Update Algorithm Research Articles

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents’ decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance.

Local differential privacy for data security in key value pair data

Data collection using local differential privacy (LDP) has mainly been studied for homogeneous data. Several data categories, including key-value pairs, must be estimated simultaneously in real-world applications, including the frequency of keys and the mean values within each key. It is challenging to achieve an acceptable utility-privacy tradeoff using LDP for key-value data collection since the data has two aspects, and a client could have multiple key-value pairs. Current LDP approaches are not scalable enough to handle large and small datasets. When the dataset is small, there is insufficient data to calculate statistical parameters; when the dataset is enormous, such as in streaming data, there is a risk of data leakage due to the high availability of too much information. The result is unsuitable for examination due to the substantial amount of randomization used in some methods. Existing LDP approaches are mostly restricted to basic data categories like category and numerical values. To address these difficulties, this research developed the DKVALP (Differentially private key-value pairs) algorithm, which ensures differential privacy in key-value pair data. This DKVALP is a lightweight, differentially private data algorithm that generates random noise using an updated Laplace algorithm to ensure differential privacy for the data. According to execution outputs on synthetic and real-world datasets, the proposed DKVALP framework offers improved usefulness for both frequency and mean predictions over the similar LDP security as conventional approaches.

Screening for metabolic dysfunction-associated fatty liver disease: Time to discard the emperor's clothes of normal liver enzymes?

Metabolic dysfunction-associated fatty liver disease (MAFLD) is the most prevalent chronic liver condition worldwide. Current liver enzyme-based screening methods have limitations that may missed diagnoses and treatment delays. Regarding Chen et al, the risk of developing MAFLD remains elevated even when alanine aminotransferase levels fall within the normal range. Therefore, there is an urgent need for advanced diagnostic techniques and updated algorithms to enhance the accuracy of MAFLD diagnosis and enable early intervention. This paper proposes two potential screening methods for identifying individuals who may be at risk of developing MAFLD: Lowering these thresholds and promoting the use of noninvasive liver fibrosis scores.

Appropriateness of Hysterectomy as Treatment for Benign Gynecological Conditions.

Objective: To assess the appropriateness of hysterectomies performed at a large tertiary health system using the 1997 RAND appropriateness classification system and an updated algorithm. Design: We abstracted structured and unstructured data from electronic medical records on patient demographics, primary indication(s) for hysterectomy, diagnosis codes associated with the hysterectomy, previous treatments, and laboratory results. Subjects: Patients aged 18-44 years. Exposure: Receipt of hysterectomy for benign and nonobstetric conditions from October 2014 to December 2017. Main Outcome Measures: Using these data, we provided a RAND-based (dichotomous: inappropriate/appropriate) and Wright-based (3-level: inappropriate/ambiguous/appropriate) appropriateness rating and characterized missing information patterns associated with inappropriate ratings. Results: We analyzed 1,829 hysterectomies across 30 nonmutually exclusive primary indications for surgery. Nearly a third (32.8%) of surgeries had only one primary indication for surgery. Using the RAND-based classifier, 31.3% of hysterectomies were rated as appropriate and 68.7% as inappropriate. Using the Wright-based algorithm, 58.1% of hysterectomies were rated as appropriate, 15.7% as ambiguous, and 26.2% as inappropriate. Missing information on diagnostic procedures was the most common characteristic related to both RAND-based (46.1%) and Wright-based (51.2%) inappropriate ratings. Conclusions: The 1997 RAND classification lacked guidance for several contemporary indications, including gender-affirming care. RAND also has an outdated requirement for diagnostic surgeries such as laparoscopies, which have decreased in practice as diagnostic imaging has improved. Sensitivity analyses suggest that inappropriate surgeries cannot all be attributed to bias from missing electronic medical record data. Accurately documenting care delivery for benign gynecological conditions is key to ensuring quality and equity in gynecological care.

