a higher risk of developing schizophrenia or related conditions. Environmental circumstances during childhood also appear to play a crucial role in shaping the risk of schizophrenia or related conditions. Park, Lee et al. show that positive parenting and supportive school and neighborhood environments boost child intelligence and mental health. In the experiments, Park, Lee et al. analyzed data on 6,602 children to determine how genetics and environmental factors shaped their intelligence and mental health. The models show that children with higher intelligence have a lower risk of psychosis. Both genetics and supportive environments contribute to higher intelligence. Complex interactions between biology and social factors shape children's intelligence and mental health. Beneficial genetics and coming from a family with more financial resources are helpful. Yet, social environments, such as having parents who use positive child-rearing practices, or having supportive schools or neighborhoods, have protective effects that can offset other disadvantages. Policies that help parents, encourage supportive school environments, and strengthen neighborhoods may boost children's intelligence and mental health later in life.

Hidden Markov Models based intelligent health assessment and fault diagnosis of rolling element bearings.

Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.

Health system performance assessment: embedding resilience through performance intelligence.

Health systems around the world are facing challenges in achieving their goals. In the wake of the coronavirus disease pandemic, the need for resilient health systems has become even more apparent. This article argues that embedding resilience into health system performance assessment (HSPA) frameworks can be a valuable approach for improving health system performance. This perspective examines key challenges threatening health systems and makes a case for the continued relevance of HSPA by embedding resilience-related performance intelligence.

What maternal educational mobility tells us about the mother's parenting routines, offspring school achievement and intelligence.

BACKGROUND: Educational mobility at the macro-level is a common measure of social inequality. Nonetheless, the correlates of mobility of education at the individual level are less well studied. We evaluated whether educational mobility of the second generation (compared to the first generation level) predicts differences in parenting practices of the second generation and school achievement and intelligence in the third generation. METHODS: Data from a population-based cohort of children in the Netherlands (N = 3547; 49.4% boys) were analyzed. Maternal, grandparental education and family routines, a parenting practice, were reported by the mother. Child school achievement at the end of primary school (∼12 years, with the national Dutch academic test score) and child intelligence (∼6 and 13 years) were measured in a standardized manner. Also, a child genome-wide polygenic score of academic attainment was calculated. To estimate the effect of educational mobility, inverse probability-weighted linear models and Diagonal Reference Models (DRM) were used. RESULTS: Upward maternal educational mobility was associated with better offspring school achievement, higher intelligence, and more family routines if compared to offspring of mothers with no upward mobility. However, mothers did not implement the same level of family routines as similarly educated mothers and grandfathers who already had achieved this educational level. Likewise, children of mothers with upward educational mobility had lower school achievement and intelligence than children of similarly educated mothers with no mobility. Child's genetic potential for education followed a similar association pattern with higher potential in children of upward mobile mothers. CONCLUSION: Policymakers might overlook social inequalities when focused on parental socioeconomic status. Grandparental socioeconomic status, which independently predicts child school achievement, intelligence, and parental family routines, should also be assessed. The child's genetic endowment reflects the propensity for education across generations that partly underlies mobility and some of its effect on the offspring.

Designing and psychometric assessment of the moral intelligence scale for healthcare professionals.

The moral intelligence of healthcare professionals in the cardiac operating room is one of the most important aspects of professional competence. However, moral intelligence is an abstract and multidimensional concept that needs to be clarified and described based on organizational culture and environment. Therefore, there is a need to design a specific scale for measuring the moral intelligence of healthcare professionals in the cardiac operating room. This study aims to design and assess the psychometric properties of a moral intelligence scale for healthcare professionals in the cardiac operating room. The present study was a mixed method study with a sequential exploratory approach. The research was conducted in 2023-2024 in Iran. The first phase data were collected from 20 healthcare professionals and were analyzed by conventional content analysis method. In the second phase, the validity and reliability of the instrument were evaluated by involving 300 healthcare professionals in the cardiac operating room. The moral intelligence of health care professionals in the cardiac operating room was defined as moral sensitivity combined with moral commitment and moral courage for the provision of quality care that respects the principles of medical ethics. After deducing the conceptual framework, the moral intelligence scale for healthcare professionals in the cardiac operating room was developed with three dimensions: "moral sensitivity," "moral commitment," and "moral courage." 11 items were removed during testing to ensure content validity. Face validity was confirmed with impact scores > 1.5 for all items. A scale was developed through factor analysis with three factors that accounted for 73.04% of the observed variance. The instrument's reliability using Cronbach's alpha coefficient calculation was reported as 0.94 for the entire instrument. The testretest showed no statistically significant difference between the pre and post-test scores of moral intelligence (p = 0.51). The moral intelligence scale demonstrated acceptable psychometric properties. The moral intelligence scale for health care professionals in the cardiac operating room demonstrated acceptable psychometric properties. This instrument may serve to assess the moral intelligence of healthcare professionals and determine the need for educational interventions to reduce the ethical challenges and improve the moral intelligence of this healthcare.

