Dynamic Online Research Articles

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy

BackgroundBecause spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen.MethodsFrom September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model.ResultsA total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort.ConclusionsThe dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient’s prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.

A dynamic online nomogram predicting prostate cancer short-term prognosis based on 18F-PSMA-1007 PET/CT of periprostatic adipose tissue: a multicenter study.

Rising prostate-specific antigen (PSA) levels following radical prostatectomy are indicative of a poor prognosis, which may associate with periprostatic adipose tissue (PPAT). Accordingly, we aimed to construct a dynamic online nomogram to predict tumor short-term prognosis based on 18F-PSMA-1007 PET/CT of PPAT. Data from 268 prostate cancer (PCa) patients who underwent 18F-PSMA-1007 PET/CT before prostatectomy were analyzed retrospectively for model construction and validation (training cohort: n = 156; internal validation cohort: n = 65; external validation cohort: n = 47). Radiomics features (RFs) from PET and CT were extracted. Then, the Rad-score was constructed using logistic regression analysis based on the 25 optimal RFs selected through maximal relevance and minimal redundancy, as well as the least absolute shrinkage and selection operator. A nomogram was constructed to predict short-term prognosis which determined by persistent PSA. The Rad-score consisting of 25 RFs showed good discrimination for classifying persistent PSA in all cohorts (all P < 0.05). Based on the logistic analysis, the radiomics-clinical combined model, which contained the optimal RFs and the predictive clinical variables, demonstrated optimal performance at an AUC of 0.85 (95% CI: 0.78-0.91), 0.77 (95% CI: 0.62-0.91) and 0.84 (95% CI: 0.70-0.93) in the training, internal validation and external validation cohorts. In all cohorts, the calibration curve was well-calibrated. Analysis of decision curves revealed greater clinical utility for the radiomics-clinical combined nomogram. The radiomics-clinical combined nomogram serves as a novel tool for preoperative individualized prediction of short-term prognosis among PCa patients.

Adaptive Impedance Control for Teleoperation with Event-Triggered Controller

Uncertainty in the master–slave model is one of the primary factors affecting the transparency of teleoperation systems, and congestion in the master–slave communication network also greatly influences the performance of the teleoperation system. This paper proposes a combined framework of adaptive and impedance control to address the uncertainty in the master–slave model and achieve smooth operation at the slave end. Building upon this linear model, an event-triggered mechanism is designed using Lyapunov functions, with dynamic online adjustment of the triggering threshold parameters. Following the completion of the aforementioned research, control objectives are established to validate the performance of the teleoperation control system proposed in this paper. Finally, simulation verification is conducted in the Matlab/Simulink environment.

Unimart: OLX for College Students

Unimart is an innovative project designed to address the ever-growing demand among university students for an efficient and collaborative platform to exchange study materials and related academic resources. In today's digital age, students are constantly seeking ways to enhance their learning experiences and expand their educational horizons. Unimart steps in as the solution, providing a dynamic online marketplace where students can freely and securely share textbooks, lecture notes, research materials, and various academic essentials. This platform seeks to foster a sense of community among students by promoting the exchange of knowledge, making education more accessible, affordable, and sustainable. By offering a user-friendly and organized interface, Unimart empowers students to connect with peers from diverse academic backgrounds, facilitating a peer-to-peer ecosystem that fosters collaborative learning and resource sharing. As a catalyst for academic success and community building, Unimart is poised to revolutionize how university students interact with their academic surroundings. This project promises to empower students by promoting collaboration.

Unpacking the Nuances of Agenda-Setting in the Online Media Environment: An Hourly-Event Approach in the Context of Chinese Economic News

ABSTRACT The study of agenda-setting has faced considerable challenges in the digital age. Not only are the existing conceptualizations inadequate regarding the new features of online media outlets, but traditional analysis has also become insufficient to address the nuances of agenda-setting in the dynamic online media environment. In this study, we refined assumptions of reverse and intermedia agenda-setting theory, introduced the news events and hourly intervals approach, and considered economic news events in China as an appropriate theme. Our analysis showed that the few popular media played a gatekeeping role in the Chinese online media environment, and the average time interval in which agendas transferred across media and netizens was about 4 h. This study, thereby, advanced the understanding of agenda-setting dynamics in the digital society and showed directions for future agenda-setting research in terms of establishing more up-to-date conceptual assumptions and employing more nuanced methodological approaches.