Improved Model Predictive Control Path Tracking Approach Based on Online Updated Algorithm with Fuzzy Control and Variable Prediction Time Domain for Autonomous Vehicles

The design of trajectory tracking controllers for smart driving cars still faces problems, such as uncertain parameters and it being time-consuming. To improve the tracking performance of the trajectory tracking controller and reduce the computation of the controller, this paper proposes an improved model predictive control (MPC) method based on fuzzy control and an online update algorithm. First, a vehicle dynamics model is constructed and a feedforward MPC controller is designed; second, a real-time updating method of the time domain parameters is proposed to replace the previous method of empirically selecting the time domain parameters; lastly, a fuzzy controller is proposed for the real-time adjustment of the weight coefficient matrix of the model predictive controller according to the lateral and heading errors of the vehicle, and a state matrix-based cosine similarity updating mechanism is developed for determining the updating nodes of the state matrix to reduce the controller computation caused by the continuous updating of the state matrix when the longitudinal vehicle speed changes. Finally, the controller is compared with the traditional model prediction controller through the co-simulation of CARSIM and MATLAB/Simulink, and the results show that the controller has great improvement in terms of tracking accuracy and controller computational load.

Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.

Increasing research has shown that the abnormal expression of microRNA (miRNA) is associated with many complex diseases. However, biological experiments have many limitations in identifying the potential disease-miRNA associations. Therefore, we developed a computational model of Three-Layer Heterogeneous Network based on the Integration of CircRNA information for MiRNA-Disease Association prediction (TLHNICMDA). In the model, a disease-miRNA-circRNA heterogeneous network is built by known disease-miRNA associations, known miRNA-circRNA interactions, disease similarity, miRNA similarity, and circRNA similarity. Then, the potential disease-miRNA associations are identified by an update algorithm based on the global network. Finally, based on global and local leave-one-out cross validation (LOOCV), the values of AUCs in TLHNICMDA are 0.8795 and 0.7774. Moreover, the mean and standard deviation of AUC in 5-fold cross-validations is 0.8777+/-0.0010. Especially, the two types of case studies illustrated the usefulness of TLHNICMDA in predicting disease-miRNA interactions.

Achieving Tunable Erasure Coding with Cluster-Aware Redundancy Transitioning

Erasure coding has been demonstrated as a storage-efficient means against failures, yet its tunability remains a challenging issue in data centers, which is prone to induce substantial cross-cluster traffic. In this paper, we present ClusterRT , a cluster-aware redundancy transitioning approach that can dynamically tailor the redundancy degree of erasure coding in data centers. ClusterRT formulates the data relocation as the maximum flow problem to reduce cross-cluster data transfers. It then designs a parity-coordinated update algorithm, which gathers the parity chunks within the same cluster and leverage encoding dependency to further decrease the cross-cluster update traffic. ClusterRT finally rotates the parity chunks to balance the cross-cluster transitioning traffic across the data center. Large-scale simulation and Alibaba Cloud ECS experiments show that ClusterRT reduces 94.0-96.2% of transitioning traffic and reduces 70.4-88.4% of transitioning time.

Parallel tempered metadynamics: Overcoming potential barriers without surfing or tunneling

At fine lattice spacings, Markov chain Monte Carlo simulations of QCD and other gauge theories with or without fermions are plagued by slow modes that give rise to large autocorrelation times. This can lead to simulation runs that are effectively stuck in one topological sector, a problem known as topological freezing. Here, we demonstrate that for a relevant set of parameters, metadynamics can be used to unfreeze four-dimensional SU(3) gauge theory. However, compared to local update algorithms and the Hybrid Monte Carlo algorithm, the computational overhead is significant in pure gauge theory, and the required reweighting procedure may considerably reduce the effective sample size. To deal with the latter problem, we propose modifications to the metadynamics bias potential and the combination of metadynamics with parallel tempering. We test the new algorithm in four-dimensional SU(3) gauge theory and find that it can achieve topological unfreezing without compromising the effective sample size, thereby reducing the autocorrelation times of topological observables by at least 2 orders of magnitude compared to conventional update algorithms. Additionally, we observe significantly improved scaling of autocorrelation times with the lattice spacing in two-dimensional U(1) gauge theory. Published by the American Physical Society 2024

The protective role of cardiovascular health against kidney stones: A study based on LE8 score

ObjectivesThe American Heart Association recently released an updated algorithm for evaluating cardiovascular health (CVH)-Life's Essential 8 (LE8) score. Our objective was to investigate the correlation between levels of CVH, as determined by the LE8 score, and the risk of kidney stones among a representative sample of adults in the United States. MethodsWe included data from the National Health and Nutrition Examination Survey (NHANES) covering the years 2007–2016 for further analysis. The LE8 score, a comprehensive measurement ranging from 0 to 100, was used to evaluate overall CVH and classified into three categories: low (0–49), moderate (50–79), and high (80–100) CVH. Logistic regression was employed to assess the association between the LE8 score and kidney stones. Furthermore, sensitivity analysis was conducted to validate the findings, and the presence of a non-linear relationship was examined using restricted cubic spline (RCS) regression methods. ResultsA total of 19,988 participants were included in this study (weighted mean age, 47.99 years; 95 % confidence interval [CI]: 47.46–48.53 years), with 10,319 being female (weighted percentage, 51.98 %; 95 % CI: 51.26–52.71 %) and 1923 identified as having kidney stones (weighted percentage, 9.95 %; 95 % CI: 9.41–10.53 %). In the fully-adjusted multivariable model, higher LE8 scores were associated with prevalence of self-reported kidney stones (odds ratio [OR] for a 10-unit increase in score, 0.86; 95 % CI: 0.82–0.91), presenting a linear dose-response relationship. Compared to the low CVH group, participants in the moderate and high CVH groups exhibited a lower prevalence of kidney stones (OR = 0.80; 95 % CI: 0.69–0.92; OR = 0.54; 95 % CI: 0.43–0.69, respectively). Similar trends were observed when assessing the association between health behavior scores and kidney stones. Moreover, the negative correlation between the LE8 score and the prevalence of kidney stones was markedly more pronounced in various stratified analyses. ConclusionOur study suggests that a higher level of CVH, as assessed by the LE8 metrics, is independently associated with a lower prevalence of self-reported kidney stones in a linear relationship. Further research, particularly through longitudinal or intervention studies, is required to establish whether actively promoting optimal CVH levels can effectively reduce the incidence of kidney stones.

Surface roughness and wave slope statistics from the multi-spectral polarimetric imaging of the ocean surface.

The polarization of light in Ocean Color (OC) applications provides important information about atmospheric parameters, water composition, and the ocean surface. The Stokes vector components and the degree of linear polarization of light contain useful information about the air-water interface, including ocean surface roughness. We present polarimetric measurements and analysis of the ocean wave slopes at several bands. Data is acquired with a Teledyne DALSA camera, which uses a polarizer-on-chip focal plane of 1232 × 1028 super-pixels, where each pixel is made of four subpixels with 0-, 90-, 45- and 135-degrees orientation of polarization. We present a modified version of the Polarization Slope Sensing (PSS) technique [Zappa et al., 2008] for the non-contact detection of wave slopes and demonstrate a good performance of the updated algorithm in several conditions where the original technique was not applicable. Derived wave slopes are presented for various aquatic and atmospheric environments, including during VIIRS Cal/Val cruises and at a near-shore pier. The results are shown to be consistent with theoretical wave slope models.

Outpatient versus inpatient shoulder arthroplasty outcomes using an updated patient-selection algorithm: minimum two-year follow-up

BackgroundPrevious studies have demonstrated the safety and cost-effectiveness of outpatient total shoulder arthroplasty (TSA), with the majority of studies focusing on 90-day outcomes and complications. Patient selection algorithms have helped appropriately choose patients for an outpatient TSA setting. This study aimed to determine the outcomes of TSA between outpatient and inpatient cohorts with at least a 2-year follow-up. MethodsA retrospective review identified patients older than 18 years who underwent a TSA with a minimum of 2-year follow-up in either an inpatient or outpatient setting. Using a previously published outpatient TSA patient-selection algorithm, patients were allocated into three groups: outpatient, inpatient due to insurance requirements, and inpatient due to not meeting algorithm criteria. Outcomes evaluated included visual analog scale (VAS) pain, American Shoulder and Elbow Surgeons (ASES) score, Single Assessment Numeric Evaluation (SANE) score, range of motion (ROM), strength, complications, re-admissions, and re-operations. Analysis was performed between the outpatient and inpatient groups to demonstrate the safety and efficacy of outpatient TSA with midterm follow-up. ResultsA total of 779 TSA were included in this study, allocated into the outpatient (N = 108), inpatient due to insurance (N = 349), and inpatient due to algorithm (N = 322). The average age between these groups was significantly different (59.4 ± 7.4, 66.5 ± 7.5, and 72.5 ± 8.7, respectively; P < 0.0001). All patient groups demonstrated significant improvements in preoperative to final patient-outcomes scores, ROM, and strength. Analysis between cohorts showed similar final follow-up outcome scores, ROM, and strength, with few significant differences that are likely not clinically different, regardless of surgical location, insurance status, or meeting patient-selection algorithm. Complications, reoperations, and readmissions between all three groups were not significantly different. ConclusionThis study reaffirms prior short-term follow-up literature. Transitioning appropriate patients to outpatient TSA results in similar outcomes and complications compared to inpatient cohorts with mid-term follow-up.

