Information Metrics Research Articles

Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling.

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults' health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multi-time-scale topic modelling approach to quantitatively evaluate concern about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were firstly recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants' signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modelling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. Additionally, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult's active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.

Capacity at All-Way Stop Control Intersections: Case Study

This paper presents an empirical investigation into the capacity of all-way stop-controlled (AWSC) intersections. Video data was collected over four days at an AWSC intersection site in Bozeman, Montana. The site is characterized by single-lane approaches and high level of vehicular and pedestrian traffic. Using strict protocols, video records were processed at the individual vehicle level and several information metrics were extracted for each vehicle in the data set on all approaches. Study results indicate that the total intersection capacity at the study site varied between 400 and 1,400 vehicles per hour. The study suggests that the wide range of capacity observations is largely associated with the pedestrian crossing activity at the study site. Statistical tests confirmed that both pedestrian crossing activity and the level of conflict have significant effects on intersection capacity at the 95% confidence level. For movement type, the right-turn movement was not found to have a significant effect on intersection capacity while left-turn movement was found to negatively affect the intersection capacity. The results presented in this paper offer valuable information on AWSC intersection capacity, given the limited amount of information in the literature and the dated nature of those empirical observations.

Learning Distributed Representations and Deep Embedded Clustering of Texts

Instructors face significant time and effort constraints when grading students’ assessments on a large scale. Clustering similar assessments is a unique and effective technique that has the potential to significantly reduce the workload of instructors in online and large-scale learning environments. By grouping together similar assessments, marking one assessment in a cluster can be scaled to other similar assessments, allowing for a more efficient and streamlined grading process. To address this issue, this paper focuses on text assessments and proposes a method for reducing the workload of instructors by clustering similar assessments. The proposed method involves the use of distributed representation to transform texts into vectors, and contrastive learning to improve the representation that distinguishes the differences among similar texts. The paper presents a general framework for clustering similar texts that includes label representation, K-means, and self-organization map algorithms, with the objective of improving clustering performance using Accuracy (ACC) and Normalized Mutual Information (NMI) metrics. The proposed framework is evaluated experimentally using two real datasets. The results show that self-organization maps and K-means algorithms with Pre-trained language models outperform label representation algorithms for different datasets.

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Introduction to the Virtual Special Issue: Strategy Science’s Contributions to Doctoral Reading Lists

Introduction to the Virtual Special Issue: <i>Strategy Science</i>’s Contributions to Doctoral Reading Lists

Joint Optimization of Freshness and Fidelity for Status Updates in a Periodical Decision System

Development of applications in real-time service promotes more stringent freshness and fidelity requirements for the status update and decision systems. However, the freshness and fidelity of status updates are mutually restrictive, as fresh information requires timely transmission whereas high quantization precision incurs long transmission time. This work improves trade-off between the freshness and fidelity of status updates at periodical decision epochs by selecting appropriate transmission time and quantization precision. Specifically, the age of information (AoI) metric is used to assess data freshness, while distortion that consists of both static and dynamic parts is used to characterize the fidelity of status updates. We first formulate a joint AoI and distortion optimization problem. Then, the optimal transmission time and quantization precision are theoretically analyzed. We further minimize the sum of AoI and distortion by using power allocation strategy which selects a proper budget SNR of the wireless channel. Numerical results show that by using the optimized transmission time, quantization precision, associated with transmit power allocation, the overall freshness and fidelity of status updates can be improved remarkably.

Editorial: Continuing on an Inclusive Path to Scholarly Excellence with Renewed Vigor

Editorial: Continuing on an Inclusive Path to Scholarly Excellence with Renewed Vigor

Quantized Information in Spectral Cyberspace.

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time-frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA

The adoption of deep neural networks for profiling side-channel attacks opened new perspectives for leakage detection. Recent publications showed that cryptographic implementations featuring different countermeasures could be broken without feature selection or trace preprocessing. This success comes with a high price: an extensive hyperparameter search to find optimal deep learning models. As deep learning models usually suffer from overfitting due to their high fitting capacity, it is crucial to avoid over-training regimes, which require a correct number of epochs. For that, early stopping is employed as an efficient regularization method that requires a consistent validation metric. Although guessing entropy is a highly informative metric for profiling side-channel attacks, it is time-consuming, especially if computed for all epochs during training, and the number of validation traces is significantly large. This paper shows that guessing entropy can be efficiently computed during training by reducing the number of validation traces without affecting the efficiency of early stopping decisions. Our solution significantly speeds up the process, impacting the performance of the hyperparameter search and overall profiling attack. Our fast guessing entropy calculation is up to 16× faster, resulting in more hyperparameter tuning experiments and allowing security evaluators to find more efficient deep learning models.