Digital Communities as Online 'Gardens' for Memes: Metamorphic Narratives of Two Filipino 'Memeifiers' as Cultural Pollinators

The increasingly ubiquitous digital communities have become dynamic online social hubs for netizens to proliferate internet memes. While there have been an abundance of studies of memes, a dearth of investigations have examined how an internet user becomes an active producer of memes. Hence, we explored the narratives of two Filipino netizens, whom we call “memeifiers,” as we identified them as constant generators of memes. Based on the semi-structured interview data and analysis of their memes, we metaphorically posit that digital communities can be likened to online gardens where butterflies (memeifiers) dynamically engage in producing and pollinating memes. Using narrative configuration, we labelled their experiences into metamorphic narratives with three stages: (1) the cocoon stage, in which meme literacy is formed through active memes consumption; (2) the metamorphosis stage, in which meme-literate netizens vigorously generate memes then metamorphose into memeifiers; and finally, (3) the pollination stage, when memeifiers, akin to butterflies, continuously become cultural pollinators as they spread cultural artifacts through memes in various online communities. With these findings, we lay down some recommendations for future meme studies, such as that the metamorphic narrative of memeifiers be investigated in other geographical contexts since a memeifier’s story may vary due to cultural differences. Moreover, we recommend that the scope of participants be broadened, employing similar or other qualitative research designs and even quantitative perspectives to further scrutinize the metamorphosis of memeifiers and their roles as cultural pollinators. Keywords: digital communities, memes, narrative study, online communities

An online integrated satellite–terrestrial IoT task offloading and service deployment strategy

As a core component of 6G networking, low earth orbit (LEO) satellite networks are considered a promising solution for providing network services in remote areas and have received considerable attention in recent years. Meanwhile, with the rapid development of IoT terminals and applications, an increasing emphasis has been placed on task offloading for real-time tasks. However, most existing works on task offloading for integrated satellite–terrestrial IoT (IST-IoT) applications are based on the assumption that the global task and network information is known, an assumption that is obviously not met in online real-time task offloading. In addition, current studies rarely consider the tradeoff among the task offloading volume, satellite endurance and wholesale cost of terrestrial resources, which are precisely the holistic issues that satellite network operators must confront. To address such issues, this study considers task offloading, service deployment and resource allocation in highly dynamic online task offloading scenarios with the goal of maximizing the average task offloading volume. Moreover, virtual queues are constructed in this study to account for the impacts of long-term satellite endurance and terrestrial network resource costs on task offloading. We first decouple the original problem to obtain a single-time-slot optimization problem within the Lyapunov optimization framework, and we then propose a 2-stage optimization approach based on deep reinforcement learning (DRL) to solve the complex single-time-slot mixed integer nonlinear programming (MINLP) problem. Simulation experiments demonstrate that our proposed algorithm can yield near-optimal solutions that show superior performance compared to existing results.

A PSMA PET/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer.

This study is to investigate the diagnostic value of 68Ga-PSMA-11 in improving the concordance between mpMRI-TB and combined biopsy (CB) in detecting PCa. 115 consecutive men with 68Ga-PSMA-11 PET/CT prior to prostate biopsy were included for analysis. PSMA intensity, quantified as maximum standard uptake value (SUVmax), minimum apparent diffusion coefficient (ADCmin) and other clinical characteristics were evaluated relative to biopsy concordance using univariate and multivariate logistic regression analyses. A prediction model was developed based on the identified parameters, and a dynamic online diagnostic nomogram was constructed, with its discrimination evaluated through the area under the ROC curve (AUC) and consistency assessed using calibration plots. To assess its clinical applicability, a decision curve analysis (DCA) was performed, while internal validation was conducted using bootstrapping methods. Concordance between mpMRI-TB and CB occurred in 76.5% (88/115) of the patients. Multivariate logistic regression analyses performed that SUVmax (OR= 0.952; 95% CI 0.917-0.988; P= 0.010) and ADCmin (OR= 1.006; 95% CI 1.003-1.010; P= 0.001) were independent risk factors for biopsy concordance. The developed model showed a sensitivity, specificity, accuracy and AUC of 0.67, 0.78, 0.81 and 0.78 in the full sample. The calibration curve demonstrated that the nomogram's predicted outcomes closely resembled the ideal curve, indicating consistency between predicted and actual outcomes. Furthermore, the decision curve analysis (DCA) highlighted the clinical net benefit achievable across various risk thresholds. These findings were reinforced by internal validation. The developed prediction model based on SUVmax and ADCmin showed practical value in guiding the optimization of prostate biopsy pattern. Lower SUVmax and Higher ADCmin values are associated with greater confidence in implementing mono-TB and safely avoiding SB, effectively balancing benefits and risks.

LuminousBookWeb

Abstract: "LuminousBookWeb" is a groundbreaking library management system designed for an enhanced user experience. The system features a dynamic online catalog with keyword search, providing detailed book information, abstracts, and full PDF access. A streamlined booking system generates QR codes for physical library transition, integrating with a smart almirah that guides users to booked books using responsive lighting. This project redefines library interactions by seamlessly blending digital cataloging with a novel wayfinding mechanism, simplifying material retrieval, and offering an interactive and user-friendly library experience.

Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy

Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively.Graphical

Modeling the evolution of collective overreaction in dynamic online product diffusion networks

With the development of e-commerce, collective overreactions such as buying frenzy have become prominent. However, studies have rarely investigated the mechanism of irrational consumer behavior at the group level. To investigate the evolution of collective overreaction in dynamic online product diffusion networks, we employed a sequential multiple-methods approach. A conceptual model is constructed to capture the influence of social network dynamic evolution on individual irrationality. An agent-based model (ABM) under different network dynamic growth mechanisms is implemented and verified. The findings revealed the following. In external dynamic growth mechanisms, key opinion consumer (KOC) connection can lead to positive collective overreaction (i.e., the adoption rate of consumer groups spikes). This effect fades as the probability of KOC connection increases and stabilizes as the node change rate decreases. Random connection is prone to negative collective overreaction (i.e., a sudden and sharp decline in the adoption rate of consumer groups), and key opinion leader (KOL) connection exhibits both positive and negative collective overreaction. Increasing the edge change rate increases the frequency of negative collective overreaction in KOL connections. In internal dynamic growth mechanisms, KOL and KOC connections are prone to negative collective overreaction; increasing the edge change rate can reduce the frequency of negative collective overreaction in KOL overreaction, and an appropriate edge change rate can inhibit the emergence of negative collective overreaction in KOC connection. This research contributes to the area of internet product marketing and provides a new basic framework through which to combine psychology and the ABM.

Development and validation of an online dynamic nomogram system for pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia.

The occurrence of pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia (MPP) can lead to exacerbation of the disease. Therefore, early identification of children with MPP in combination with pulmonary consolidation is critical. The purpose of this study was to develop a straightforward, easy-to-use online dynamic nomogram for the identification of children with MPP who are at high risk of developing pulmonary consolidation. 491 MPP patients were chosen and divided randomly into a training cohort and an internal validation cohort at a 4:1 ratio. Multi-factor logistic regression was used to identify the risk variables for mixed pulmonary consolidation in children with Mycoplasma pneumoniae (MP). The selected variables were utilized to build the nomograms and validated using the C-index, decision curve analysis, calibration curves, and receiver operating characteristic (ROC) curves. Seven variables were included in the Nomogram model: age, fever duration, lymphocyte count, C-reactive protein (CRP), ferritin, T8 lymphocyte percentage, and T4 lymphocyte percentage. We created a dynamic nomogram that is accessible online ( https://ertong.shinyapps.io/DynNomapp/ ). The C-index was 0.90. The nomogram calibration curves in the training and validation cohorts were highly comparable to the standard curves. The area under the curve (AUC) of the prediction model was, respectively, 0.902 and 0.883 in the training cohort and validation cohort. The decision curve analysis (DCA) curve shows that the model has a significant clinical benefit. We developed a dynamic online nomogram for predicting combined pulmonary consolidation in children with MP based on 7 variables for the first time. The predictive value and clinical benefit of the nomogram model were acceptable.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

A Dynamic Online Nomogram Based on Gd-EOB-DTPA-Enhanced MRI and Inflammatory Biomarkers for Preoperative Prediction of Pathological Grade and Stratification in Solitary Hepatocellular Carcinoma: A Multicenter Study