A SDN improvement scheme for multi‐path QUIC transmission in satellite networks

AbstractIn recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS.

6th and 7th International consensus guidelines for the management of advanced breast cancer (ABC guidelines 6 and 7)

This manuscript describes the Advanced Breast Cancer (ABC) international consensus guidelines updated at the last two ABC international consensus conferences (ABC 6 in 2021, virtual, and ABC 7 in 2023, in Lisbon, Portugal), organized by the ABC Global Alliance. It provides the main recommendations on how to best manage patients with advanced breast cancer (inoperable locally advanced or metastatic), of all breast cancer subtypes, as well as palliative and supportive care. These guidelines are based on available evidence or on expert opinion when a higher level of evidence is lacking. Each guideline is accompanied by the level of evidence (LoE), grade of recommendation (GoR) and percentage of consensus reached at the consensus conferences. Updated diagnostic and treatment algorithms are also provided. The guidelines represent the best management options for patients living with ABC globally, assuming accessibility to all available therapies. Their adaptation (i.e. resource-stratified guidelines) is often needed in settings where access to care is limited.

Correction to: In the original article there are several transcription errors in appendix A: the stress update algorithm

Correction to: In the original article there are several transcription errors in appendix A: the stress update algorithm

Modeling confined concrete behavior in finite element with a non-orthogonal elastoplastic model

The accuracy of a constitutive model for confined concrete largely relies on its capability to capture concrete's dilatancy behavior. In this paper, a non-orthogonal flow rule (NFR) is used to reasonably characterize the concrete's volume change law in relation to the stress state without the necessity for a plastic potential function. Then, the non-orthogonal plastic model is implemented in the finite element (FE) software ABAQUS using the implicit stress update algorithm, which employs the line search method and the numerical consistent tangent stiffness matrix to ensure the robustness and computational efficiency of FE analysis. Finally, FE simulations are performed to evaluate the constitutive model's capabilities in actively and passively confined concrete. In the latter case, steel tubes restrict the concrete's lateral deformation. An analysis and discussion are conducted regarding the impact of dilatancy behavior on concrete-filled steel tube (CFST) columns. The consistency between experimental data and simulation results demonstrates that the FE modeling with a non-orthogonal constitutive model provides an effective tool to describe the behavior of confined concrete.

#3124 How well do risk assessment guidelines perform for ADPKD?

Abstract Background and Aims The approval of Tolvaptan for treatment of ADPKD heralds a new era when mechanism-based therapy is now possible. However, Tolvaptan is an expensive drug associated with potentially serious side effects and is currently reserved for patients at high-risk for progression to ESKD. Two sets of risk assessment guidelines for ADPKD are now available based on the consensus of two panels of nephrologists from Canada and Europe. However, how well do these guidelines perform in risk assessment has not been formally assessed. Method We conducted a prospective study in 474 patients with typical imaging pattern of ADPKD by MRI who also had detailed clinical and laboratory data. We used age- and height-adjusted total kidney volume to derive the Mayo Clinic Imaging Class as a “gold-standard” for risk assessment (i.e. low-risk: 1A-1B; high-risk: 1C-1E). We then applied the revised Canadian guidelines (Can J Kidney Health Dis. 2018) and the updated European guidelines (NDT 2022) to our patient cohort to assess their performance. Results The study cohort consisted of 286/474 (60%) high-risk patients with MCIC 1C-1E. Applying the updated Canadian risk assessment algorithm resulted in exclusion of 245/474 (52%) patients including 86/286 (30%) of the high-risk patients. The resultant cohort (229/474) was enriched with 88% high-risk patients but also included 12% of low-risk patients. The updated European guidelines provide a 3-step hierarchal algorithm, when applied resulted in exclusion of 55% (157/286) of high-risk patients. During the last step, 122 patients could not be classified due to a lack of eGFR slope information. The resultant cohort (180/474) was enriched with 72% high-risk patients but also included 28%(51/180) of low-risk patients. Conclusion Risk assessment in ADPKD is an evolving process that needs to be redefined by new clinical data and test technologies. Clinical guidelines should be evaluated using real-life data and those that enrich high-risk patients while minimizing low-risk patients have the most clinical utility

Pierre Robin Sequence: An Updated Evidence-Based Treatment Proposal.

