Secular trends in physical growth, biological maturation, and intelligence in children and adolescents born between 1978 and 1993.

Introduction: Human physical growth, biological maturation, and intelligence have been documented as increasing for over 100 years. Comparing the timing of secular trends in these characteristics could provide insight into what underlies them. However, they have not been examined in parallel in the same cohort during different developmental phases. Thus, the aim of this study was to examine secular trends in body height, weight, and head circumference, biological maturation, and intelligence by assessing these traits concurrently at four points during development: the ages of 4, 9, 14, and 18 years. Methods: Data derived from growth measures, bone age as an indicator of biological maturation, and full-scale intelligence tests were drawn from 236 participants of the Zurich Longitudinal Studies born between 1978 and 1993. In addition, birth weight was analyzed as an indicator of prenatal conditions. Results: Secular trends for height and weight at 4 years were positive (0.35 SD increase per decade for height and an insignificant 0.27 SD increase per decade for weight) and remained similar at 9 and 14 years (height: 0.46 SD and 0.38 SD increase per decade; weight: 0.51 SD and 0.51 SD increase per decade, respectively) as well as for weight at age 18 years (0.36 SD increase per decade). In contrast, the secular trend in height was no longer evident at age 18 years (0.09 SD increase per decade). Secular trends for biological maturation at 14 years were similar to those of height and weight (0.54 SD increase per decade). At 18 years, the trend was non-significant (0.38 SD increase per decade). For intelligence, a positive secular trend was found at 4 years (0.54 SD increase per decade). In contrast, negative secular trends were observed at 9 years (0.54 SD decrease per decade) and 14 years (0.60 SD decrease per decade). No secular trend was observed at any of the four ages for head circumference (0.01, 0.24, 0.17, and - 0.04 SD increase per decade, respectively) and birth weight (0.01 SD decrease per decade). Discussion: The different patterns of changes in physical growth, biological maturation, and intelligence between 1978 and 1993 indicate that distinct mechanisms underlie these secular trends.

Irregular word reading as a marker of semantic decline in Alzheimer's disease: implications for premorbid intellectual ability measurement.

BACKGROUND: Irregular word reading has been used to estimate premorbid intelligence in Alzheimer's disease (AD) dementia. However, reading models highlight the core influence of semantic abilities on irregular word reading, which shows early decline in AD. The primary objective of this study is to ascertain whether irregular word reading serves as an indicator of cognitive and semantic decline in AD, potentially discouraging its use as a marker for premorbid intellectual abilities. METHOD: Six hundred eighty-one healthy controls (HC), 104 subjective cognitive decline, 290 early and 589 late mild cognitive impairment (EMCI, LMCI) and 348 AD participants from the Alzheimer's Disease Neuroimaging Initiative were included. Irregular word reading was assessed with the American National Adult Reading Test (AmNART). Multiple linear regressions were conducted predicting AmNART score using diagnostic category, general cognitive impairment and semantic tests. A generalized logistic mixed-effects model predicted correct reading using extracted psycholinguistic characteristics of each AmNART words. Deformation-based morphometry was used to assess the relationship between AmNART scores and voxel-wise brain volumes, as well as with the volume of a region of interest placed in the left anterior temporal lobe (ATL), a region implicated in semantic memory. RESULTS: EMCI, LMCI and AD patients made significantly more errors in reading irregular words compared to HC, and AD patients made more errors than all other groups. Across the AD continuum, as well as within each diagnostic group, irregular word reading was significantly correlated to measures of general cognitive impairment / dementia severity. Neuropsychological tests of lexicosemantics were moderately correlated to irregular word reading whilst executive functioning and episodic memory were respectively weakly and not correlated. Age of acquisition, a primarily semantic variable, had a strong effect on irregular word reading accuracy whilst none of the phonological variables significantly contributed. Neuroimaging analyses pointed to bilateral hippocampal and left ATL volume loss as the main contributors to decreased irregular word reading performances. CONCLUSIONS: While the AmNART may be appropriate to measure premorbid intellectual abilities in cognitively unimpaired individuals, our results suggest that it captures current semantic decline in MCI and AD patients and may therefore underestimate premorbid intelligence. On the other hand, irregular word reading tests might be clinically useful to detect semantic impairments in individuals on the AD continuum.

Prenatal exposure to per- and polyfluoroalkyl substances, fetal thyroid function, and intelligence quotient at 7 years of age: Findings from the Sheyang Mini Birth Cohort Study.

BACKGROUND: Prenatal exposure to per- and polyfluoroalkyl substances (PFASs) influences neurodevelopment. Thyroid homeostasis disruption is thought to be a possible underlying mechanism. However, current epidemiological evidence remains inconclusive. OBJECTIVES: This study aimed to explore the effects of prenatal PFAS exposure on the intelligence quotient (IQ) of school-aged children and assess the potential mediating role of fetal thyroid function. METHODS: The study included 327 7-year-old children from the Sheyang Mini Birth Cohort Study (SMBCS). Cord serum samples were analyzed for 12 PFAS concentrations and 5 thyroid hormone (TH) levels. IQ was assessed using the Wechsler Intelligence Scale for Children-Chinese Revised (WISC-CR). Generalized linear models (GLM) and Bayesian Kernel Machine Regression (BKMR) were used to evaluate the individual and combined effects of prenatal PFAS exposure on IQ. Additionally, the impact on fetal thyroid function was examined using a GLM, and a mediation analysis was conducted to explore the potential mediating roles of this function. RESULTS: The molar sum concentration of perfluorinated carboxylic acids (ΣPFCA) in cord serum was significantly negatively associated with the performance IQ (PIQ) of 7-year-old children (ß = -6.21, 95 % confidence interval [CI]: -12.21, -0.21), with more pronounced associations observed among girls (ß = -9.57, 95 % CI: -18.33, -0.81) than in boys. Negative, albeit non-significant, cumulative effects were noted when considering PFAS mixture exposure. Prenatal exposure to perfluorooctanoic acid, perfluorononanoic acid, and perfluorooctanesulfonic acid was positively associated with the total thyroxine/triiodothyronine ratio. However, no evidence supported the mediating role of thyroid function in the link between PFAS exposure and IQ. CONCLUSIONS: Increased prenatal exposure to PFASs negatively affected the IQ of school-aged children, whereas fetal thyroid function did not serve as a mediator in this relationship.

Infant Behaviors, Prenatal Cocaine Exposure, and Adult Intelligence.

Importance: Linking prenatal drug exposures to both infant behavior and adult cognitive outcomes may improve early interventions. Objective: To assess whether neonatal physical, neurobehavioral, and infant cognitive measures mediate the association between prenatal cocaine exposure (PCE) and adult perceptual reasoning IQ. Design, Setting, and Participants: This study used data from a longitudinal, prospective birth cohort study with follow-up from 1994 to 2018 until offspring were 21 years post partum. A total of 384 (196 PCE and 188 not exposed to cocaine [NCE]) infants and mothers were screened for cocaine or polydrug use. Structural equation modeling was performed from June to November 2023. Exposures: Prenatal exposures to cocaine, alcohol, marijuana, and tobacco assessed through urine and meconium analyses and maternal self-report. Main Outcomes and Measures: Head circumference, neurobehavioral assessment, Bayley Scales of Infant Development, Fagan Test of Infant Intelligence score, Wechsler Perceptual Reasoning IQ, Home Observation for Measurement of the Environment (HOME) score, and blood lead level. Results: Among the 384 mothers in the study, the mean (SD) age at delivery was 27.7 (5.3) years (range, 18-41 years), 375 of 383 received public assistance (97.9%) and 336 were unmarried (87.5%). Birth head circumference (standardized estimate for specific path association, -0.05, SE = 0.02; P = .02) and 1-year Bayley Mental Development Index (MDI) (standardized estimate for total of the specific path association, -0.05, SE = 0.02; P = .03) mediated the association of PCE with Wechsler Perceptual Reasoning IQ, controlling for HOME score and other substance exposures. Abnormal results on the neurobehavioral assessment were associated with birth head circumference (ß = -0.20, SE = 0.08; P = .01). Bayley Psychomotor Index (ß = 0.39, SE = 0.05; P < .001) and Fagan Test of Infant Intelligence score (ß = 0.16, SE = 0.06; P = .01) at 6.5 months correlated with MDI at 12 months. Conclusions and Relevance: In this cohort study, a negative association of PCE with adult perceptual reasoning IQ was mediated by early physical and behavioral differences, after controlling for other drug and environmental factors. Development of infant behavioral assessments to identify sequelae of prenatal teratogens early in life may improve long-term outcomes and public health awareness.

Thyroid function variations within the reference range and cognitive function: A two-sample Mendelian randomization study.

BACKGROUND: The causal relationship between thyroid function variations within the reference range and cognitive function remains unknown. We aimed to explore this causal relationship using a Mendelian randomization (MR) approach. METHODS: Summary statistics of a thyroid function genome-wide association study (GWAS) were obtained from the ThyroidOmics consortium, including reference range thyroid stimulating hormone (TSH) (N = 54,288) and reference range free thyroxine (FT4) (N = 49,269). GWAS summary statistics on cognitive function were obtained from the Social Science Genetic Association Consortium (SSGAC) and the UK Biobank, including cognitive performance (N = 257,841), prospective memory (N = 152,605), reaction time (N = 459,523), and fluid intelligence (N = 149,051). The primary method used was inverse-variance weighted (IVW), supplemented with weighted median, Mr-Egger regression, and MR-Pleiotropy Residual Sum and Outlier. Several sensitivity analyses were conducted to identify heterogeneity and pleiotropy. RESULTS: An increase in genetically associated TSH within the reference range was suggestively associated with a decline in cognitive performance (ß = -0.019; 95%CI: -0.034 to -0.003; P = 0.017) and significantly associated with longer reaction time (ß = 0.016; 95 % CI: 0.005 to 0.027; P = 0.004). Genetically associated FT4 levels within the reference range had a significant negative relationship with reaction time (ß = -0.030; 95%CI:-0.044 to -0.015; P = 4.85 × 10-5). These findings remained robust in the sensitivity analyses. CONCLUSIONS: Low thyroid function within the reference range may have a negative effect on cognitive function, but further research is needed to fully understand the nature of this relationship. LIMITATIONS: This study only used GWAS data from individuals of European descent, so the findings may not apply to other ethnic groups.

Predictability of intelligence and age from structural connectomes.

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

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.

SCB-YOLOv5: a lightweight intelligent detection model for athletes' normative movements.

Intelligent detection of athlete behavior is beneficial for guiding sports instruction. Existing mature target detection algorithms provide significant support for this task. However, large-scale target detection algorithms often encounter more challenges in practical application scenarios. We propose SCB-YOLOv5, to detect standardized movements of gymnasts. First, the movements of aerobics athletes were captured, labeled using the labelImg software, and utilized to establish the athlete normative behavior dataset, which was then enhanced by the dataset augmentation using Mosaic9. Then, we improved the YOLOv5 by (1) incorporating the structures of ShuffleNet V2 and convolutional block attention module to reconstruct the Backbone, effectively reducing the parameter size while maintaining network feature extraction capability; (2) adding a weighted bidirectional feature pyramid network into the multiscale feature fusion, to acquire precise channel and positional information through the global receptive field of feature maps. Finally, SCB-YOLOv5 was lighter by 56.9% than YOLOv5. The detection precision is 93.7%, with a recall of 99% and mAP value of 94.23%. This represents a 3.53% improvement compared to the original algorithm. Extensive experiments have verified that our method. SCB-YOLOv5 can meet the requirements for on-site athlete action detection. Our code and models are available at https://github.com/qingDu1/SCB-YOLOv5 .

Relationship between cultural intelligence and career and work adaptability in nursing students.

Objectives: To investigate the relationship between cultural intelligence and career and work adaptability among nursing students. METHODS: The descriptive, cross-sectional study was conducted at Kilis 7 Aralik University Nursing Department in Turkey from April to May 2019, and comprised nursing students of either gender. Data was gathered using Cultural Intelligence Scale and Career and Work Adaptability Questionnaire. Data was analysed using SPSS24. RESULTS: Of the 277 subjects, 162(58.5%) were females and 115(41.5%) were males. The overall mean age was 21.21±1.81 years. The mean Cultural Intelligence Scale score was 95.17±18.16. The mean Career and Work Adaptability Questionnaire score was 115.69±19.38. There was a positive correlation between the total scores and subscale scores of both the scales (r=598, p<0.001). The student's father's occupation, desire to work overseas, feeling like a good fit for nursing, and feeling prepared for professional life significantly affected cultural intelligence (p<0.05). The student's father's occupation significantly affected career and work adaptability (p=0.001). Conclusion: There was a positive correlation between the total scores and subscale scores of Cultural Intelligence Scale and Career and Work Adaptability Questionnaire.

Irrigation intelligence-enabling a cloud-based Internet of Things approach for enhanced water management in agriculture.

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

Towards secure and efficient integration of blockchain and 6G networks.

The future of communication systems is undergoing a transformative shift towards intelligence, efficiency, and flexibility. Presently, the amalgamation of blockchain technology and the sixth-generation mobile communication network (6G) has garnered significant attention, as their fusion is poised to profoundly impact the digital economy and society at large. However, the convergence of blockchain and 6G networks poses challenges pertaining to security and performance. In this article, we propose an approach based on the design of secure mechanisms and performance optimization to delve into the key issues surrounding the integration of blockchain and 6G networks from both security and performance perspectives. Specifically, we first introduce the application scenarios of 6G networks and blockchain's empowerment of them to highlight the necessity of combining blockchain technology with 6G. Subsequently, in order to ensure the security of communication and data transmission between blockchain and 6G networks, we have investigated the design requirements for security mechanisms. Furthermore, we discuss the efficient realization of the amalgamation between blockchain and 6G networks by proposing a solution based on Directed Acyclic Graph (DAG) for blockchain's asynchronous consensus protocol, alongside optimization strategies for storage and communication to meet the desired characteristics and requirements of 6G networks. Lastly, we provide valuable research directions that serve as references and guidance for the future development of the integration between blockchain and 6G networks.

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.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems.

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Short-term forecasting approach of single well production based on multi-intelligent agent hybrid model.

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.

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.

[Research practice and development direction of intelligent manufacturing of presonalized traditional Chinese medicine preparations].

In recent years, as people's living standards continue to improve, and the pace of life accelerates dramatically, the demand and quality of traditional Chinese medicine(TCM) services from patients continue to rise. As an essential supplement to the existing forms of TCM application, such as Chinese patent medicine, decoction, and formulated granules, presonalized TCM preparations is facing an increasing market demand. Currently, manual and semi-mechanized production are the primary production ways in presonalized TCM preparations. However, the production process control level is low, and digitalization and informatization need to be improved, which restricts the automated and intelligent development of presonalized TCM preparations. Presonalized TCM preparations faces a significant opportunity and challenge in integrating with intelligent manufacturing through research and development of intelligent equipment and core technology. This paper overviews the connotation and characteristics of intelligent manufacturing and summarizes the application of intelligent manufacturing technologies such as "Internet of things" "big data", and "artificial intelligence" in the TCM industry. Based on the innovative research and development model of "intelligent classification of TCM materials, intelligent decision making of prescription and process, and online control and intelligent production" of presonalized TCM preparations, the research practice and achievements from our research group in the field of intelligent manufacturing of presonalized TCM preparations are introduced. Ultimately, the paper proposes the direction for developing intelligent manufacturing of presonalized TCM preparations, which will provide a reference for the research and application of automation and intelligence of presonalized TCM preparations.

Understanding the limits to animal cognition.

The thriving field of comparative cognition examines the behaviour of diverse animals in cognitive terms. Comparative cognition research has primarily focused on the abilities of animals - what tasks they can do - rather than on the limits of their cognition - tasks that exceed an animal's cognitive abilities. We propose that understanding and identifying cognitive limits is as important as demonstrating the capacities of animal minds. Here, we identify challenges that have deterred the study of cognitive limits related to epistemic, practical and publication problems. The epistemic problem is concerned with how we can confidently infer a cognitive limit from null or negative results. The practical problem is how can we be certain our research has identified a cognitive limit rather than failures in tasks due to methodological or experimental design issues. The publication problem outlines the publication bias toward positive and exciting results over negative or null results in animal cognition. We propose solutions to these three challenges and examples of how to conduct research to confidently identify and confirm cognitive limits in animals. We believe a refocus on the cognitive limits of animals is the next step in the field of comparative cognition. Knowing the limits to the intelligence of different animals will aid us in appreciating the diversity of animal intelligence, and will resolve outstanding questions of how cognition evolves.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

Intelligence and its effects on environmental decline: A worldwide analysis.

The research investigates the relationship between intelligence quotient (IQ) and environmental degradation, aiming to understand how cognitive abilities influence environmental outcomes across different nations and time periods. The objective is to examine the impact of intelligence quotient (IQ) on environmental indicators such as carbon emissions, ecological demand, and the Environmental Kuznets Curve (EKC), seeking insights to inform environmental policy and stewardship. The study utilizes statistical techniques including Ordinary Least Squares (OLS), Two Stage Least Squares (2SLS), and Iteratively Weighted Least Squares (IWLS) to analyze data from 147 nations over the years 2000-2017. These methods are applied to explore the relationship between IQ and environmental metrics while considering other relevant variables. The findings reveal unexpected positive associations between human intelligence quotient and carbon emissions, as well as ecological demand, challenging conventional notions of "delay discounting." Additionally, variations in the Environmental Kuznets Curve (EKC) hypothesis are identified across different pollutants, highlighting the roles of governance and international commitments in mitigating emissions. The study concludes by advocating for the adoption of a "delay discounting culture" to address environmental challenges effectively. It underscores the complex interactions between intelligence, governance, and population dynamics in shaping environmental outcomes, emphasizing the need for targeted policies to achieve sustainability objectives.

Impacts of decision support systems on cognition and performance for intelligence-gathering path planning.

Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload.