Measurement Of Information Research Articles

Uncertainty-aware autonomous sensing with deep reinforcement learning

Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics that are task-specific and often non-adaptive. In this work, we propose reducing manual-engineering efforts in designing sensing policies by using an automated approach based on deep reinforcement learning. Guided by an uncertainty-aware prediction model, the sensors learn sensing behaviors autonomously by optimizing an application goal formulated in the reward function based on the measured peaks-over-threshold. We apply the proposed approach in two use cases of monitoring air quality and indoor noise and show the adaptability and transferability of the learned policies. Compared to conventional periodic sensing methods, our results achieve, on average, an increased detection in periods of interest by 78.5% and 357.3% while reducing energy expenditure by 14.3% and 7.6% for air quality and noise monitoring, respectively. Additionally, the resulting representation models are more credible, as measured by various metrics of probabilistic modeling.

Feature selection for classification with Spearman’s rank correlation coefficient-based self-information in divergence-based fuzzy rough sets

Feature selection facilitates uncertainty disposal and information mining, and it has received widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind of fuzzy rough sets, have been applied to feature selection and induced two efficient algorithms, FS-AFS and FS-RFS. Nevertheless, FS-AFS and FS-RFS still have advancement space, because the dependency functions only focus on lower approximation and ignore the uncertainty in upper approximation, which will certainly undermine the algorithmic evaluation effects. To this end, this paper introduces the upper approximation and fuses it with lower approximation via class-specific pattern and Spearman coefficient to construct a new information measurement for metric perfection, called Spearman-based self-information. Relying on this new measurement, a novel feature selection algorithm SPESI is established to improve FS-AFS and FS-RFS. At first, Spearman coefficient is introduced and upper approximation is defined. Second, class-driven precision and roughness are built by incorporating Spearman coefficient-based class-specific weight vectors with class-driven lower and upper approximations. Meanwhile, the granulation monotonicity of newly-defined measurements is also explored. Then, the core measurement Spearman-based self-information is firstly given and its feature significance motivates a feature selection algorithm SPESI with heuristic search. Finally, data experiments are implemented to validate the effectiveness of SPESI, and a conclusion can be drawn that SPESI outperforms FS-AFS and FS-RFS to acquire better classification performances with fewer feature numbers and less running time.

Interrelatedness of thermodynamics and information: transformation of heat as a measurable information process and quantity, an overview

No one has yet investigated the information reach and influence of thermal analysis. Even so the heat and entropy are bound closely. The very problem of the term entropy is that it was defined specifically within the classical, equilibrium Thermodynamics like Clausius entropy, while its meaning is more general. It is usable in all areas of physics, in the communication theory and technology, and also, in social sciences, just as an universal quantitative model. The bridge to this wider world is in Boltzmann’s statistical point of view used in the Shannon’s information point of view. The difference is that the Shannon entropy is defined more generally, both for the uniform and for the not uniform probability distributions. But with renormalization and with the Boltzmann constant we can speak, using the Clausius entropy, about a certain thermodynamic system considered as a heat model of the area of our wider interests. So we will to deal with the heat and its entropy embodied to a wider consideration. In addition to the standard physical meaning, another thermodynamic realization in the form of a message is derived from Shannon’s information theory, where the sample carries a measure of Shannon’s information given by its structure or internal organization. This is also a contribution to improving the approach to thinking about the importance of observation and measurement, reflecting at the same time the costs and influence of the measurement itself and its organization on the measured object or, on its environment or, even on the environment of the measuring itself generally. Statistical sorting also carries with it the internal movement of individual elementary particles, the value of which indicates the resulting state of heat transfer and dissipation to and from the system providing a state of called equilibrium. It is statistically described by a weighted quantity called temperature, the changes of which via momentum alteration satisfy Newton’s laws of inertia allowing enumeration of the related heat information processing. The terminology used is still non-institutional.

Agreement between parent-report and EMR height, weight, and BMI among rural children.

