ABSTRACT This study integrates embodied intelligence (EI) with a two‐stage two‐sided Hotelling duopoly model to reveal how physical intelligence reshapes digital platform equilibrium in intelligent logistics. By embedding EI‐driven efficiency parameters into market cost functions, the model demonstrates that improved perception and coordination reduce the effective transportation cost and transform pricing dynamics between competing platforms. Experiments in a digital twin warehouse show that when EI strength η increases from 0 to 0.6, throughput rises by 37.5%, Dock‐to‐Stock time decreases by 30.9%, and unit energy consumption drops by 7%–8%, verifying that EI directly enhances operational and economic efficiency. Further analysis confirms that asymmetric advantages in action or data lead to discriminatory pricing as the optimal strategy. Complementary encryption experiments indicate that lightweight security algorithms such as SHA‐1/SHA‐256 add less than 3% latency overhead, maintaining real‐time performance.
Electromagnetic field (EMF) exposure mapping is increasingly important for ensuring compliance with safety regulations, supporting the deployment of next-generation wireless networks, and addressing public health concerns. While numerous surveys have addressed specific aspects of radio propagation or radio environment maps, a comprehensive and unified overview of EMF mapping methodologies has been lacking. This review bridges that gap by systematically analyzing computational, geospatial, and machine learning approaches used for EMF exposure mapping across both wireless communication engineering and public health domains. A novel taxonomy is introduced to clarify overlapping terminology—encompassing radio maps, radio environment maps, and EMF exposure maps—and to classify construction methods, including analytical models, model-based interpolation, and data-driven learning techniques. In addition, the review highlights domain-specific challenges such as indoor versus outdoor mapping, data sparsity, and model generalization, while identifying emerging opportunities in hybrid modeling, big data integration, and explainable AI. By combining perspectives from communication engineering and public health, this work provides a broader and more interdisciplinary synthesis than previous surveys, offering a structured reference and roadmap for advancing robust, scalable, and socially relevant EMF mapping frameworks.
Segmentation of retinal vessels from fundus images is critical for diagnosing diseases such as diabetes and hypertension. However, the inherent challenges posed by the complex geometries of vessels and the highly imbalanced distribution of thick versus thin vessel pixels demand innovative solutions for robust feature extraction. In this paper, we introduce DAF-UNet, a novel architecture that integrates advanced modules to address these challenges. Specifically, our method leverages a pre-trained deformable convolution (DC) module within the encoder to dynamically adjust the sampling positions of the convolution kernel, thereby adapting the receptive field to capture irregular vessel morphologies more effectively than traditional convolutional approaches. At the network’s bottleneck, an enhanced atrous spatial pyramid pooling (ASPP) module is employed to extract and fuse rich, multi-scale contextual information, significantly improving the model’s capacity to delineate vessels of varying calibers. Furthermore, we propose a hybrid loss function that combines pixel-level and segment-level losses to robustly address the segmentation inconsistencies caused by the disparity in vessel thickness. Experimental evaluations on the DRIVE and CHASE_DB1 datasets demonstrated that DAF-UNet achieved a global accuracy of 0.9572/0.9632 and a Dice score of 0.8298/0.8227, respectively, outperforming state-of-the-art methods. These results underscore the efficacy of our approach in precisely capturing fine vascular details and complex boundaries, marking a significant advancement in retinal vessel segmentation.
Elinor Randi Schoenfeld, Tracy Trimboli, Kaylyn Schwartz
et al.
By 2050, most adults aged 65 and older in the United States will want to age independently at home, a goal that will strain healthcare resources. Adults aged 50 and older (N = 112) were recruited for study participation between 2018 and 2022. They completed surveys and participated in discussion sessions to explore their needs and opinions regarding smart home sensors. Survey results indicated that older adults’ comfort with smart home sensors increased with their perceived need for monitoring when home alone (OR = 1.46; <i>p</i> = 0.012) or sick/recovering from an illness (OR = 2.21; <i>p</i> < 0.001). When sick compared to when healthy, individuals were 2.65 times more likely to prefer installing multiple sensors in the living room, 1.75 times more likely in the kitchen, 3.66 times more likely in the bedroom, and 3.41 times more likely in the bathroom (<i>p</i> < 0.05). Regarding data sharing, participants were most willing to share information with healthcare providers and family members on a regular basis (80 and 81%, respectively) and 71% on a regular basis or when sick/recovering. Comfort with data sharing with professional caregivers (OR = 1.67; <i>p</i> = 0.0017) and monitoring companies (OR = 1.34; <i>p</i> = 0.030) significantly increased when sick/recovering. Discussion sessions highlighted overwhelming concerns about personal security/privacy, loss of independence, and ethical issues in data collection. Participants emphasized the need for new systems to be flexible, cost-effective, user-friendly, and respectful of user autonomy, accommodating diverse life stages, comfort levels, home environments, income levels, and support structures. Insights are now informing sensor data collection in our model home. Study findings underscore the importance of involving potential users in technology development to create effective and acceptable solutions for aging in place.
Social network provides a medium for information dissemination,leading to the rapid development of public opinion.Controlling the development direction of public opinion is one of the core issues of public opinion dynamics.However,the public opinion dynamics model mainly studies the way of updating the opinions of the subject so as to deduce the law of public opinion evolution.This paper classifies the current public opinion dynamics models,analyzes their advantages and disadvantages,and their applications in different fields,and summarizes the future research direction of public opinion dynamics.It is helpful to understand the law of the evolution of public opinion,so as to provide better guidance for the government and other institutions to control the direction of public opinion.
Joost C. F. de Winter, Dimitra Dodou, Yke Bauke Eisma
The processes underlying human cognition are often divided into System 1, which involves fast, intuitive thinking, and System 2, which involves slow, deliberate reasoning. Previously, large language models were criticized for lacking the deeper, more analytical capabilities of System 2. In September 2024, OpenAI introduced the <i>o1</i> model series, designed to handle System 2-like reasoning. While OpenAI’s benchmarks are promising, independent validation is still needed. In this study, we tested the <i>o1-preview</i> model twice on the Dutch ‘Mathematics B’ final exam. It scored a near-perfect 76 and 74 out of 76 points. For context, only 24 out of 16,414 students in the Netherlands achieved a perfect score. By comparison, the <i>GPT-4o</i> model scored 66 and 62 out of 76, well above the Dutch students’ average of 40.63 points. Neither model had access to the exam figures. Since there was a risk of model contamination (i.e., the knowledge cutoff for <i>o1-preview</i> and <i>GPT-4o</i> was after the exam was published online), we repeated the procedure with a new Mathematics B exam that was published after the cutoff date. The results again indicated that <i>o1-preview</i> performed strongly (97.8th percentile), which suggests that contamination was not a factor. We also show that there is some variability in the output of <i>o1-preview</i>, which means that sometimes there is ‘luck’ (the answer is correct) or ‘bad luck’ (the output has diverged into something that is incorrect). We demonstrate that the self-consistency approach, where repeated prompts are given and the most common answer is selected, is a useful strategy for identifying the correct answer. It is concluded that while OpenAI’s new model series holds great potential, certain risks must be considered.
The growing number of vehicles on the roads has resulted in several challenges, including increased accident rates, fuel consumption, pollution, travel time, and driving stress. However, recent advancements in intelligent vehicle technologies, such as sensors and communication networks, have the potential to revolutionize road traffic and address these challenges. In particular, the concept of platooning for autonomous vehicles, where they travel in groups at high speeds with minimal distances between them, has been proposed to enhance the efficiency of road traffic. To achieve this, it is essential to determine the precise position of vehicles relative to each other. Global positioning system (GPS) devices have an intended positioning error that might increase due to various conditions, e.g., the number of available satellites, nearby buildings, trees, driving into tunnels, etc., making it difficult to compute the exact relative position between two vehicles. To address this challenge, this paper proposes a new architectural framework to improve positioning accuracy using images captured by onboard cameras. It presents a novel algorithm and performance results for vehicle positioning based on GPS and video data. This approach is decentralized, meaning that each vehicle has its own camera and computing unit and communicates with nearby vehicles.
The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.
Earthquakes are among the most dangerous natural disasters that can cause major losses to buildings and threaten human lives. The research community is very interested in the topic of earthquakes because they occur suddenly and predicting them is very important for human safety. Creating accurate earthquake prediction techniques by applying machine learning (ML) approaches will help save people's lives and prevent damage. To identify important features and analyze the correlation between these features before submitting them to classification models, we proposed a new feature selection approach in this paper which combines two filtering ways: Normalization which is based on the Chi-square approach and analysis of variance, and the correlation approach based on the logistic regression technique (CLR-AVCH). Accordingly, three algorithms are applied. Then a facilitated voting classifier is created that combines the two best models with the highest prediction accuracy (histogram-based gradient boosting, adaptive boosting) to create a single technique that includes the strengths of the techniques that were combined to help find important patterns in the acquired data to obtain a model capable of early prediction of earthquakes. The proposed work achieved higher accuracy, F1_score, recall, and precision (0.94, 0.92, 0.94, 0.92), respectively.
An asymmetric image encryption algorithm based on integer wavelet transformation (IWT) and Rivest-Shamir-Adleman (RSA) algorithm is proposed. Firstly, two plain characteristic parameters (PCP) of the plain image are extracted and two random numbers are chosen. Then, a new parameter transformation model (PTM) is constructed to do nonlinear processing for them, and three cipher characteristic parameters (CCP) are got. After applying RSA operation for CCP (seen as plain messages), three cipher messages are obtained. Secondly, a new initial value obtaining model (IOM) for all plain messages and cipher messages is established, by which initial values of 3D chaotic system are produced. Then, three chaotic sequences can be generated. Thirdly, chaotic sequences are used to confuse the plain image by a way of row-column cycle. Then, IWT operation is carried out and the above chaotic sequences are employed to confuse again the wavelet coefficients. Thereafter, inverse IWT is applied to get the confused image, realizing double confusion operations on both spatial domain and frequency domain. Finally, the confused image is diffused as a whole to get the cipher image. Experiment results explain that the proposed algorithm can realize the encryption in short time, and resist effectively against brute-force attack and noise attack.
Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are artificial neural networks, multi-expression programming, k-nearest neighbour, support vector machines, and decision trees with C5.0. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate acceptable error rates on the test set.
This data article presents four experimental sets of results related to flax fibre composites with epoxy shape memory polymer matrix: water vapor absorption, mass diffusion immersed in water, hygroscopic expansion, mechanical properties. The water vapor absorption tests are described in raw data related to four types of laminates with weights measured at different relative humidity (0%, 9%, 33%, 44%,75%, 85% and 100%). The mass diffusion experiments are related to weights of immersed samples over time. The unidirectional composite hygroscopic expansion is also measured along the fibre longitude and transverse directions. The mechanical properties of flax composite at various temperatures (20°C, 40°C, 60°C, 80°C and 100°C) and humidity environments (50% and immersed) are also described. Load-displacement diagrams of the hygromorph composites are converted into stress-strain diagrams via a compliance calibration, from which the tensile moduli are extracted. The data presented in this article can provide a benchmark for the development of new models, or for the determination of other properties via post processing. The detailed interpretation of the data can be found in [1]. The data is available in the Mendeley Data repository at [2].
Computer applications to medicine. Medical informatics, Science (General)
With the development of multimedia techniques, the demand for copyright protection of digital products has also gradually risen.Digital watermarking is an effective means to protect the copyright of digital products.It is generally made by adding important identification information (i.e., digital watermark) to a digital carrier (e.g., text, image, etc.), so that the carrier carries the identification information but does not affect the normal use of the carrier.The common digital watermark embedding scheme is to embed the watermark information by modifying the carrier via specific algorithms.In the actual application scenarios, there are many images or objects to be protected (such as art paintings, etc.) that are not allowed to be modified.Based on this background, a new carrier-independent deep optical watermarking algorithm was proposed, which can realize watermark information embedding without modifying the original carrier and achieve the purpose of copyright protection.Specifically, a new watermark template expression scheme at the embedding end was proposed, which expressed the watermark information by visible light modulation.By analyzing the visual system of human eyes, a watermark template pattern based on alternating projection was proposed to embed the watermark information, which made the embedding process neither require modification of the original carrier nor affect the visual senses of human eyes.At the extraction end, a watermark extraction network based on residual connection was designed, and the captured watermarked images were fed into this network after perspective transformation to extract the watermark information.The experiments were conducted under various conditions and comparisons with three baseline algorithms were made.The experimental results show that the proposed algorithm generates watermarked images with less visual distortion and is robust to the ＆quot;projecting-shooting＆quot; process.The watermark extraction network has high accuracy in extracting watermark information at different distances, angles and illumination conditions, and has certain advantages over other general networks.
Global Navigation Satellite Systems (GNSS) jamming is an acute problem in the world of modern navigation. As more and more applications rely on GNSS for both position and timing, jamming ramifications are becoming more severe. In this paper we suggest a novel framework to cope with these threats. First, a Bayesian jamming detection algorithm is introduced. The algorithm can both detect and track several jammers in a pre-defined region of interest. Then, a jamming coverage map algorithm is offered. Similar to cellular <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3</mn><mi>G</mi><mo>/</mo><mn>4</mn><mi>G</mi></mrow></semantics></math></inline-formula> coverage maps, such a map can detect “weak” GNSS reception spots and handle them. Since jamming interference can be a dynamic phenomenon (e.g., a vehicle equipped with a jammer), the coverage map changes with time. Thus, interference patterns can be detected more easily. Utilizing the offered algorithm, both on simulation and field experiments, we have succeeded to localize an arbitrary jammer(s) within the region of interest. Thus, the results validate the viability of the proposed method.
With the help of the auxiliary function method, we solved the improved Boussinesq (IBq) equation with fluid dynamic damping and the modified IBq (IMBq) equation with Stokes damping, and we obtained their three types of travelling wave exact solutions, which is an extension service of the numerical simulation and the existence of a solution. From the waveform diagram of IBq equation with hydrodynamic damping, it can be seen that when the propagation velocity of kink wave changes, the amplitude also changes significantly, and it is also found that the kink isolated waveform is significantly asymmetric due to the increase of damping coefficient v, which may be of some value in explaining some physical phenomena. In addition, the symbolic computing software maple makes our computing work easier.
CAI Hanshu,WANG Ziyang,ZHANG Yanhao,CHEN Yunfei,HU Bi
At present,depression is treated with antidepressant drugs and assisted with psychotherapy and physical therapy,and these expensive and time-consuming treatment methods often end prematurely and result in a prolonged course without effective symptom relief.Therefore,based on the neurofeedback therapy,a novel depression rehabilitation method using a Virtual-Reality(VR) game framework is proposed.In this VR game framework,an innovative three-electrode Electroencephalography (EEG) collector is used to record patients’ EEG data.The data are processed and converted into feedback features,and the feedback will be displayed to patients in real time through a VR headset.Patients can adjust their psychological activity based on the feedback,and relieve their physiological dysfunction effectively.The framework also provides a data storage module,which may offer therapists ways to assess patients’ rehabilitation results in a long term,as well as provide possibility to continue towards better algorithm of feedback features with data-mining.The prototype system is tested in the framework,and the experimental results prove the effectiveness of the framework in depression rehabilitation.