When tomorrow comes: A prospective risk assessment of a future artificial general intelligence-based uncrewed combat aerial vehicle system.

There are concerns that Artificial General Intelligence (AGI) could pose an existential threat to humanity; however, as AGI does not yet exist it is difficult to prospectively identify risks and develop requisite controls. We applied the Work Domain Analysis Broken Nodes (WDA-BN) and Event Analysis of Systemic Teamwork-Broken Links (EAST-BL) methods to identify potential risks in a future 'envisioned world' AGI-based uncrewed combat aerial vehicle system. The findings suggest five main categories of risk in this context: sub-optimal performance risks, goal alignment risks, super-intelligence risks, over-control risks, and enfeeblement risks. Two of these categories, goal alignment risks and super-intelligence risks, have not previously been encountered or dealt with in conventional safety management systems. Whereas most of the identified sub-optimal performance risks can be managed through existing defence design lifecycle processes, we propose that work is required to develop controls to manage the other risks identified. These include controls on AGI developers, controls within the AGI itself, and broader sociotechnical system controls.

An intelligent agriculture management system for rainfall prediction and fruit health monitoring.

Contrary to popular belief, agriculture is becoming more data-driven with artificial intelligence and Internet-of-Things (IoT) playing crucial roles. In this paper, the integrated processing executed by various sensors combined as an IoT pack and driving an intelligent agriculture management system designed for rainfall prediction and fruit health monitoring have been included. The proposed system based on an AI aided model makes use of a Convolutional Neural Network (CNN) with long short-term memory (LSTM) layer for rainfall prediction and a CNN with SoftMax layer along with a few deep learning pre-trained models for fruit health monitoring. Another model that works as a combined rainfall predictor and fruit health recognizer is designed using a CNN + LSTM and a multi-head self-attention mechanism which proves to be effective. The entire system is cloud resident and available for use through an application.

Assessment of psychopathy among justice-involved adult males with low versus average intelligence: Differential links to violent offending.

Psychopathic personality is a multidimensional construct (De Brito et al., 2021) and the dimensions have differential associations with general and violent offending. Impairment in cognitive functioning, particularly intelligence (IQ), is another construct linked to both general and violent offending. However, the evidence is mixed on whether the combination of elevated psychopathy and low IQ increases the risk for violent offending (Hampton et al., 2014; Heilbrun, 1982; Walsh et al., 2004). Also, before this interaction can be firmly established, assessment of whether psychopathic traits are equivalent among individuals with different levels of IQ, especially those in the justice system, is needed. Using multiple-group confirmatory factor analysis (MG-CFA), this study of justice-involved adult males tested whether Psychopathy Checklist-Revised (PCL-R; Hare, 2003) item parameters were invariant among those with low (< 85) versus average IQ (≥ 85). In addition, moderated nonlinear factor analysis was conducted using continuous IQ scores to test for its effect on a range of model parameters. Both approaches provided evidence of measurement invariance. Adding criminal offenses to the MG-CFA revealed differential associations of the psychopathy dimensions with violent offending. Finally, analysis of variance results suggested an interaction between psychopathy status and IQ level-that is, those meeting diagnostic criterion for psychopathy with low IQ had the highest number of violent offenses. This study provides evidence of measurement invariance for the PCL-R among justice-involved persons with varying levels IQ and helps to extend research on the dynamic associations between psychopathy, IQ, and violent behavior. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Review of Manufacturing Methods for Flexible Devices and Energy Storage Devices.