Remote anthropometric surveillance has emerged as a strategy to accommodate lapses in growth monitoring for pediatricians during coronavirus disease 2019 (COVID-19). The purpose of this investigation was to validate parent-reported anthropometry and inform acceptable remote measurement practices among rural, preschool-aged children. Parent-reported height, weight, body mass index (BMI), BMI z-score, and BMI percentile for their child were collected through surveys with the assessment of their source of home measure. Objective measures were collected by clinic staff at the child's well-child visit (WCV). Agreement was assessed using correlations, alongside an exploration of the time gap (TG) between parent-report and WCV to moderate agreement. Using parent- and objectively reported BMI z-scores, weight classification agreement was evaluated. Correction equations were applied to parent-reported anthropometrics. A total of 55 subjects were included in this study. Significant differences were observed between parent- and objectively reported weight in the overall group (-0.24 kg; p = 0.05), as well as height (-1.8 cm; p = 0.01) and BMI (0.4 kg/m2; p = 0.02) in the ≤7d TG + Direct group. Parental reporting of child anthropometry ≤7d from their WCV with direct measurements yielded the strongest correlations [r = 0.99 (weight), r = 0.95 (height), r = 0.82 (BMI), r = 0.71 (BMIz), and r = 0.68 (BMI percentile)] and greatest classification agreement among all metrics [91.67% (weight), 54.17% (height), 83.33% (BMI), 91.67% (BMIz), and 33.33% (BMI percentile)]. Corrections did not remarkably improve correlations. Remote pediatric anthropometry is a valid supplement for clinical assessment, conditional on direct measurement within 7 days. In rural populations where socioenvironmental barriers exist to care and surveillance, we highlight the utility of telemedicine for providers and researchers.

Multitask Image Clustering via Deep Information Bottleneck.

Multitask image clustering approaches intend to improve the model accuracy on each task by exploring the relationships of multiple related image clustering tasks. However, most existing multitask clustering (MTC) approaches isolate the representation abstraction from the downstream clustering procedure, which makes the MTC models unable to perform unified optimization. In addition, the existing MTC relies on exploring the relevant information of multiple related tasks to discover their latent correlations while ignoring the irrelevant information between partially related tasks, which may also degrade the clustering performance. To tackle these issues, a multitask image clustering method named deep multitask information bottleneck (DMTIB) is devised, which aims at conducting multiple related image clustering by maximizing the relevant information of multiple tasks while minimizing the irrelevant information among them. Specifically, DMTIB consists of a main-net and multiple subnets to characterize the relationships across tasks and the correlations hidden in a single clustering task. Then, an information maximin discriminator is devised to maximize the mutual information (MI) measurement of positive samples and minimize the MI of negative ones, in which the positive and negative sample pairs are constructed by a high-confidence pseudo-graph. Finally, a unified loss function is devised for the optimization of task relatedness discovery and MTC simultaneously. Empirical comparisons on several benchmark datasets, NUS-WIDE, Pascal VOC, Caltech-256, CIFAR-100, and COCO, show that our DMTIB approach outperforms more than 20 single-task clustering and MTC approaches.

Proposed Specifiers for Conduct Disorder (PSCD): Further validation of the parent-report version in a nationally representative U.S. sample of 10- to 17-year-olds.

The Proposed Specifiers for Conduct Disorder (PSCD; Salekin & Hare, 2016) is a new self-report and informant measure designed to assess psychopathic characteristic domains along with symptoms of conduct disorder in youth. Previous factor analytic studies on the PSCD have found that the items are accounted for by a four-factor model reflecting grandiose-manipulative, callous-unemotional, daring-impulsive, and conduct disorder (CD) symptoms. The present study examined the factor structure, psychometric properties, and criterion-related validity of the parent-report version of the PSCD (PSCD-P) in a nationally representative U.S. sample of children and adolescents (N = 1,091, Mage = 13.39, SD = 2.20, range age = 10-17; 50.0% boys, 76% White). Confirmatory factor analyses for the full (24-item) and a shortened (13-item) PSCD-P revealed good internal reliability estimates and support for the four-factor model (grandiose-manipulative, callous-unemotional, daring-impulsive, CD). Results also provided evidence for (a) measurement invariance of the PSCD-P items across sex, race/ethnicity, and age of the child; (b) convergent validity with CD/oppositional defiant disorder symptoms and discriminant validity with a measure of neuroticism; and (c) criterion-related validity with respect to prosociality, peer and family functioning, reactive and proactive aggression, delinquency, academic performance, and substance use. The prevalence for psychopathic personality propensity was found to be 2%. We discuss clinical and research implications regarding the use of the parent-report version of the PSCD for school-aged children. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effect of aerobic exercise on anthropometric parameters among Indian primary school children.

Childhood obesity is a global public health concern with significant implications for long-term health. This study addresses the rising rates of obesity among school-age children (10-12 years) and investigates the effectiveness of aerobic exercise interventions in improving anthropometric parameters, specifically focusing on BMI and mid-arm circumference (MAC). The study emphasizes the role of schools in shaping children's behaviors and aims to contribute empirical evidence to inform health promotion strategies for this demographic. The research employs a quasi-experimental design, involving 60 school-age children in Visnagar, Gujarat, India. The 12-week aerobic exercise intervention, conducted three times a week, comprises activities like running, jumping jacks, and dance routines. Data collection includes sociodemographic information, BMI, and MAC measurements. The study design, participant criteria, and intervention details are carefully outlined. Socio-demographic variables such as age and monthly family income significantly influence BMI, highlighting the importance of considering these factors in interventions. Pretest results show 80% of children classified as overweight, reducing to 58.3% post-intervention. The mean BMI significantly decreases from 24.41 to 22.84 (p < 0.05), indicating the positive impact of aerobic exercise. The study also explores the association between BMI, MAC, and socio-demographic variables through chi-square tests. Data shows the prevalence of overweight and obesity among school-age children and demonstrates the effectiveness of a 12-week aerobic exercise program in improving BMI. Findings align with existing literature on the positive impact of physical activity on weight management in children.

A systematic review of studies on fine and coarse root traits measurement: towards the enhancement of urban forests monitoring and management

The analysis of fine and coarse roots’ functional traits has the potential to reveal the performance of the root system, which is pivotal in tree growth, development, and failure in both natural and urban forest ecosystems. Furthermore, root traits may be a powerful indicator of tree resilience mechanisms. However, due to the inherent difficulties in measuring ‘the hidden half,’ and despite the recent advancements, the relationships among root functional traits and biotic and abiotic drivers still suffer from a lack of information. Thus, our study aimed to evidence knowledge milestones and gaps and to categorize, discuss, and suggest future directions for effective experimental designs in fine and coarse root studies. To this end, we conducted a systematic literature review supported by backward manual referencing based on 55 root functional traits and 136 plant species potentially suitable for afforestation and reforestation of natural and urban forest ecosystems. The majority of the 168 papers on fine and coarse root studies selected in our review focused predominantly on European natural contexts for a few plant species, such as Fagus sylvatica, Picea abies, Pinus sylvestris, and Pinus cembra, and root functional traits such as standing biomass, phenology production, turnover rate, and non-structural carbohydrates (NSC). Additionally, the analyzed studies frequently lack information and uniformity in experimental designs, measurements, and statistical analysis, highlighting the difficult integration and comparison of outcomes derived from different experiments and sites. Moreover, no information has been detected in selected literature about urban forest ecosystems, while most of the studies focus on natural forests. These biases observed during our literature analysis led us to give key indications for future experiment designs with fine and coarse roots involved, which may contribute to the building up of common protocols to boost the monitoring, managing, and planning of afforestation and reforestation projects.

Possible Integration of Soil Information into Land Degradation Analysis for the United Nations (UN) Land Degradation Neutrality (LDN) Concept: A Case Study of the Contiguous United States of America (USA)

Soil makes important contributions to the United Nations (UN) Land Degradation Neutrality (LDN) concept and targets; however, currently, soil is not integrated into measurable information (e.g., indicators, metrics) to monitor land degradation (LD) patterns and trends. This study examines the role of soil in LDN in the UN Convention to Combat Desertification (UNCCD), and UN Sustainable Development Goal (SDG 15: Life on Land). This study is specifically focused on the LDN and biodiversity loss as they relate to an indicator 15.3.1 Proportion of land that is degraded over total land area. Tracking of LD status can be improved by using detailed soils databases combined with satellite-derived land cover maps. This study has applied these newly improved methods to quantify and map the anthropogenic LD status and trends in the contiguous United States of America (USA), as well as to identify potential land areas for nature-based solutions (NBS) to compensate for LD. Anthropogenic LD in 2016 in the contiguous USA affected over two million square kilometers, about one-third of the country’s total area, with high variability by state. Between 2001 and 2016, LD in the USA showed an overall increase of 1.5%, with some states exhibiting increases in degraded land while other states had overall improvements to their land. All ten soil orders present in the contiguous USA have been anthropogenically degraded, with Mollisols, Alfisols, and Vertisols having the highest LD levels. Compensating for LD requires a variety of strategies and measures (e.g., NBS), which often require additional land. In 2016, the potential land area for NBS was over two million square kilometers, an area approximately equal to that of degraded land. Some of the states that have high proportions of land available for potential NBS are dominated by soils (Aridisols) typical of deserts and therefore may have less promise for NBS. The variability of LD needs to be evaluated at finer spatial scales for realistic LDN analysis.

Transferable Deep Learning Model for the Identification of Fish Species for Various Fishing Grounds

The digitization of catch information for the promotion of sustainable fisheries is gaining momentum globally. However, the manual measurement of fundamental catch information, such as species identification, length measurement, and fish count, is highly inconvenient, thus intensifying the call for its automation. Recently, image recognition systems based on convolutional neural networks (CNNs) have been extensively studied across diverse fields. Nevertheless, the deployment of CNNs for identifying fish species is difficult owing to the intricate nature of managing a plethora of fish species, which fluctuate based on season and locale, in addition to the scarcity of public datasets encompassing large catches. To overcome this issue, we designed a transferable pre-trained CNN model specifically for identifying fish species, which can be easily reused in various fishing grounds. Utilizing an extensive fish species photographic database from a Japanese museum, we developed a transferable fish identification (TFI) model employing strategies such as multiple pre-training, learning rate scheduling, multi-task learning, and metric learning. We further introduced two application methods, namely transfer learning and output layer masking, for the TFI model, validating its efficacy through rigorous experiments.

Stabilizing translucencies: Governing AI transparency by standardization

Standards are put forward as important means to turn the ideals of ethical and responsible artificial intelligence into practice. One principle targeted for standardization is transparency. This article attends to the tension between standardization and transparency, by combining a theoretical exploration of these concepts with an empirical analysis of standardizations of artificial intelligence transparency. Conceptually, standards are underpinned by goals of stability and solidification, while transparency is considered a flexible see-through quality. In addition, artificial intelligence-technologies are depicted as ‘black boxed’, complex and in flux. Transparency as a solution for ethical artificial intelligence has, however, been problematized. In the empirical sample of standardizations, transparency is largely presented as a static, measurable, and straightforward information transfer, or as a window to artificial intelligence use. The standards are furthermore described as pioneering and able to shape technological futures, while their similarities suggest that artificial intelligence translucencies are already stabilizing into similar arrangements. To rely heavily upon standardization to govern artificial intelligence transparency still risks allocating rule-making to non-democratic processes, and while intended to bring clarity, the standardizations could also create new distributions of uncertainty and accountability. This article stresses the complexity of governing sociotechnical artificial intelligence principles by standardization. Overall, there is a risk that the governance of artificial intelligence is let to be too shaped by technological solutionism, allowing the standardization of social values (or even human rights) to be carried out in the same manner as that of any other technical product or procedure.

The performance of AC-coupled Strip LGAD developed by IHEP

The AC-coupled Strip LGAD (Strip AC-LGAD) is a novel LGAD design that diminishes the density of readout electronics through the use of strip electrodes, enabling the simultaneous measurement of time and spatial information. The Institute of High Energy Physics has designed a long Strip AC-LGAD prototype with a strip electrode length of 5.7 mm and pitches of 150 μm, 200 μm, and 250 μm. Spatial and timing resolutions of the long Strip AC-LGAD are studied by pico-second laser test and beta source tests. The laser test demonstrates that spatial resolution improves as the pitch size decreases, with an optimal resolution achieved at 8.3μm. Furthermore, the Beta source test yields a timing resolution of 37.6 ps.

Environment-independent textile fiber identification using Wi-Fi channel state information

Textile fiber identification is a technique that can help identify the type of target textile fiber. Existing methods usually rely on expensive detection instruments, specialized researchers, and complex processing techniques. The large number of textile fibers makes it difficult for researchers to use a stable and fast method for identification. This paper introduces a textile fiber identification method based on Wi-Fi signals, and at the same time, in the actual measurement, the signal characteristics of Wi-Fi are usually interfered with by the hardware noise and multipath propagation of channel state information (CSI) measurement equipment. To eliminate the inherent noise of CSI, we designed a denoising method based on the CSI data acquisition of textile fiber samples in independent environments. Then, the features of Wi-Fi signal wavelet packet decomposition could be extracted more stably, and the principal component analysis (PCA) method was used to reduce the data dimension. Finally, the convolutional neural network (CNN) was used to classify the data features. We conducted extensive experiments to verify the effectiveness of the proposed method. The results show that the proposed method can identify all 14 kinds of common textile fibers used in the experiment, and the average accuracy is 93.25%.

Multiexposure Fusion Based on Multiscale Edge Information Measurement and Adaptive Pyramid Optimization

Multiexposure Fusion Based on Multiscale Edge Information Measurement and Adaptive Pyramid Optimization

Impact of in utero tobacco exposure on fetal growth: Amount of exposure and second trimester fetal growth measurements

BackgroundResearch reveals small and inconsistent findings linking prenatal tobacco exposure and early fetal growth, but failure to consider confounding and amount of exposure many explain inconsistencies. GoalTo examine whether fetal growth effects following exposure to tobacco are evident in the second trimester, specific to certain growth parameters, and dose dependent. MethodsParticipants were pregnant women (64 smokers, 100 controls) with no other drug use. Available data included background/medical information and ultrasound measurements coded as percentiles. ResultsControlling for background differences, 10+ cig/day predicted a 10+ percentile point reduction in estimated fetal weight, femur length, head circumference, and biparietal diameter compared to non-exposed controls. Exposure to <10 cig/day predicted significant reduction in only biparietal diameter. Exposure was unrelated to abdominal circumference. ConclusionsResults demonstrate utility of considering amount of exposure when examining/quantifying fetal growth effects, and suggest even reduction in early pregnancy smoking may positively benefit aspects of fetal development.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan.

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

CROSE: Low-light enhancement by CROss-SEnsor interaction for nighttime driving scenes

An increasing number of image perception models are being utilized in the field of autonomous driving. During nighttime driving, the visual perception capabilities of a single-modality RGB sensor become compromised. To address this issue, image fusion methods that utilize multi-modality data to provide a more comprehensive visual representation for nighttime scenes. Nevertheless, the existing image fusion methods are limited by poor generalization abilities and lighting conditions, resulting in inadequate handling of the contrast and spectral corruption. In this paper, we propose a cross-sensor low-light enhancement algorithm that provides more accurate visual perceptions. Our approach employs multiple sensors as preceptors for nighttime driving scenes. Compared with typical fusion algorithms that use a simple one-stage workflow for cross-sensor data, our proposed method adopts an advanced content-enhancement strategy by recursively and interactively scaling up informative pixels. More specifically, our approach employs information measurement to describe the information and then utilizes a cross-sensor content enhancement module to dynamically enhance the mutual information between the infrared spectrum and the RGB streams. Our experiments show that CROSE effectively preserves texture details, resulting in clearer and more solid fusion results for nighttime driving scenes.

The confined helium atom: An information–theoretic approach

AbstractIn this article, we study the helium atom confined in a spherical impenetrable cavity by using informational measures. We use the Ritz variational method to obtain the energies and wave functions of the confined helium atom as a function of the cavity radius . As trial wave functions we use one uncorrelated function and five explicitly correlated basis sets in Hylleraas coordinates with different degrees of electronic correlation. We computed the Shannon entropy, Fisher information, Kullback–Leibler entropy, Tsallis entropy, disequilibrium and Fisher–Shannon complexity, as a function of . We found that these entropic measures are sensitive to electronic correlation and can be used to measure it. As expected these entropic measures are less sensitive to electron correlation in the strong confinement regime ( a.u.).

Association between zinc-alpha 2-glycoprotein and lipid profile among pregnant women with gestational diabetes

Background: Gestational diabetes is a condition that occurs during the latter stages of pregnancy and is marked by notable resistance to insulin due to the release of hormones by the placenta. Zinc alpha 2-glycoprotein (ZAG) is an intriguing protein due to its ability to perform numerous essential functions in the human body. Prior research has linked ZAG to lipid metabolism. Objectives: This study was designed to evaluate the ZAG level in GDM pregnant women undergoing different therapeutic modalities. Furthermore, the study examines the correlation between ZAG and lipid parameters. Method: The study included a sample of 76 pregnant women aged 18 to 40 years. A total of 22 pregnant women without any health issues (Group 1), 30 pregnant women recently diagnosed with GDM (Group 2), and 24 pregnant women with GDM who were receiving various treatments (Group 3) were included in the study. All participants were in their second or third trimester of pregnancy, and their demographic information and lipid measurements were assessed. Results: There was no statistically significant difference in ZAG levels among study groups (P ≥ 0.05); hence, GDM pregnant women in Group 2 showed the highest level. In GDM pregnant women who received treatment, there was a significant difference in ZAG levels among subgroups (p&lt;0.01). The metformin group had the lowest level of ZAG. The high-density lipoprotein and triglyceride levels were significantly higher in GDM-pregnant women in Groups 2 and 3 (p&lt;0.05). Only in Group 3, ZAG levels and other lipid parameters (total cholesterol, high-density lipoprotein, low-density lipoprotein, triglyceride, and very low-density lipoprotein) were highly statistically significantly correlated with each other (p&lt;0.01), except for triglyceride levels, where there was no correlation between them (p≥0.05). Conclusion: The current findings revealed that ZAG levels are altered in GDM pregnant women and are beneficial indicators for metabolic status during treatment. ZAG levels correlate well with the lipid profile in GDM pregnant women who received treatment.

Exploring the chain mediating roles of frailty and depressive symptoms in the relationship between pain and cognitive function among nursing home older adults.

The prevalence of cognitive decline is high among nursing home older adults. Pain is a vital factor in cognitive function. Furthermore, the current literature lacks the complex association between pain, frailty, depressive symptoms, and cognitive function. The aim of this study was to explore the chain mediating roles of frailty and depressive symptoms in the association between pain and cognitive function among nursing home older adults. This is a population-based cross-sectional study, conducted in China, of 210 nursing home older adults aged 64-98 years old, who completed the measurements of sociodemographic information, pain, frailty, depressive symptoms, and cognitive function. Mediation analyses tested the indirect effect of frailty and depressive symptoms in the relationship between pain and cognitive function by PROCESS macro. Pain, frailty, as well as depressive symptoms, were negatively related to cognitive function. Frailty mediated the association between pain and cognitive function. Importantly, mediation analyses showed that frailty and depressive symptoms acted as sequential mediators of pain and cognitive function. These findings have crucial clinical implications, as they suggest targeting physiological and psychological factors in older adults with chronic pain to alleviate cognitive decline.