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We approach the questions, what part of evolutionary change results from selection, and what is the adaptive information flow into a population undergoing selection, as a problem of quantifying the divergence of typical trajectories realized under selection from the expected dynamics of their counterparts under a null stochastic-process model representing the absence of selection. This approach starts with a formulation of adaptation in terms of information and from that identifies selection from the genetic parameters that generate information flow; it is the reverse of a historical approach that defines selection in terms of fitness, and then identifies adaptive characters as those amplified in relative frequency by fitness. Adaptive information is a relative entropy on distributions of histories computed directly from the generators of stochastic evolutionary population processes, which in large population limits can be approximated by its leading exponential dependence as a large-deviation function. We study a particular class of generators that represent the genetic dependence of explicit transitions around reproductive cycles in terms of stoichiometry, familiar from chemical reaction networks. Following Smith (2023), which showed that partitioning evolutionary events among genetically distinct realizations of lifecycles yields a more consistent causal analysis through the Price equation than the construction from units of selection and fitness, here we show that it likewise yields more complete evolutionary information measures. [ABSTRACT FROM AUTHOR]

Collisions involving space objects pose significant threats to space sustainability. Within these collisions, catastrophic collisions which generate a larger amount of debris are particularly harmful. The quantity and characteristics of fragments generated from a collision are usually modeled using the NASA standard breakup model (SBM). However, on several occasions, the NASA SBM has incorrectly estimated the effects of collisions, including the Yunhai 1-02 breakup event. Using outer probability measure theory and information theory, we develop a correction methodology to estimate the Yunhai 1-02 event based on NASA SBM. This methodology successfully passes a statistical test using historical data. After correction, the characteristic diameter and relative velocity distributions of fragments generated from this event exhibit greater similarity with observations. The work in this paper facilitates the analysis of the properties of the debris cloud resulting from collisions with limited information, improving the predictions of future collisions. [Display omitted] • We condense and analyze the historical collision data. • We obtain a corrected parameter to adjust the SBM result, and this adjusted result passes the statistical test. • We improve the precision of characteristic diameter and relative velocity of fragments generated from Yunhai event. • This work is important to estimate the effect of collisions with limited information. [ABSTRACT FROM AUTHOR]

Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable. [ABSTRACT FROM AUTHOR]

To have accuracy in the extracted information is the goal of the reliability theory investigation. In information theory, varentropy has recently been proposed to describe and measure the degree of information dispersion around entropy. Theoretical investigation on varentropy of past life has been initiated, however details on its stochastic properties are yet to be discovered. In this paper, we propose a novel stochastic order and introduce new classes of life distributions based on past varentropy. Further, we illustrate some of its applications in reliability modeling and in the diversity measure of Boltzmann distribution. [ABSTRACT FROM AUTHOR]

The fitting and modeling of skewed, complex, symmetric, and asymmetric datasets is an exciting research topic in many fields of applied sciences: notably, lifetime, medical, and financial sciences. This paper introduces a heavy-tailed Nadarajah Haghighi model by compounding the heavy-tailed family and Nadarajah Haghighi distribution. The model obtained has three parameters that account for the scale and shape of the distribution. The proposed distribution's fundamental characteristics, such as the probability density, cumulative distribution, hazard rate, and survival functions, are provided, several key statistical properties are established, and several entropy information measures are proposed. Estimation of model parameters is performed via a maximum likelihood estimator procedure. Further, different simulation experiments are conducted to demonstrate the proposed estimator's performance using measures like the average estimate, the average bias, and the associated mean square error. Finally, we apply our proposed model to analyze three different real datasets. In our illustration, we compare the practicality of the recommended model with several well-known competing models. [ABSTRACT FROM AUTHOR]

In today's TEST interconnected world, the security of 5G Medical IoT networks is of paramount concern. The increasing number of connected devices and the transmission of vast amounts of data necessitate robust measures to protect information integrity and confidentiality. However, securing Medical IoT edge nodes poses unique challenges due to their limited resources, making the implementation of cryptographic protocols a complex task. Within these protocols, modular multiplication assumes a crucial role. Therefore, careful consideration must be given to its implementation. This study focuses on developing a resource-efficient hardware implementation of the Montgomery modular multiplication algorithm over GF( 2 l ), which is a critical operation in cryptographic algorithms. The proposed solution introduces a bit-serial systolic array layout with a modular structure and local connectivity between processing elements. This design, inspired by the principles of symmetry, allows for efficient utilization of resources and optimization of area and delay management. This makes it well-suited for deployment in compact Medical IoT edge nodes with limited resources. The suggested bit-serial processor structure was evaluated through ASIC implementation, which demonstrated substantial improvements over competing designs. The results showcase an average area reduction of 24.5% and significant savings in the area–time product of 26.2%. [ABSTRACT FROM AUTHOR]

Parenting stress has long been proposed as a major risk factor for child maltreatment. However, there is a lack of evidence from existing studies on the temporal sequence to establish a causal relationship. This study aims to examine bidirectional temporal relationships between parenting stress and child maltreatment. Longitudinal data from two different sources were analysed: a pre-post study of an online parenting programme conducted across six countries - the ePLH Evaluation Study, and a prospective cohort study in the United States - LONGSCAN. Cross-lagged panel model on parenting stress and child maltreatment was used in each dataset. Based on repeatedly measured data of 484 caregivers in the ePLH study across five time points (every two weeks), we found that parenting stress at an earlier time point predicted later child maltreatment (IRR = 1.14, 95 % CI: 1.10,1.18). In addition, the occurrence of child maltreatment was associated with higher subsequent short-term parenting stress (IRR = 1.04, 95 % CI: 1.01,1.08) and thus could form a vicious circle. In the LONGSCAN analysis with 772 caregivers who were followed up from child age of 6 to child age of 16, we also found parenting stress at an earlier time point predicted later child maltreatment (β = 0.11, 95 % CI: 0.01,0.20), but did not observe an association between child maltreatment and subsequent long-term parenting stress. Potential information bias on the measurements. This study provides evidence for a bidirectional temporal relationship between parenting stress and child maltreatment, which should be considered in parenting intervention programmes. • We observed a bi-directional relationship between parenting stress and child maltreatment. • Parenting stress at an earlier time point predicted later child maltreatment. • The occurrence of child maltreatment predicted higher subsequent short-term parenting stress. • Stress-related affective factors should be considered in parenting intervention programmes. [ABSTRACT FROM AUTHOR]

Enormous information is an accumulation of vast measures of information that contains copied things gathered from different sources. Distinguishing copied information from enormous information is a difficult issue. In this paper secure deduplication scheme has been presented for huge information stockpiling. Customers may store and transmit data effectively and efficiently due to cloud service providers. Providers of cloud services are drawn to the application of data de-duplication tools to lower storage costs and conserve bandwidth. To secure the data they share on the cloud, users of the cloud are interested in having a secure and private connection. As a result, the priory is transferred to the cloud, and the data is encrypted. Because the encryption objective conflicts with the purpose of data deduplication, this feature presents a difficult problem. The author then suggested brand-new hybrid chunking methods to boost deduplication performance in cloud storage. Content-based hybrid chunking algorithm of Two Threshold Two Divisor (TTTD) with a Dynamic Prime Coding (DPC) algorithm is suggested for reducing the deduplication data. To increase throughput, which means reducing execution time and CPU resource utilisation, an index method must be incorporated into the deduplication matching process. For chunks with higher security levels, it offers greater security protection, while for chunks with lower security levels, it trades off security for greater deduplication effectiveness. When moving data to open, semi-trusted systems like public clouds, identity management, secure data exchange, and privacy preservation become more challenging than they are in closed, trusted systems where security and privacy can be controlled more simply. Accordingly, the article proposed a cross-domain authentication technique for the verification process. Evaluations of the real-world datasets show the efficiency of the design. With other methods, the proposed hybrid Content-based TTTD-DPC analyses many performance parameters, including chunk distribution, processing time, chunking time, throughput, average chunk size, and deduplication ratio. The deduplication system's total throughput and processing speed are both enhanced by the proposed approach. In addition, the recommended methodology reduces computational cost (772 ms) in terms of hash function compared to other existing methods. [ABSTRACT FROM AUTHOR]

The effects of induced incidental moods on patterns of information search and decision outcomes were investigated in a risky choice task with mixed-domain problems. Viewing of short videos was used to induce either happy or sad mood in participants, who then made choices between pairs of options consisting of a probabilistic gain coupled with a probabilistic loss. Eyetracking measures of information search, specifically frequencies of transitions between key aspects of the decision alternatives, were analyzed and related to use of heuristic or analytic compensatory strategies. Data were also gathered in a control condition, where participants were instructed to use an EV-calculation strategy, a prototypical integrative compensatory strategy. Results showed significant differences in choices and attention transitions between the EV-instruction and the induced mood conditions, but minimal differences between the happy and sad induced mood conditions. Participants in the induced mood conditions showed relatively more evidence of heuristic strategy use, but analytic strategies remained the modal strategy in all conditions. Importantly, key types of attention transitions were shown to reliably predict the frequency of observed choices consistent with optimal (EV- maximizing) and certain heuristic strategies. [ABSTRACT FROM AUTHOR]

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements' distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements' distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp. [ABSTRACT FROM AUTHOR]

It is interesting to learn that the complementary dual of the Shannon entropy measure exists and has some common properties. This new measure of uncertainty has been introduced by Lad et al. (2015) and is known as extropy. Although there are some mathematical analogies between the two measures, extropy typically has different uses and interpretations than entropy. Taking into account the importance of extropy measure, and its various generalizations, in the present communication, we consider and study Kullback-Leibler based "divergence-extropy" measure between the distribution of nth upper k-record and mth upper k-record values. Characterization problems for the proposed "divergence-extropy" measure have been studied. Further, some specific lifetime distributions used in lifetime testing, physical sciences, survival analysis and reliability engineering have been studied using the proposed "divergence-extropy" measure. At the end, we study the proposed "divergence-extropy" measure between the distribution of k-record value and order statistics. [ABSTRACT FROM AUTHOR]