Given the advancements in modern living standards and technological development, conventional smart devices have proven inadequate in meeting the demands for a high-quality lifestyle. Therefore, a revolution is necessary to overcome this impasse and facilitate the emergence of flexible electronics. Specifically, there is a growing focus on health detection, necessitating advanced flexible preparation technology for biosensor-based smart wearable devices. Nowadays, numerous flexible products are available on the market, such as electronic devices with flexible connections, bendable LED light arrays, and flexible radio frequency electronic tags for storing information. The manufacturing process of these devices is relatively straightforward, and their integration is uncomplicated. However, their functionality remains limited. Further research is necessary for the development of more intricate applications, such as intelligent wearables and energy storage systems. Taking smart wear as an example, it is worth noting that the current mainstream products on the market primarily consist of bracelet-type health testing equipment. They exhibit limited flexibility and can only be worn on the wrist for measurement purposes, which greatly limits their application diversity. Flexible energy storage and flexible display also face the same problem, so there is still a lot of room for development in the field of flexible electronics manufacturing. In this review, we provide a brief overview of the developmental history of flexible devices, systematically summarizing representative preparation methods and typical applications, identifying challenges, proposing solutions, and offering prospects for future development.

The Godspeed Questionnaire Series in the Assessment of the Social Robot TIAGo by Older Individuals.

(1) Background: A robot in care for older adults requires solid research confirming its acceptance. The aim of this study was to present the Polish version of the Godspeed Questionnaire Series (GQS) and assess the perception of the social robot TIAGo; (2) Methods: The study involved older individuals living in the community and care homes and measured perception after interaction with TIAGo using five series of GQS (S1: Anthropomorphism, S2: Animacy, S3: Likeability, S4: Perceived intelligence, and S5: Perceived safety); (3) Results: We studied 178 individuals (age: 75.2 ± 9.6 years, 103 women). Good internal consistency was found. Cronbach's Alpha was 0.90 for the entire tool (from 0.75 to 0.94 for the individual series). Mean scores for S1 and S2 were comparable but lower than all others (p < 0.001). Average scores for S3 and S4 did not differ but were higher than those of S5. Age, gender and education did not impact the answers, as did the ease of use of technology and self-assessment of independence. Solely, the place of residence influenced the results of S3 and S5; people living in institutions scored higher (p < 0.05 and p < 0.001, respectively); (4) Conclusions: Acceptance does not go hand in hand with the perception of anthropomorphism and animacy.

Association Between Neighborhood Deprivation and Child Cognition in Clinically Referred Youth.

OBJECTIVE: When socioeconomic status is measured at the individual and/or family level, it has long been associated with cognition in children. However, the association between neighborhood deprivation, an index of community-level socioeconomic status, and child cognition is not fully understood. The goal of this study was to investigate (1) the relationship between neighborhood deprivation and child cognitive functioning and (2) whether child age moderates the relationship between cognitive functioning and neighborhood deprivation. METHODS: This study included 9878 children, ages 3 through 17 years (M = 10.4 yrs, SD = 3.4 yrs). Data were gathered from children referred for and evaluated at an urban, outpatient neuropsychology assessment clinic between 2006 and 2022, located in the Mid-Atlantic region of the United States. Neighborhood socioeconomic status was measured at the census block level using the Area Deprivation Index composite. RESULTS: There was a 20-point median difference in overall intelligence between the neighborhoods with the lowest and highest levels of deprivation. Overall intelligence and verbal comprehension, compared with working memory, fluid reasoning, and processing speed, demonstrated the strongest negative association with neighborhood deprivation (all p < 0.05). Older children had lower overall intelligence scores compared with younger children in neighborhoods with high levels of deprivation ( p < 0.01), suggesting a cumulative influence of poverty exposure. CONCLUSION: This study demonstrates the stark disparities in child cognitive functioning across levels of neighborhood deprivation. Findings support the importance of access to early interventions and services that promote intellectual growth and verbal capacity among children who live in neighborhoods with great deprivation.