BackgroundHepatocellular carcinoma (HCC) is an inflammatory cancer. We aimed to explore whether preoperative inflammation biomarkers compared with the gadoxetic acid disodium (Gd-EOB-DTPA) enhanced MRI can add complementary value for predicting HCC pathological grade, and to develop a dynamic nomogram to predict solitary HCC pathological grade. Methods331 patients from the Institution A were divided chronologically into the training cohort (n=231) and internal validation cohort (n=100), and recurrence-free survival (RFS) was determined to follow up after surgery. 79 patients from the Institution B served as the external validation cohort. Overall, 410 patients were analyzed as the complete dataset cohort. Least absolute shrinkage and selection operator (LASSO) and multivariate Logistic regression were used to gradually filter features for model construction. The area under the receiver operating characteristic curve (AUC) and decision curve analysis were used to evaluate model's performance. ResultsFive models of the inflammation, imaging, inflammation+AFP, inflammation+imaging and nomogram were developed. Adding inflammation to imaging model can improve the AUC in training cohort (from 0.802 to 0.869), internal validation cohort (0.827 to 0.870), external validation cohort (0.740 to 0.802) and complete dataset cohort (0.739 to 0.788), and obtain more net benefit. The nomogram had excellent performance for predicting high-grade HCC in four cohorts (AUCs: 0.882 vs. 0.869 vs. 0.829 vs. 0.806) with a good calibration, and accessed at https://predict-solitaryhccgrade.shinyapps.io/DynNomapp/. Additionally, the nomogram obtained an AUC of 0.863 (95% CI 0.797-0.913) for predicting high-grade HCC in the HCC≤3cm. Kaplan-Meier survival curves demonstrated that the nomogram owned excellent stratification for HCC grade (P<0.0001). ConclusionThis easy-to-use dynamic online nomogram hold promise for use as a noninvasive tool in prediction HCC grade with high accuracy and robustness.

Dynamic Survival Risk Prognostic Model and Genomic Landscape for Atypical Teratoid/Rhabdoid Tumors: A Population-Based, Real-World Study.

An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

A nomogram model based on the systemic immune-inflammation index to predict the risk of venous thromboembolism in elderly patients after hip fracture: A retrospective cohort study

Background and objectivesVenous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and secondary pulmonary embolism (PE), represents a significant complication post-hip fracture in the elderly. It is a prevalent cause of VTE-related complications, prolonged hospitalization, and mortality. This study aimed to investigate the potential of the systemic immune-inflammation index (SII) as a predictive marker for VTE in older patients following hip fracture. MethodsThe study was structured as an observational, analytical, retrospective cohort analysis. A total of 346 elderly patients diagnosed with hip fracture were included. We retrospectively collated clinical and laboratory data for these patients. Using the bootstrap method, the patients were divided in a 7:3 ratio into a training cohort (DVT group = 170 patients; no-DVT group = 72 patients) and an internal validation cohort (DVT group = 81 patients; no-DVT group = 23 patients). In the training cohort, relevant indices were initially identified using univariate analysis. Subsequently, least absolute shrinkage and selection operator logistic analysis was employed to determine significant potential independent risk factors (P < 0.05). A dynamic online diagnostic nomogram was developed, with its discriminative ability assessed using the area under the receiver operating characteristic curve (AUC). The nomogram's accuracy was further appraised using calibration plots. The clinical utility of the nomogram was evaluated through decision curve analysis (DCA) and corroborated by internal validation within the training set. ResultsSII emerged as the sole independent risk factor identified from the multivariate logistic analysis of the training cohort and was incorporated into the VTE diagnostic nomogram for older patients' post-hip fracture. The nomogram demonstrated AUC values of 0.648 in the training cohort and 0.545 in the internal testing cohort. Calibration curves corroborated the close alignment of the nomogram's predicted outcomes with the ideal curve, indicating consistency between predicted and actual outcomes. The DCA curve suggested that all patients could derive benefit from this model. These findings were also validated in the validation cohort. ConclusionThe systemic immune-inflammation index is a robust predictor of venous thromboembolism in elderly patients following hip fracture, underscoring its potential as a valuable tool in clinical practice.

From the Classroom to the Screen: The Power of Artistic Expression and Emotional Intelligence in Effective Online Teaching and Communication

In the context of the increasing prevalence of online education, it is crucial to explore productive approaches that enhance student engagement and foster meaningful interactions. The article examines the role of emotional intelligence and artistic expression in online teaching and communication, drawing from original global survey research with over 200 teacher and educator respondents. The study emphasizes the effectiveness of artistic expression in capturing students' attention, facilitating comprehension, and promoting creativity. The research explores strategies for incorporating emotional intelligence and artistic expression in online teaching, offering practical tips specifically suited for online English instruction. These strategies guide educators in creating dynamic online learning environments that optimize the learning experience and promote academic success. Acknowledging challenges, the paper sheds light on the hesitations and fears experienced by participants in being recorded or teaching in front of a camera. It highlights the need for training, resources, and guidelines to support educators in acquiring and developing artistic expression skills. By addressing challenges and integrating artistic expression, teachers can create impactful and supportive online learning environments that promote student engagement and provide an enriched learning experience in the digital era of education. Finally, the article provides valuable insights and practical recommendations to enhance online teaching practices.

A dynamic online nomogram for predicting the heterogeneity trajectories of frailty among elderly gastric cancer survivors

BackgroundFrailty is very common among older people with gastric cancer and seriously affects their prognosis. The development of frailty is continuous and dynamic, increasing the difficulty and burden of care. ObjectivesThe aims of this study were to delineate the developmental trajectory of frailty in older people with gastric cancer 1 year after surgery, identify heterogeneous frailty trajectories, and further explore their predictors to construct a nomogram for prediction. DesignWe conducted a prospective longitudinal observation study. Clinical evaluation and data collection were performed at discharge, and at 1, 3, 6, and 12 months. Setting and participantsThis study was conducted in a tertiary hospital and 381 gastric cancer patients over 60 years who underwent radical gastrectomy completed the 1-year follow-up. MethodsA growth mixture model (GMM) was used to delineate the frailty trajectories, and identify heterogeneous trajectories. A regression model was performed to determine their predictors and further construct a nomogram based on the predictors. Bootstrap with 1000 resamples was used for internal validation of nomogram, a receiver operating characteristic (ROC) curve to evaluate discrimination, calibration curves to evaluate calibration and decision curve analysis (DCA) to evaluate the clinical value. ResultsGMM identified three classes of frailty trajectories: “frailty improving”, “frailty persisting” and “frailty deteriorating”. The latter two were referred to as heterogeneous frailty trajectories. Regression analysis showed 8 independent predictors of heterogeneous frailty trajectories and a nomogram was constructed based on these predictors. The area under ROC curve (AUC) of the nomogram was 0.731 (95 % CI = 0.679–0.781), the calibration curves demonstrated that probabilities predicted by the nomogram agreed well with the actual observation with a mean absolute error of 0.025, and the DCA of nomogram indicated that the net benefits were higher than that of the other eight single factors. ConclusionsOlder gastric cancer patients have heterogeneous frailty trajectories of poor prognosis during one-year postoperative survival. Therefore, early assessment to predict the occurrence of heterogeneous frailty trajectories is essential to improve the outcomes of elderly gastric cancer patients. Scientific and effective frailty interventions should be further explored in the future to improve the prognosis of older gastric cancer patients. Contribution of the paper statementsThis study constructed a static and dynamic online nomogram with good discrimination and calibration, which can help to screen high-risk patients, implement preoperative risk stratification easily and promote the rational allocation of medical resources greatly. RegistrationClinicalTrials.gov (Number: NCT05982899). Tweetable abstractOur findings identified three frailty trajectories and constructed a nomogram to implement preoperative risk stratification and improve patient outcomes.

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

This article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization. Supplementary materials for this article are available online.

Hierarchical Resource Orchestration Framework for Real-time Containers

Container-based virtualization is a promising deployment model in fog and edge computing applications, because it allows a seamless co-existence of virtualized applications in a heterogeneous environment without introducing significant overhead. Certain application domains (e.g., industrial automation, automotive, or aerospace) mandate that applications exhibit a certain degree of temporal predictability. Container-based virtualization cannot be easily used for such applications, since the technology is not designed to support real-time properties and handle temporal disturbances. This article proposes a framework consisting of a static offline and a dynamic online phase for resource allocation and adaptive re-dimensioning of real-time containers. In the offline phase, the optimal initial deployment and dimensioning of containers are decided based on ideal system models. Additionally, to adapt to dynamic variations caused by changing workloads or interferences, the online phase adapts the CPU usage and limits of real-time containers at runtime to improve the real-time behavior of the real-time containerized applications while optimizing resource usage. We implement the framework in a real Linux-based system and show through a series of experiments that the proposed framework is able to adjust and re-distribute computing resources between containers to improve the real-time behavior of containerized applications in the presence of temporal disturbances while optimizing resource usage.