Call for Papers: Service Science Special Issue: Navigating the Use of Technology in Service Marketing

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Call for Papers: Service Science Special Issue: Navigating the Use of Technology in Service MarketingGuest Editors: Haitao (Tony) Cui, Alison Jing Xu, Ting ZhuGuest Editors: Haitao (Tony) Cui, Alison Jing Xu, Ting ZhuPublished Online:17 Feb 2023https://doi.org/10.1287/serv.2023.0319New technologies have been transforming business models in many service industries. With the assistance of new technologies, firms can provide improved services or expand their services into new domains. For example, in retail, artificial intelligence (AI) has been used to make product recommendations. In the healthcare industry, AI algorithms help doctors detect conditions earlier and diagnose conditions more accurately; medical robots are programmed to perform procedures. In the education industry, cloud-based online meeting platforms remove the barrier of physical distance and provide consumers with access to educators all over the world. Consumers, conversely, are propelled to interact with technology-based service providers at different stages of their decision journey (such as recognizing their needs, searching for information, evaluating and comparing options, making decisions, and providing feedback). The goal of this special issue is to document the contributions of new technologies in transforming how consumers interact with service providers in the marketplace. The role of new technologies can be studied from different perspectives, including (but not exclusive to) consumers, service providers, competition, policy makers, and societal influences. We welcome papers using a variety of methodological approaches, including empirical modeling, theoretical analytics, and the experimental method.We encourage submissions on the following topics (nonexhaustive).How Does the Use of Technology Influence Consumer Behavior?▪ The influence of new technology adoption and innovation (e.g., chatbot, robot, virtual reality, and augmented reality) on consumer behavior (such as consumer decision-making in service contexts, service satisfaction, consumer engagement, etc.)▪ The influence of the use of technology in health-care services (e.g., virtual doctor visits; use of robots in medical services) on patients’ behavior▪ The factors that influence data-sharing intentions and data-privacy concerns in service contexts (e.g., on service platforms such as DoorDash)▪ How consumers react to AI-based recommendations▪ The impact of technology on consumers’ welfare, including the potential gains from the convenience and lower prices and the cost of data sharingHow Does the Use of Technology Influence Firm Behavior?▪ The impact of technology on firms’ performance in service contexts▪ Firms’ strategies for adopting and investing new technology to provide services▪ The effect of marketing analytics tools on the interactions between firms and customers in the service industry▪ The regulation of technology in the service sector▪ The adoption and impact of technology in the marketing of the shared economy▪ The impact of technology on the competitive environment in service industrySubmission Process and TimelineWe offer two tracks of submissions, both of which will undergo a double-blind peer review process. Submissions should be directed to the Service Science online submission system (https://mc.manuscriptcentral.com/serv). Criteria for acceptance include originality, contribution, and scientific merit. For submission guidelines, please see https://pubsonline.informs.org/page/serv/submission-guidelines.Regular TrackPapers for the regular track must be submitted by September 1, 2023. Decisions will typically be made after two rounds of review.▪ Regular track submission opening: March 1, 2023▪ Regular track submission deadline: September 1, 2023Fast TrackThe fast track option provides authors with early feedback on the fit of paper with this special issue. Authors are allowed to submit a long abstract of the paper (two to four pages, single spaced) by an early deadline of May 1, 2023. The editors will provide feedback on the fit of the paper with the special issue and offer recommendations for further development of the paper. Once the full paper is submitted by the deadline of September 1, 2023, it will undergo the review process, with a decision made after one round of review. The decision will be one of the following: accept, conditional accept, minor revision, or reject.▪ Fast track long abstract submission opening: March 1, 2023▪ Fast track long abstract submission deadline: May 1, 2023▪ Fast track full paper submission deadline: September 1, 2023 Previous Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:February 17, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Haitao (Tony) Cui, Alison Jing Xu, Ting Zhu (2023) Call for Papers: Service Science Special Issue: Navigating the Use of Technology in Service Marketing. Service Science 15(1):77-78. https://doi.org/10.1287/serv.2023.0319 PDF download

Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments.

This paper considers the appearance of indications of useful acoustic signals in the signal/noise mixture. Various information characteristics (information entropy, Jensen-Shannon divergence, spectral information divergence and statistical complexity) are investigated in the context of solving this problem. Both time and frequency domains are studied for the calculation of information entropy. The effectiveness of statistical complexity is shown in comparison with other information metrics for different signal-to-noise ratios. Two different approaches for statistical complexity calculations are also compared. In addition, analytical formulas for complexity and disequilibrium are obtained using entropy variation in the case of signal spectral distribution. The connection between the statistical complexity criterion and the Neyman-Pearson approach for hypothesis testing is discussed. The effectiveness of the proposed approach is shown for different types of acoustic signals and noise models, including colored noises, and different signal-to-noise ratios, especially when the estimation of additional noise characteristics is impossible.

Techno-economic model-based design space exploration of ‘combined’ ship propulsion systems

The architecture of a ship propulsion system, developed during early stages of the overall ship design process, has a very large impact on the overall design and performance of the ship. The design space exploration to arrive at the final ship propulsion architecture can be a rather complex process for high-performance 'combined' ship propulsion systems designed to achieve multiple, often conflicting, design objectives. This paper proposes a novel process for the process of design space exploration based on a model-based ‘Techno-economic & Environmental Risk Assessment’ (TERA) approach, executed using a hybrid ‘Multiple-Criteria Decision-Making’ (MCDM) procedure, to select a compromise solution from competing propulsion system architectures populating the design space. The process utilizes a combination of performance data generated from performance simulation of developed models, as well as comparative expert opinions-based metrics for information not available early in the ship design process for selection of a 'compromise solution'. The paper includes an illustrative example of application of the proposed process for design space exploration for a combined propulsion system architecture for a notional destroyer.

REACTMIN: Reactive scanning based single particle tracking using a minimum of light.

Real-time feedback-driven single particle tracking (RT-FD-SPT) is an emerging method of measuring the state and dynamics of individual molecules and particles at the nanometer scale as they move inside living cells. Unlike wide field methods, which passively image a particle and extract trajectory information through post-processing, RT-FD-SPT methods limit the field of view to a single particle and then actively track that particle with high spatiotemporal resolution as it moves in three dimensions. Two problems limit the performance of these systems: the photon budget of fluorescent labels, and the control temporal budget. We propose a new control technique called REACTMIN that overcomes these challenges by reactively scanning a minimum of light to track diffusing particles and molecules. It is comprised of a nonlinear, extremum seeking based controller combined with structured illumination to generate a donut-shaped excitation beam. The controller allows for tracking without the need for online estimation, addressing the control temporal budget, while the structured illumination enables information rich measurements, addressing the photon budget. We use two metrics of performance, Fisher information and tracking duration, to characterize the system and find optimal settings controller parameters. We demonstrate that there is a trade-off between tracking duration and localization precision and discuss how to select parameters in the context of specific experimental aims.

Quality Metric to Assess Adequacy of Hydrogel Rectal Spacer Placement for Prostate Radiation Therapy and Association of Metric Score With Rectal Toxicity Outcomes