Interactions among atmospheric, root‐soil, and vegetation processes drive carbon dioxide fluxes (Fc) from land to atmosphere. Eddy covariance measurements are commonly used to measure Fc at sub‐daily timescales and validate process‐based and data‐driven models. However, these validations do not reveal process interactions, thresholds, and key differences in how models replicate them. We use information theory‐based measures to explore multivariate information flow pathways from forcing data to observed and modeled hourly Fc, using flux tower data sets in the Midwestern U.S. in intensively managed corn‐soybean landscapes. We compare multiple linear regressions, long‐short term memory, and random forests (RF), and evaluate how different model structures use information from combinations of sources to predict Fc. We extend a framework for model predictive and functional performance, which examines a suite of dependencies from all forcing variables to the observed or modeled target. Of the three model types, RF exhibited the highest functional and predictive performance, correctly capturing strong dependencies between radiation and temperature variables with Fc. Regionally trained models demonstrate lower predictive but higher functional performance compared to site‐specific models, suggesting superior reproduction of observed relationships at the expense of predictive accuracy. This study shows that some metrics of predictive performance encapsulate functional behaviors better than others, highlighting the need for multiple metrics of both types. This study improves our understanding of carbon fluxes in an intensively managed landscape, and more generally provides insight into how model structures and forcing variables translate to interactions that are well versus poorly captured in models. Plain Language Summary: In an agricultural landscape, exchanges of carbon dioxide between the land and atmosphere occur due to photosynthesis and respiration and depend on weather, soil, and vegetation conditions. Traditional model performance metrics focus on the relationship between observed and modeled outputs, while functional performance considers the relationships between interacting inputs and outputs. We compare several performance measures for three different machine learning (ML) models that simulate sub‐daily carbon fluxes. We look at how drivers such as solar radiation, soil moisture, temperature, humidity, and rainfall provide information to carbon fluxes, and whether different ML models also capture these interactions. In other words: Air, soil, and plants drive carbon's upward path,Models are detectives, interpreting their math.With information theory, we map data's travel courses,To see how models find or miss carbon's causal sources. Key Points: Information theory measures describe individual and joint causal relationships in observed versus modeled vertical carbon dioxide fluxesThree machine learning models overestimate unique information from sources at the expense of synergistic, or joint informationRegionally trained models have improved functional but not predictive performances, indicating a trade‐off [ABSTRACT FROM AUTHOR]

Two distinct measures of information, connected respectively to the amplitude and phase of the wave function of a particle, are proposed. There are relations between the time derivatives of these two measures and their gradients on the configuration space, which are equivalent to the wave equation. The information related to the amplitude measures the strength of the potential coupling of the particle (which is itself aspatial) with each volume of its configuration space, i.e., its tendency to participate in an interaction localized in a region of ordinary physical space corresponding to that volume. The information connected to the phase is that required to obtain the time evolution of the particle as a persistent entity starting from a random succession of bits. It can be considered as the information provided by conservation principles. The meaning of the so-called "quantum potential" in this context is briefly discussed. [ABSTRACT FROM AUTHOR]

Inaccuracy and information measures based on cumulative residual entropy are quite useful and have received considerable attention in many fields, such as statistics, probability, and reliability theory. In particular, many authors have studied cumulative residual inaccuracy between coherent systems based on system lifetimes. In a previous paper (Bueno and Balakrishnan, Prob. Eng. Inf. Sci. 36 , 2022), we discussed a cumulative residual inaccuracy measure for coherent systems at component level, that is, based on the common, stochastically dependent component lifetimes observed under a non-homogeneous Poisson process. In this paper, using a point process martingale approach, we extend this concept to a cumulative residual inaccuracy measure between non-explosive point processes and then specialize the results to Markov occurrence times. If the processes satisfy the proportional risk hazard process property, then the measure determines the Markov chain uniquely. Several examples are presented, including birth-and-death processes and pure birth process, and then the results are applied to coherent systems at component level subject to Markov failure and repair processes. [ABSTRACT FROM AUTHOR]

The purpose of the Article is to analyze and compare certain aspects which define the limits of permissible interference with a person's private life in the legislation of Ukraine and other countries of the world in terms of ensuring the balance of interests of participants in criminal proceedings during covert measures to obtain information related to interference with a person's private life. The Article uses general scientific and special research methods, in particular comparative legal, scientific categories, definitions and approaches, formal dogmatic (legal), dialectical. On the basis of the conducted research, generalized conclusions were made regarding general trends in the field of reforming the legal regulation of special measures for covertly obtaining information, and ways of their improvement. The forms and methods of departmental, judicial, and public control over the covert activities of law enforcement agencies have been determined separately, and the influence of terrorist threats and military actions on ensuring the balance of interests of participants in criminal proceedings has been investigated. According to the results of the research, ways of solving problematic issues of the regulation of criminal procedural legislation have been determined in order to ensure the balance of the interests of the participants in criminal proceedings during the organization, conduct, recording, storage, and use of the results of covert information-gathering activities. A comparative analysis of the ways of solving a number of legal regulation issues in the field of the use of informal forms of obtaining information, the determination of different approaches to the separation of departmental, judicial, and public control is being carried out for the first time and will provide a comprehensive and systematic approach to the improvement of legislation in the specified field in the conditions of martial law or during anti-terrorist activities. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel localization scheme, location-aware ranging correction (LARC), to correct ranging estimates from ultra wideband (UWB) signals. Existing solutions to calculate ranging corrections rely solely on channel information features (e.g., signal energy, maximum amplitude, estimated range). We propose to incorporate a preliminary location estimate into a localization chain, such that location-based features can be calculated as inputs to a range-error prediction model. This way, we can add information to range-only measurements without relying on additional hardware such as an inertial measurement unit (IMU). This improves performance and reduces overfitting behavior. We demonstrate our LARC method using an open-access measurement dataset with distances up to 20 m, using a simple regression model that can run purely on the CPU in real-time. The inclusion of the proposed features for range-error mitigation decreases the ranging error 90th percentile (P90) by 58% to 15 cm (compared to the uncorrected range error), for an unseen trajectory. The 2D localization P90 error is improved by 21% to 18 cm. We show the robustness of our approach by comparing results to a changed environment, where metallic objects have been moved around the room. In this modified environment, we obtain a 56% better P90 ranging performance of 16 cm. The 2D localization P90 error improves as much as for the unchanged environment, by 17% to 18 cm, showing the robustness of our method. This method evolved from the first-ranking solution of the 2021 and 2022 International Conference on Indoor Position and Indoor Navigation (IPIN) Competition. [ABSTRACT FROM AUTHOR]

The rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently, this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can provide images with a centimeter-level or even subcentimeter-level ground resolution, which provides a new, efficient, and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we proposed a real-time edge reconstruction crack detection network (ERNet) that adopted multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and the background. To capture global dependencies across spatial and channel levels, we proposed an efficient bilateral decomposed convolutional attention module (BDAM) that combined depth-separable convolution and dilated convolution to capture global dependencies across the spatial and channel levels. To enhance the accuracy of crack detection, we used a coordinate-based fusion module that integrated spatial, semantic, and edge reconstruction information. In addition, we proposed an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. The experimental results demonstrated that our network achieved the best balance between accuracy and inference speed compared to six established models. [ABSTRACT FROM AUTHOR]

Investment in health has been proposed as a mechanism to promote upward social mobility. Previous analyses have reported inconsistent estimates of the returns to investment in health in Mexico based on different models for different years. We aim to estimate returns for Mexico using data from four time points Adult height and labor income are drawn from the periodical national health and nutrition surveys–a group of relatively standardized surveys—that are representative of individuals living in the country in 2000, 2006, 2012 & 2018. These surveys collect anthropometric measurements and information on individuals' labor income. We estimated Mincerian models separately for men and women using OLS, Heckman, instrumental variables, and Heckman with instrumental variables models. Our results indicate significant and positive returns to health for the four surveys, similar in magnitude across years for women and with variations for men. By 2018, returns to health were about 7.4% per additional centimeter in height for females and 9.3% for males. Investments in health and nutrition during childhood and adolescence that increase health capital–measured as adult height—may promote social mobility in Mexico and similar countries to the extent that these investments differentially increase health capital among the poor. [ABSTRACT FROM AUTHOR]

Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information. [ABSTRACT FROM AUTHOR]

Small objects in images captured by UAV have the characteristics of unclear feature information, complex backgrounds, and partial target occlusion, which leads to problems of false detection and missed detection during detection. In response to these problems, an improved UAV small target detection method SDT-YOLOv5 is proposed. Firstly, the dataset images are sliced to increase the proportion of small objects in each slice and improve the recognition ability of small objects. Secondly, a dynamic decoupled detection head is used to introduce dynamic convolution and adaptive receptive field mechanism while decoupling the classification and regression branches of the detection head to achieve stronger feature expression and extraction capabilities. Finally, an optimal transmission allocation method based on intersection-union ratio loss of minimum point distance is proposed. The distance between the upper left corner and lower right corner of the predicted bounding box and the real bounding box is minimized, and then based on the position information and distance measurement of the bounding box, with the goal of minimizing the total cost, the optimal real bounding box and predicted bounding box matching scheme is found to improve detection accuracy. Through experimental results on the VisDrone2019 dataset, the mAP50 value of the improved YOLOv5 reaches 58.5%, which is 23.2 percentage points higher than the mAP50 value of the original YOLOv5. This shows that the improved method effectively improves the detection accuracy of UAV small objects and can detect small objects more accurately. [ABSTRACT FROM AUTHOR]

Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. To effectively transfer information about the stimulus to the next processing level, a neuron needs to be able to adapt its working range to the properties of the stimulus. Here, we focus on the intrinsic neural properties that influence information transfer in cortical neurons and how tightly their properties need to be tuned to the stimulus statistics for them to be effective. We start by measuring the intrinsic information encoding properties of putative excitatory and inhibitory neurons in L2/3 of the mouse barrel cortex. Excitatory neurons show high thresholds and strong adaptation, making them fire sparsely and resulting in a strong compression of information, whereas inhibitory neurons that favour fast spiking transfer more information. Next, we turn to computational modelling and ask how two properties influence information transfer: 1) spike-frequency adaptation and 2) the shape of the IV-curve. We find that a subthreshold (but not threshold) adaptation, the 'h-current', and a properly tuned leak conductance can increase the information transfer of a neuron, whereas threshold adaptation can increase its working range. Finally, we verify the effect of the IV-curve slope in our experimental recordings and show that excitatory neurons form a more heterogeneous population than inhibitory neurons. These relationships between intrinsic neural features and neural coding that had not been quantified before will aid computational, theoretical and systems neuroscientists in understanding how neuronal populations can alter their coding properties, such as through the impact of neuromodulators. Why the variability of intrinsic properties of excitatory neurons is larger than that of inhibitory ones is an exciting question, for which future research is needed. Author summary: Intracellular information transfer from synaptic input to output spike train is necessarily lossy. Here, we explicitly measure the mutual information between a neuron's input and spike output and show that information transfer is more lossy and heterogeneous for excitatory than for inhibitory neurons. By using computational modelling we show that the shape of the input-output curve as well as how fast a neuron adapts to its input collectively determine the rate of information loss. These insights will help both experimentalists and modellers in designing and simulating experiments that investigate how network coding properties can adapt to the environment, for instance through the effects of neuromodulators. [ABSTRACT FROM AUTHOR]

High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a "plug and play" Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightlycoupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate realtime covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment). [ABSTRACT FROM AUTHOR]

Recommender systems (RSs) consist of predicting missing ratings based on the observed ones. This problem corresponds to matrix completion where users are its rows and items are its columns and it contains the observed ratings. An efficient RS is the one promoting the personal relevancy of its users which is not the case in the matrix completion process. It takes into account all the rates for prediction without including the users' and items' characteristics. Patterns are the key enablers of such solutions. In this work, we present a three-mode tensor representation with two aspects namely user–item interactions (observed ratings) and item–item relationship (detected similarities). To capture the similarities, the patterns are grouped in an equivalent manner using a bi-quantum clustering process (Quantum K-means). This step is adopted to consider only the relevant observed ratings in the prediction process and express item-to-item relationship using fidelity distance. Then, for each missing rating r that a user u might give to an item i in the future, a sub-tensor is created according to the detected patterns. This sub-tensor then is completed by minimizing its rank. This problem is NP-hard, hence a surrogate is used which is the nuclear norm. The effectiveness of the proposed approach is measured according to information retrieval evaluation criteria: precision, recall and F 1 -measure. The proposed approach improved the precision of the state-of-the-art methods by 20 %. [ABSTRACT FROM AUTHOR]

Spine tumors, both primary and metastatic, impose significant morbidity and mortality on patients and physicians. Patient-reported outcomes are valuable tools to assess a patient's impression of their health status and enhance communication between physicians and patients. Various spine generic patient-reported outcome tools have traditionally been used but have not been validated in the spine tumor patient population. The Spine Oncology Study Group Outcome Questionnaire, which is disease-specific for the metastatic spine patient population, has been shown to have strong validity, even across multiple languages. Patient-Reported Outcomes Measurement Information System, which has recently been developed, employs computerized adaptive testing to assess multiple health domains. It has been shown to capture information in both generic and specific questionnaires and has the potential to be used as a universal tool in the spine oncology patient population. Further long-term studies, as well as, cross-cultural adaptations, are needed to validate Patient-Reported Outcomes Measurement Information System's applicability and effectiveness. [ABSTRACT FROM AUTHOR]

The formed surface quality of metallic sealing ring of aero-engine affects the aircraft service performance directly. However, existing inspection methods, such as the final destructive inspection and the line laser scanner section profile measurement, only evaluate the formed quality from a 2-D view, that is, single or multiple radial formed section profiles. The lack of geometric information of 3-D surface is not conducive to the comprehensive monitoring of forming quality and process planning. Therefore, based on the line laser scanners, this paper mainly proposes a vibration errors compensation method based on self-feature registration. Aiming at the problem of rigid transformation of the measurement profile caused by random vibration during the rotary motion of metallic sealing ring, the feature of measurement profile in stationary scene (MPSS), that is, the medial axis, is used as the reference for the correct pose of measurement profile. The principle of finding the correct pose of measurement profile in rotary motion scene (MPRMS) is to minimize the distance between the medial axes. Next, based on the rotary motion information of metallic sealing ring and the geometric information of measurement system, a 3-D reconstruction matrix is built, so as to convert each measurement profile to the base coordinate system in turn, and finally a 3-D dynamic measurement method for the metallic sealing ring forming surface is built. The effectiveness of the proposed method is verified through simulation experiment and real measurement. [ABSTRACT FROM AUTHOR]

Variation in the strength of synapses can be quantified by measuring the anatomical properties of synapses. Quantifying precision of synaptic plasticity is fundamental to understanding information storage and retrieval in neural circuits. Synapses from the same axon onto the same dendrite have a common history of coactivation, making them ideal candidates for determining the precision of synaptic plasticity based on the similarity of their physical dimensions. Here, the precision and amount of information stored in synapse dimensions were quantified with Shannon information theory, expanding prior analysis that used signal detection theory (Bartol et al., 2015). The two methods were compared using dendritic spine head volumes in the middle of the stratum radiatum of hippocampal area CA1 as well-defined measures of synaptic strength. Information theory delineated the number of distinguishable synaptic strengths based on nonoverlapping bins of dendritic spine head volumes. Shannon entropy was applied to measure synaptic information storage capacity (SISC) and resulted in a lower bound of 4.1 bits and upper bound of 4.59 bits of information based on 24 distinguishable sizes. We further compared the distribution of distinguishable sizes and a uniform distribution using Kullback-Leibler divergence and discovered that there was a nearly uniform distribution of spine head volumes across the sizes, suggesting optimal use of the distinguishable values. Thus, SISC provides a new analytical measure that can be generalized to probe synaptic strengths and capacity for plasticity in different brain regions of different species and among animals raised in different conditions or during learning. How brain diseases and disorders affect the precision of synaptic plasticity can also be probed. [ABSTRACT FROM AUTHOR]

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance. [ABSTRACT FROM AUTHOR]

We propose a method to reconstruct sea surface height anomalies (SSHA) from vector surface currents and winds. This analysis is motivated by the proposed satellite ODYSEA, which is a Doppler scatterometer that measures coincident surface vector winds and currents. If it is feasible to estimate SSHA from these measurements, then ODYSEA could provide collocated fields of SSHA, currents, and winds over a projected wide swath of ∼1700 km. The reconstruction also yields estimates of the low-frequency surface geostrophic, Ekman, irrotational, and nondivergent current components and a framework for separation of balanced and unbalanced motions. The reconstruction is based on a steady-state surface momentum budget including the Ekman drift, Coriolis acceleration, and horizontal advection. The horizontal SSHA gradient is obtained as a residual of these terms, and the unknown SSHA is solved for using a Helmholtz–Hodge decomposition given an imposed SSHA boundary condition. We develop the reconstruction using surface currents, winds, and SSHA off the U.S. West Coast from a 43-day coupled ROMS–WRF simulation. We also consider how simulated ODYSEA measurement and sampling errors and boundary condition uncertainties impact reconstruction accuracy. We find that temporal smoothing of the currents for periods of 150 h is necessary to mitigate large reconstruction errors associated with unbalanced near-inertial motions. For the most realistic case of projected ODYSEA measurement noise and temporal sampling, the reconstructed SSHA fields have an RMS error of 2.1 cm and a model skill (squared correlation) of 0.958 with 150-h resolution. We conclude that an accurate SSHA reconstruction is feasible using information measured by ODYSEA and external SSHA boundary conditions. [ABSTRACT FROM AUTHOR]

Cybersecurity is crucial in the digital age, as organizations and individuals face increasing threats when using digital devices. The interdisciplinary nature of cybersecurity requires consolidation to meet the requirements of further theoretical and practical applications and international standards and regulations. This study reviews studies published between 1991 and 2022 in the field of cybersecurity and information security, examining trends and relevant publications and collaborations. The aim is to develop a modern, relevant, and up-to-date measurement method – named Security Awareness Measurement – by analysing security awareness measurements and questionnaires used in recent years. The study uses the Web of Science database, Zotero reference management program, VOSviewer software, Scopus database, and GoogleScholar databases for analysis. [ABSTRACT FROM AUTHOR]

Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question at an arbitrary number of discrete random variables. This work proposes a solution with a non-negative PID that satisfies an inclusion–exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. Zonogons are the Neyman–Pearson region for an indicator variable of each target state, and f-information is the expected value of quantifying its boundary. We prove that the proposed decomposition satisfies the desired axioms and guarantees non-negative partial information results. Moreover, we demonstrate how the obtained decomposition can be transformed between different decomposition lattices and that it directly provides a non-negative decomposition of Rényi-information at a transformed inclusion–exclusion relation. Finally, we highlight that the decomposition behaves differently depending on the information measure used and how it can be used for tracing partial information flows through Markov chains. [ABSTRACT FROM AUTHOR]

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max–min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results. [ABSTRACT FROM AUTHOR]

The European Commission has recently launched a proposal for a Directive to promote repair. In this paper we critically analyse the drafting process in order to understand the resulting policy options. We consider that although the ambition of the Directive was low from the beginning given the limited policy options, it became even worse during the consultation period. The selection of barriers to repair was limited, and manufacturers pushed for a narrow and closed form of Right-to-Repair (R2R) that transferred all control over the potentially emerging repair market to them. Some information measures were proposed so as to increase transparency, potentially increasing the administrative burden on independent repairers, along with measures to encourage repair when under legal guarantee. This Directive fails to promote repair or help independent repairers overcome access barriers, nor does it allow member states to go beyond the limits imposed in the Directive itself. [ABSTRACT FROM AUTHOR]

US-China relations were marked by a paradox during the first trimester of 2024. On the one hand, a distinct stabilization was evident in ties. The two sides made concerted efforts to translate their leaders' 'San Francisco Vision' into reality. Cabinet officials exchanged visits across the Pacific, working groups and dialogue mechanisms met in earnest and produced outcomes, functional cooperation was deepened, sensitive issues such as Taiwan were carefully managed, and effort was devoted to improving the relationship's political optics. On the other hand, the negative tendencies in ties continued to deepen. Both sides introduced additional selective decoupling as well as cybersecurity measures in key information and communications technology and services sectors, with US actions bearing the signs of desinicization--rather than mere decoupling--of relevant supply chains. The chasm in strategic perception remained as wide as before. In sum, the "new normal" in US-China relations continued to take form, one piece at a time. [ABSTRACT FROM AUTHOR]

The countries face a significant difficulty in achieving the Sustainable Development Goals (SDG). Due to widespread pollution, SDGs pertaining to environmental sustainability in particular pose the greatest challenges. To cope up with the environmental issues, countries have adopted various measures out of which technological advancement and green finance are gaining particular importance. The present study thus aims at estimating the effect of technological advancements and green finance on environmental sustainability measured by ecological footprints under STIRPAT model in Indonesia. Time series data spanning over 1995–2020 period is collected from different secondary sources and empirical analysis is done by applying Quantile Autoregressive Distributed Lag Model. The findings indicate the positive contribution of technological advancement measured by Information and Communication Technologies (ICT) and green finance in environmental sustainability over whole range of quantiles (0.05–0.90) in the short as well as in the long run. However, economic growth and urbanization are found to pose serious threats to the environmental sustainability as they have positive association with ecological footprints at different quantiles. The findings suggest the Indonesian government to pay attention to significant fruitful policies aimed at the promotion of green finance and ICT technologies to ensure environmental sustainability in Indonesia. [ABSTRACT FROM AUTHOR]

Existing methods for measuring the spatial information of area maps fail to take into account the diversity of adjacency relations and the heterogeneity of adjacency distances among area objects, resulting in insufficient measurement information. This article proposes a method for measuring area map information that considers the diversity of the node–edge and Gestalt principles. Firstly, this method utilizes the adjacency relations between the Voronoi diagram of area objects to construct an adjacency graph that characterizes the spatial distribution of area objects in area maps. This adjacency graph serves as the information representation of area maps. Secondly, the method selects four characteristic indicators, namely geometric information, node degree, adjacency distance, and adjacency strength, to represent the diversity of nodes and edges in the graph that affect spatial information. Finally, nodes in the adjacency graph are taken as the basic units, and the spatial information of area maps is comprehensively calculated by integrating the four characteristics that represent spatial information. To verify the validity and rationality of the proposed method, a dataset of continuously simplified area maps and a dataset of artificially simulated degrees of randomness were designed to evaluate the performance of the existing method and the method proposed in this paper. The results indicate that the correlation between the measurement results obtained by the method proposed in this paper and the degree of disorder is as high as 0.94, outperforming the existing representative methods. Additionally, the correlation between the measurement results of this method and the degree of simplification reaches 1, indicating that the variation range of the measured values is more consistent with the cognitive assumptions based on artificial simulations compared to the existing methods. The experimental results show that the method proposed in this paper is an effective metric approach for representing spatial information in area maps. [ABSTRACT FROM AUTHOR]

Security and availability are crucial for users who outsource computational tasks to remote servers. However, computation services provided by cloud platforms suffer outage risks, potential enforced censorship, and network latency problems due to the centralized architecture, also having monopolistic service prices.We proposed a decentralized platform for outsourced trusted computing. Compared with prior works, our platform works autonomously based on a public blockchain without reliance on a trusted third party. The platform is open and public for computation nodes of Trusted Execution Environments (TEE) to join and provide trusted computing services out of financial incentives. Moreover, we designed a novel protocol, named b-DTC. First, it prevents free-riding behaviors against users and prevents false reporting against computing nodes. Second, it supports real-time multi-round off-chain trusted computing in a pay-as-you-go manner, such that the performance of outsourced computing is not limited by the underlying blockchain. Third, for an incentive of high reliability and availability, it trustworthily measures the liveness, workload, and performance of the nodes and uses the measurement information to calculate the nodes' reputation. We analyzed and proved the security of our protocol. Extensive experiments are conducted based on two real-world scenarios: a clinical self-diagnosis system and an outsourced genetic testing application. Experimental results show that our system is feasible and the cost of introducing blockchain and TEE into our system is acceptable: Only takes about 20 seconds more than that of a cloud to finish a task that provides trusted service for 1000 users; The off-chain computing cost in our system is only about 20% more than that of the cloud. [ABSTRACT FROM AUTHOR]

Entropy is an important tool of information measurement in the fuzzy set and its inference. The research on information measurement based on interval-valued Pythagorean fuzzy sets mostly involves the distance formula for interval-valued Pythagorean fuzzy numbers, but seldom involves the measurement of fuzziness. In view of this situation, we have aimed to propose new interval-valued Pythagorean fuzzy entropy and weighted exponential entropy schemes. Based on the interval-valued Pythagorean fuzzy weighted averaging operator, a strategy based on weighted exponential entropy is proposed to solve the multi-criteria group decision-making (MCGDM) problem in the interval-valued Pythagorean environment. Two examples illustrate that this paper provides a feasible new method to solve the MCGDM problem in an interval-valued Pythagorean fuzzy (IVPF) environment. Finally, by comparing with the existing methods, it is concluded that the entropy measure of IVPF schemes and the corresponding MCGDM method can select the optimal solution of the practical problem more precisely and accurately. Therefore, the comparative analysis shows that the proposed measurement method has the characteristics of flexibility and universality. [ABSTRACT FROM AUTHOR]

This article dealt with further properties of the Renyi entropy and the residual Renyi entropy of k-record values. First, we discussed the Renyi entropy order and its connection with the usual stochastic and dispersive orders. We then addressed the monotonicity properties of the residual Renyi entropy of k-records, focusing on the aging properties of the component lifetimes. We also expressed the residual nth upper k-records in terms of Renyi entropy when the first dataset exceeded a certain threshold, and then studied various properties of the given formula. Finally, we conducted a parametric estimation of the Renyi entropy of the nth upper k-records. The estimation was performed using both real COVID-19 data and simulated data. [ABSTRACT FROM AUTHOR]

Currently, for underwater close-range large-target localization, visual localization techniques fail since large targets completely occupy the camera's field of view at ultraclose ranges. To address the issue, a multi-stage optical localization method combining a binocular camera and a single-point laser rangefinder is proposed in this paper. The proposed method comprises three parts. First, the imaging model of the underwater camera is modified, and a laser rangefinder is used to further correct the underwater calibration results of the binocular camera. Second, YOLOv8 is applied to recognize the targets to prepare for target localization. Third, extrinsic calibration of the binocular camera and laser rangefinder is performed, and a Kalman filter is employed to fuse the target position information measured by the binocular camera and laser rangefinder. The experimental results show that, compared with using a binocular camera alone, the proposed method can accurately and stably locate the target at close ranges with an average error of only 2.27 cm, without the risk of localization failure, and reduces binocular localization error by 90.57%. [ABSTRACT FROM AUTHOR]

When does inspecting a certain graphical plot allow for an investigator to reach the right statistical conclusion? Visualizations are commonly used for various tasks in statistics—including model diagnostics and exploratory data analysis—and though attractive due to its intuitive nature, the lack of available methods for validating plots is a major drawback. We propose a new technical validation method for visual reasoning. Our method trains deep neural networks to distinguish between plots simulated under two different data generating mechanisms (null or alternative), and we use the classification accuracy as a technical validation score (TVS). The TVS measures the information content in the plots, and TVS values can be used to compare different plots or different choices of data generating mechanisms, thereby providing a meaningful scale that new visual reasoning procedures can be validated against. We apply the method to three popular diagnostic plots for linear regression, namely scatterplots, quantile-quantile plots and residual plots. We consider various types and degrees of misspecification, as well as different within-plot sample sizes. Our method produces TVSs that increase with increasing sample size and decrease with increasing difficulty, and hence the TVS is a meaningful measure of validity. [ABSTRACT FROM AUTHOR]

Throughout the COVID-19 crisis, scientists around the globe have engaged in science communication to an unprecedented degree to convey first-hand epidemiological knowledge and information on preventive measures. The present work is aimed at empirically investigating the impact of direct exposure to scientists as compared to general COVID-19-related media consumption (N = 698) on central cognitive, affective, and behavioral variables, based on the extended parallel process model (EPPM) and its adaptations. A segment of the sample comprises individuals recruited independently, while others were sourced from an online panel. Importantly, this study sample was conducted at the outset of the COVID-19 pandemic. The results revealed that direct exposure to scientists positively affected recipients' knowledge and self-efficacy. General media consumption, by contrast, positively affected perceived threat as well as fear and uncertainty. Both sources positively affected the adherence to protective measures. [ABSTRACT FROM AUTHOR]

In traditional tunnel monitoring, the characteristic points of an object within a tunnel are measured to obtain information about the object. Considering the limitations of the traditional method in measuring the complex surface structure of tunnels, such as limited monitoring points, a long measurement period, and low precision, this study introduces an approach that uses three-dimensional (3D) laser scanning for monitoring tunnel cross-section deformation. Using this approach, the soft surrounding rock of a high-altitude ultralong tunnel was taken as the monitoring object. The test tunnel was first scanned using a 3D laser scanner, and the collected data were processed. The internal structural data of the tunnel were subsequently compared with its actual contour lines and the data of its primary branch and secondary lining on different dates. The results indicate that the arch roof of the tunnel tended to be stable within a certain time range when the positions of the primary branch and secondary lining were at different measuring points with different pile numbers. The deformation of the pile number on the left and right sides did not generally exceed 0.02 m, except at a few measuring points. A comparison between the actual cross section of the initial branch and that of the designed section showed that the actual elevation of the arch of the initial branch of the tunnel was greater than its designed elevation by no more than 0.3 m. Hence, through this study, a convenient and practical method is presented for monitoring deformation in complex curved tunnel structures. [ABSTRACT FROM AUTHOR]

Residual current protection can detect and isolate the grounding (leakage) fault of low-voltage distribution networks in time, which is an essential technical measure to reduce electric shocks and fire accidents and improve power supply safety. This paper systematically analyzes the operating characteristics of low-voltage distribution networks and proposes a distributed residual current protection method based on closed sections. It utilizes the capabilities of a distribution IoT platform to provide comprehensive measurement information for the entire substation area. A method was introduced to divide the low-voltage distribution substation into different protection closed surfaces, defining the current phasor at the external contact point of the closed surface and the remaining current of the closed surface. A calculation method for the critical current based on the remaining current of the closed surface for fault detection was proposed. Case studies have shown that this method is less affected by the inherent current and can significantly improve the sensitivity of protection. For TN-C and TN-C-S systems, in the selection of closed surfaces, the repeated grounding point of the neutral line is excluded from the closed surface. This method can also overcome the impact of residual current changes when load switching, demonstrating the flexibility of the new principle. [ABSTRACT FROM AUTHOR]

The Caribbean & Mesoamerica Biogeochemical Isotope Overview (CAMBIO) is an archaeological data community designed to integrate published biogeochemical data from the Caribbean, Mesoamerica, and southern Central America to address questions about dynamic interactions among humans, animals, and the environment in the region over the past 10,000 years. Here we present the CAMBIO human dataset, which consists of more than 16,000 isotopic measurements from human skeletal tissue samples (δ13C, δ15N, δ34S, δ18O, 87Sr/86Sr, 206/204Pb, 207/204Pb, 208/204Pb, 207/206Pb) from 290 archaeological sites dating between 7000 BC to modern times. The open-access dataset also includes detailed chronological, contextual, and laboratory/sample preparation information for each measurement. The collated data are deposited on the open-access CAMBIO data community via the Pandora Initiative data platform (https://pandoradata.earth/organization/cambio). [ABSTRACT FROM AUTHOR]

Xylella Fastidiosa has been recently detected for the first time in southern Italy, representing a very dangerous phytobacterium capable of inducing severe diseases in many plants. In particular, the disease induced in olive trees is called olive quick decline syndrome (OQDS), which provokes the rapid desiccation and, ultimately, death of the infected plants. In this paper, we analyse about two thousands pixels of MODIS satellite evapotranspiration time series, covering infected and uninfected olive groves in southern Italy. Our aim is the identification of Xylella Fastidiosa-linked patterns in the statistical features of evapotranspiration data. The adopted methodology is the well-known Fisher–Shannon analysis that allows one to characterize the time dynamics of complex time series by means of two informational quantities, the Fisher information measure (FIM) and the Shannon entropy power (SEP). On average, the evapotranspiration of Xylella Fastidiosa-infected sites is characterized by a larger SEP and lower FIM compared to uninfected sites. The analysis of the receiver operating characteristic curve suggests that SEP and FIM can be considered binary classifiers with good discrimination performance that, moreover, improves if the yearly cycle, very likely linked with the meteo-climatic variability of the investigated areas, is removed from the data. Furthermore, it indicated that FIM exhibits superior effectiveness compared to SEP in discerning healthy and infected pixels. [ABSTRACT FROM AUTHOR]

Background: Studies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel‐wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention. Purpose: This study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel‐wise consistency for deep learning‐based MRI‐to‐CT synthesis. The method was expected to assist the multi‐modality studies for radiomics. Methods: The study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre‐procession. We proposed a gray‐level co‐occurrence matrix (GLCM)‐based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning‐based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function. Results: Compared with the baseline, the proposed method improved the pixel‐wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t‐test). Generally, > 90% (22/24) of the GLCM‐based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs. Conclusions: The proposed method reproduced texture features for MRI‐to‐CT synthesis, which would benefit radiomics studies based on image multi‐modality synthesis. [ABSTRACT FROM AUTHOR]