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Study on the influence of industrial intelligence on carbon emission efficiency-empirical analysis of China's Yangtze River Economic Belt.

How to achieve the goal of "carbon peak and carbon neutrality" and explore the compatibility of industrial and ecological civilization is a major challenge for China today. This study analyzes the impact of industrial intelligence on industrial carbon emissions efficiency in 11 provinces of China's Yangtze River Economic Belt, measuring the efficiency of industrial carbon emissions through the non-expected output slacks-based measure (SBM) model, selecting industrial robot penetration to measure the level of industrial intelligence, establishing a two-way fixed model to verify the impact of industrial intelligence on carbon emission efficiency, and testing for intermediary effects and regional heterogeneity. The results show that: (1) the industrial carbon emission efficiency of the 11 provinces shows year-over-year improvement, with significant differences between upstream, midstream, and downstream, where downstream is the highest and upstream is the lowest. (2) The development of industrial intelligence is highly uneven, with the upstream level being the weakest. (3) Industrial intelligence can improve the efficiency of industrial carbon emissions by enhancing green technological innovation and energy use efficiency. (4) The effect of industrial intelligence on industrial carbon emission efficiency also shows regional heterogeneity. Finally, we present policy recommendations. This research provides mathematical and scientific support for achieving carbon reduction targets at an early stage and helps accelerate the construction of a modern, low-carbon China.

Assessment of the corpus callosum size in male individuals with high intelligence quotient (members of Mensa International).

OBJECTIVES: The aim of this study was to assess the size of the corpus callosum in members of Mensa International, which is the world's largest and oldest high-intelligence quotient (IQ) society. METHODS: We performed T2-weighted magnetic resonance imaging (Repetition Time, TR = 3200 ms, Time of Echo, TE = 409 ms) to examine the brain of members of Mensa International (Polish national group) in order to assess the size of the corpus callosum. Results from 113 male MENSA members and 96 controls in the age range of 21-40 years were analyzed. RESULTS: The comparative analysis showed that the mean length of the corpus callosum and the thickness of the isthmus were significantly greater in the Mensa members compared to the control groups. A statistically significant difference was also identified in the largest linear dimension of the brain from the frontal lobe to the occipital lobe. The mean corpus callosum cross-sectional area and its ratio to the brain area were significantly greater in the Mensa members. CONCLUSIONS: The results show that the dimensions (linear measures and midsagittal cross-sectional surface area) of the corpus callosum were significantly greater in the group of Mensa members than in the controls.

Does pre-school developmental assessment agree with later intellectual assessment? A retrospective cohort study.

AIM: This study sought to assess the association between early developmental assessment of toddlers with idiopathic global developmental delay (GDD) and their later intelligence test scores. METHODS: Toddlers with idiopathic GDD attending a community clinic over a 6-year period were assessed initially using the Griffiths Mental Development Scales - Extended Revised version (GMDS-ER) and later completing formal intelligence testing using the Stanford-Binet Intelligence Scale - 5th Edition (SB5) at age 4-6 years. Spearman's correlation was used to assess the association of quotient scores across the tools. The composite quotient (GQ) and the subscale quotients of GMDS-ER were correlated with the full-scale IQ (FSIQ), verbal and non-verbal IQ scores from the SB5. RESULTS: Thirty of 153 children assessed at the clinic were eligible for the study. The correlation between GMDS-ER GQ and later SB5 FSIQ was strong (r = 0.86, P < 0.001). The subscales' associations were moderate to strong (0.48-0.71). Eighty-six percent (86%) of children with delay on GMDS-ER GQ were found later to be in the impaired category based on the FSIQ of the SB5. CONCLUSION: There was a strong association between toddlers' early developmental quotients and later IQ scores for children with idiopathic GDD, though agreement between early GDD diagnoses and later intellectual disability is not absolute. Individualised care is needed around prognostic advice and recommendations to caregivers and families in the early years, so they may effectively plan for interventions, supports and later reassessment to optimise their child's development and learning.

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition.

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions.