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 .

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.

Research on the Vision-Based Dairy Cow Ear Tag Recognition Method.

With the increase in the scale of breeding at modern pastures, the management of dairy cows has become much more challenging, and individual recognition is the key to the implementation of precision farming. Based on the need for low-cost and accurate herd management and for non-stressful and non-invasive individual recognition, we propose a vision-based automatic recognition method for dairy cow ear tags. Firstly, for the detection of cow ear tags, the lightweight Small-YOLOV5s is proposed, and then a differentiable binarization network (DBNet) combined with a convolutional recurrent neural network (CRNN) is used to achieve the recognition of the numbers on ear tags. The experimental results demonstrated notable improvements: Compared to those of YOLOV5s, Small-YOLOV5s enhanced recall by 1.5%, increased the mean average precision by 0.9%, reduced the number of model parameters by 5,447,802, and enhanced the average prediction speed for a single image by 0.5 ms. The final accuracy of the ear tag number recognition was an impressive 92.1%. Moreover, this study introduces two standardized experimental datasets specifically designed for the ear tag detection and recognition of dairy cows. These datasets will be made freely available to researchers in the global dairy cattle community with the intention of fostering intelligent advancements in the breeding industry.

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.

[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.

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.

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.

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.

Psychometric Properties of the Spanish Clance Impostor Scale (S-CIPS) / Propiedades psicométricas de la adaptación española de la Clance Impostor Phenomenon Scale (S-CIPS)

El objetivo del presente estudio fue el de examinar la fiabilidad, validez y estructura factorial de la adaptación española de la Clance Impostor Phenomenon Scale (CIPS). Para ello, un total de 271 estudiantes españoles completaron una versión traducida de la escala original de 20 ítems. En nuestra muestra, el instrumento mostró una alta fiabilidad, medida como consistencia interna, (ωTotal =.90) y correlaciones moderadas-altas con medidas de depresión (r =.633), autoestima (r = -.754) y miedo a las evaluaciones negativas (r = .666), lo cual sugiere tanto una validez nomológica como discriminante. Aunque en la validación original se propuso una estructura de tres factores, otros estudios han encontrado ajuste a estructuras de uno y dos factores. Aquí, utilizamos un análisis factorial confirmatorio (AFC) para probar el ajuste de estos tres modelos. Nuestros resultados muestran que, en la adaptación a español, el modelo con dos factores es el preferido. Esta adaptación al español de la CIPS provee a los profesionales clínicos una de una nueva herramienta para poder investigar los mecanismos que subyacen al síndrome del impostor, así como futuros tratamientos.(AU)

The aim of this study was to examine the reliability, validity, and factorial structure of the Spanish version of the Clance Impostor Phenom-enon Scale (CIPS). A sample of 271 Spanish students was recruited to complete a translated version of the original 20-item CIPS. In our sample, the instrument showed high internal consistency reliability (ωTotal=.90) and a moderate-to-strong correlation with measures of depression (r= .633), self-esteem (r= -.754) and fear of negative evaluation (r= .666), suggesting both nomological and discriminant validity. Althoughthe original valida-tion of the CIPS proposed a factorial structure with three factors, subse-quent validations also revealed adjustment to two-and one-factor struc-tures. Here, we used confirmatory factor analysis (CFA) to test the three different models. The results showed that in our adaptation, a 2-factor structure might be preferred. This adaptation of the CIPS to Spanish pro-vides clinicians with a new method to gain insight into the psychological mechanisms behind the Impostor phenomenon and suitable treatments.(AU)

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.

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.

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.

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.

Influencing factors and countermeasures on intelligent transformation and upgrading of logistics firms: A case study in China.

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward.

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.

Research on the key technology of high-precision waveform synchronization identification and measurement.

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning.

In the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing. Specifically, the task of fruit detection and counting in orchards represents a complex issue that is crucial for efficient orchard management and harvest preparation. Traditional techniques often fail to provide the timely and precise data necessary for these tasks. With the agricultural sector increasingly relying on technological advancements, the integration of innovative solutions is essential. This study presents a novel approach that combines artificial intelligence (AI), deep learning (DL), and unmanned aerial vehicles (UAVs). The proposed approach demonstrates superior real-time capabilities in fruit detection and counting, utilizing a combination of AI techniques and multi-UAV systems. The core innovation of this approach is its ability to simultaneously capture and synchronize video frames from multiple UAV cameras, converting them into a cohesive data structure and, ultimately, a continuous image. This integration is further enhanced by image quality optimization techniques, ensuring the high-resolution and accurate detection of targeted objects during UAV operations. Its effectiveness is proven by experiments, achieving a high mean average precision rate of 86.8% in fruit detection and counting, which surpasses existing technologies. Additionally, it maintains low average error rates, with a false positive rate at 14.7% and a false negative rate at 18.3%, even under challenging weather conditions like cloudiness. Overall, the practical implications of this multi-UAV imaging and DL-based approach are vast, particularly for real-time fruit recognition in orchards, marking a significant stride forward in the realm of digital agriculture that aligns with the objectives of Industry 4.0.

Studying the relationship between intelligence quotient and schizophrenia polygenic scores in a family design with first-episode psychosis population.

BACKGROUND: The intelligence quotient (IQ) of patients with first-episode psychosis (FEP) and their unaffected relatives may be related to the genetic burden of schizophrenia (SCZ). The polygenic score approach can be useful for testing this question. AIM: To assess the contribution of the polygenic risk scores for SCZ (PGS-SCZ) and polygenic scores for IQ (PGS-IQ) to the individual IQ and its difference from the mean IQ of the family (named family-IQ) through a family-based design in an FEP sample. METHODS: The PAFIP-FAMILIES sample (Spain) consists of 122 FEP patients, 131 parents, 94 siblings, and 176 controls. They all completed the WAIS Vocabulary subtest for IQ estimation and provided a DNA sample. We calculated PGS-SCZ and PGS-IQ using the continuous shrinkage method. To account for relatedness in our sample, we performed linear mixed models. We controlled for covariates potentially related to IQ, including age, years of education, sex, and ancestry principal components. RESULTS: FEP patients significantly deviated from their family-IQ. FEP patients had higher PGS-SCZ than other groups, whereas the relatives had intermediate scores between patients and controls. PGS-IQ did not differ between groups. PGS-SCZ significantly predicted the deviation from family-IQ, whereas PGS-IQ significantly predicted individual IQ. CONCLUSIONS: PGS-SCZ discriminated between different levels of genetic risk for the disorder and was specifically related to patients' lower IQ in relation to family-IQ. The genetic background of the disorder may affect neurocognition through complex pathological processes interacting with environmental factors that prevent the individual from reaching their familial cognitive potential.