Although hydrogel spacer placement (HSP) minimizes rectal dose during prostate cancer radiation therapy, its potential benefit for modulating rectal toxicity could depend on the achieved prostate-rectal separation. We therefore developed a quality metric associated with rectal dose reduction and late rectal toxicity among patients treated with prostate stereotactic body radiation therapy (SBRT). A quality metric consisting of prostate-rectal interspace measurements from axial T2-weighted magnetic resonance imaging simulation images was applied to 42 men enrolled in a multi-institutional phase 2 study using HSP with prostate SBRT (45 Gy in 5 fractions). A score of 0, 1, or 2 was assigned to a prostate-rectal interspace measurement of <0.3 cm, 0.3 to 0.9 cm, or ≥1 cm, respectively. An overall spacer quality score (SQS) was computed from individual scores at rectal midline and ±1 cm laterally, located at the prostate base, midgland, and apex. Associations of SQS with rectal dosimetry and late toxicity were evaluated. The majority of the analyzed cohort had an SQS of 1 (n=17; 41%) or 2 (n=18; 43%). SQS was associated with maximum rectal point dose (rectal Dmax; P=.002), maximum dose to 1 cc of rectum (D1cc; P=.004), and volume of rectum receiving ≥100% of prescription dose (V45; P=.046) and ≥40 Gy (V40; P=.005). SQS was also associated with a higher incidence of (P=.01) and highest-graded late rectal toxicity (P=.01). Among the 20 men who developed late grade ≥1 rectal toxicity, 57%, 71%, and 22% had an SQS of 0, 1, and 2, respectively. Men with an SQS of 0 or 1 compared with 2 had 4.67-fold (95% CI, 0.72-30.11) or 8.40-fold (95% CI, 1.83-38.57) greater odds, respectively, of developing late rectal toxicity. We developed a reliable and informative metric for assessing HSP, which appears to be associated with rectal dosimetry and late rectal toxicity after prostate SBRT.

Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection.

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.

Development and assessment of an epidemiologic dashboard for surveillance of Varroa destructor in Ontario apiaries

Varroosis (caused by the Varroa destructor mite) is a key health issue for honey bees in North America. Because these mites can exist in reservoirs of feral honey bee colonies, eradication is impossible, and instead efforts are made to maintain mites below a critical threshold. Monitoring for Varroa mites within a population is key for allocating resources and targeting interventions but surveillance can be difficult and/or expensive. This project aims to reflect on the success of data dashboards developed throughout the 2019-coronavirus pandemic and showcase how these methods can improve surveillance of Varroa mite infestations in Ontario, Canada. Dashboards provide a consistent source of information and epidemiologic metrics through data visualizations, and mobilize data otherwise bound to tables and intermittent reports. In the present work, an interactive dashboard for the surveillance of Varroa mite infestations across the province is proposed. This dashboard was developed using routine ministry inspection data to depict the spatio-temporal distribution of mites across a five-year data collection period. Through interactive figures and plots, able to be disaggregated to a specific region and time frame, this dashboard will allow for members of the beekeeping community to monitor provincial mite levels throughout the season. Seven criteria found to be common across highly actionable COVID-19 dashboards were used in a beta testing stage of development to assess the quality of the dashboard, and critically reflect on its strengths and weaknesses. Furthermore, future directions for surveillance dashboards are explored, including integration with citizen science data collection to develop a comprehensive province-wide surveillance system. The outcome of this project is a functional dashboard proof-of-concept for population-level monitoring of Varroa mites and a model for future tools designed for other species and diseases.

Local reference and achievable dose levels for vascular and enterostomy access procedures in pediatric interventional radiology.

Knowledge of radiation quantities delivered in routine practice is an essential responsibility of a pediatric interventional radiology department. To review radiation indices in frequently performed vascular and enterostomy access procedures at a quaternary pediatric hospital to formulate dosimetric reference levels and achievable levels. A retrospective review of patient demographics, procedure information and quantitative dose metrics over a 2-year period was performed. Dosimetric details forcommon procedures (central line insertions, gastrostomy/gastrojejunostomy insertions and maintenance) were evaluated, correlated with demographic data and stratified acrossfive weight groups (0-5kg, 5-15kg, 15-30kg, 30-50kg, 50-80kg).Achievable (50th percentile) and reference (75thpercentile)levels with confidence intervals were established for each procedure. Within the evaluation period, 3,165 studies satisfied the inclusion criteria. Five were classified as device insertions (peripherally inserted central catheter, n=1,145; port-a-catheter, n=321; central venous line, n=285; gastrostomy-tube [G-tube], n=262, and gastrojejunostomy-tube [GJ-tube], n=66),and two were classified as maintenance procedures (G-tube, n=358, and GJ-tube, n=728, checks, exchanges and reinsertions). Representative reference and achievable levels were calculated for each procedure category and weight group. This work highlights the creation of local reference and achievable levels for common pediatric interventional procedures. These data establish a dosimetric reference to understand the quantity of radiation routinely applied, allowing for improved relative radiation risk assessment and enriched communication to interventionalists, health care providers, parents and patients.

Identifying Shared Neuroanatomic Architecture between Cognitive Traits through Multiscale Morphometric Correlation Analysis.

We introduce an informative metric, called morphometric correlation, as a measure of shared neuroanatomic similarity between two cognitive traits. Traditional estimates of trait correlations can be confounded by factors beyond brain morphology. To exclude these confounding factors, we adopt a Gaussian kernel to measure the morphological similarity between individuals and compare pure neuroanatomic correlations among cognitive traits. In our empirical study, we employ a multiscale strategy. Given a set of cognitive traits, we first perform morphometric correlation analysis for each pair of traits to reveal their shared neuroanatomic correlation at the whole brain (or global) level. After that, we extend our whole brain concept to regional morphometric correlation and estimate shared neuroanatomic similarity between two cognitive traits at the regional (or local) level. Our results demonstrate that morphometric correlation can provide insights into shared neuroanatomic architecture between cognitive traits. Furthermore, we also estimate the morphometricity of each cognitive trait at both global and local levels, which can be used to better understand how neuroanatomic changes influence individuals' cognitive status.

Performance Analysis of Spectrum Sharing Radar in Multipath Environment

Radar based sensing and communication systems sharing a common spectrum have become a potential research problem in recent years due to spectrum scarcity. The spectrum sharing radar (SSR) is a new technology that uses the total available bandwidth (BW) for both radar based sensing and communication. Unlike traditional radar, the SSR divides the total available BW into radar-only and mixed-use bands. In a radar-only band, only radar sensor signals can be transmitted and received. In contrast, radar and communication signals can both be transmitted and received in the mixed-use band. Taking such BW sharing into account, this paper investigates the performance of SSR in an information-theoretic sense. To evaluate performance, mutual information (MI), spectral efficiency (SE) and capacity (C) metrics are used. Initially, this paper considered a clean environment (no multipath) in order to evaluate performance metrics in the mixed-use band with and without successive interference cancellation. Following that, this paper addresses the performance of BW allocation by allocating low to high BW in mixed-band. Furthermore, the performance metrics are extended to account for the multipath environment, and the same analogy as in a clean environment is used. In addition, the MI and SE of traditional radar system is taken into account when comparing the performance of SSR with and without the use of the SIC. Finally, MI and capacity results show that using the SIC scheme in a mixed-use band yields performance comparable to traditional radar and communication system. In terms of SE, the SSR with SIC scheme outperforms traditional radar and communication system.

Association between mean platellet volume levels and mortality outcomes in geriatric patients in intensive care

To prospectively evaluate the relationship between Mean Platelet Volume (MPV) levels and short-term mortality outcomes in critically ill patients aged ≥85 years. We hypothesized that abnormal MPV levels could offer actionable insights for predicting mortality risk in this highly vulnerable group, thereby improving therapeutic decision-making processes in the ICU setting. This study included patients aged ≥85 years. The participants were categorized into two groups based on their MPV levels at admission: group 1 (MPV≤12.5, normal range) and group 2 (MPV&gt;12.5, elevated).A comprehensive set of variables, including demographic information, clinical indices, biochemical parameters, and respiratory metrics, were recorded. In a cohort of 475 participants, patients were stratified into two distinct groups based on MPV values: group 1 (n=400, 84.2%) and group 2 (n=75, 15.8%). The mean age was 89 (SD=7.7) and 88 (SD=6.2) years for group 1 and group 2, respectively, with a statistically significant difference (p=0.035). Significant differences were found in key biochemical parameters such as urea (p=0.045) and sodium (p=0.005). The most profound disparity was observed in platelet counts, with a highly significant p-value &lt;0.001. The clinical outcomes showed a substantial divergence in the 30-day mortality rates between the two MPV-based cohorts (p&lt;0.001). Additionally, a higher incidence of sepsis was recorded in the Group 2 (p=0.024). MPV is a significant prognostic indicator for mortality, 30-day mortality, and sepsis incidence, advocating its inclusion in routine clinical assessments to optimize patient care. Routinely accessible platelet indices serve as robust indicators for assessing both morbidity and mortality in critically ill patients.