The Pierre Robin sequence (PRS) is characterized by the presence of micrognathia, glossoptosis, and respiratory obstruction during the neonatal period, its prompt recognition allows to mitigate the associated morbidity and mortality. A diagnosis and treatment algorithm was previously proposed based on data from the literature to guide therapeutic efforts; therefore, it was proposed to carry out a new search for relevant evidence to update or complement it. A literature review of the subject was conducted in PubMed, Embase, and Cochrane databases, corresponding to the period between November 2016 and September 2021. Using the GRADE methodology, 38 articles from different clinical studies that discussed diagnostic tests or therapeutic approaches, directly or indirectly compared, were selected and evaluated. After evaluating and analyzing the selected articles, the new information was incorporated into an updated algorithm according to the most recent evidence found for the diagnosis and comprehensive management of patients with PRS. To date, there is no consensus in the literature on the treatment of patients with PRS nor are there multicenter studies comparing different management modalities. The indications to proceed with surgical strategies do not present changes with respect to the previous article. Nutritional monitoring is the main objective, and the study of oral feeding is essential in all scenarios.

Online updating mode learning for streaming datasets

In the case of limited computer memory, it is worth discussing how to regression the big data stream and solve the outlier problem reasonably. To solve the above problems, this article proposes an online updatable modal linear regression algorithm, which is robust to heavy-tailed distribution, heterogeneous errors and outliers. However, due to streaming data, the traditional static statistical methods are facing new challenges. In addition, existing inference tools in online learning cannot be used directly for modal linear regression. A major challenge is that the data is one-pass and the final amount of data is not even known, which results in the inability to obtain optimal bandwidth, resulting in poor theoretical performance of the estimator. In particular, the estimation and inference methods are proposed. The process only passes the data once. Finally, the finite sample performance of the proposed method is verified by simulation and real data analysis.

Biomechanical and histological changes associated with riboflavin ultraviolet-A-induced CXL with different irradiances in young human corneal stroma

Keratoconus (KC) is a degenerative condition affecting the cornea, characterized by progressive thinning and bulging, which can ultimately result in serious visual impairment. The onset and progression of KC are closely tied to the gradual weakening of the cornea's biomechanical properties. KC progression can be prevented with corneal cross-linking (CXL), but this treatment has shortcomings, and evaluating its tissue stiffening effect is important for determining its efficacy. In this field, the shortage of human corneas has made it necessary for most previous studies to rely on animal corneas, which have different microstructure and may be affected differently from human corneas. In this research, we have used the lenticules obtained through small incision lenticule extraction (SMILE) surgeries as a source of human tissue to assess CXL. And to further improve the results' reliability, we used inflation testing, personalized finite element modeling, numerical optimization and histology microstructure analysis. These methods enabled determining the biomechanical and histological effects of CXL protocols involving different irradiation intensities of 3, 9, 18, and 30 mW/cm2, all delivering the same total energy dose of 5.4 J/cm2. The results showed that the CXL effect did not vary significantly with protocols using 3–18 mW/cm2 irradiance, but there was a significant efficacy drop with 30 mW/cm2 irradiance. This study validated the updated algorithm and provided guidance for corneal lenticule reuse and the effects of different CXL protocols on the biomechanical properties of the human corneal stroma.

Delay-guaranteed Mobile Augmented Reality Task Offloading in Edge-assisted Environment

With the introduction of Augmented Reality (AR) into mobile devices, it becomes a trend to develop mobile AR applications in various fields. However, the execution of AR task demands the extensive resources of computation, memory and storage, which makes it difficult for mobile terminals with constrained hardware resources to carry out AR applications within the limited delay. In response to this challenge, we propose a mobile AR offloading method under the edge-assisted environment. Firstly, we divide an AR task into consecutive subtasks, and then collect the features of hardware, software, configuration, and runtime environments from the edge servers to be offloaded. With the features, we construct an AR subtask Execution delay Prediction Bayesian Network (EPBN) to predict the execution delay of different subtasks on each edge platform. Based on the prediction, we model the task offloading as the NP-hard Traveling Salesman Problem (TSP), and then propose a PSO-GA based solution by adopting the heuristic algorithm of Particle Swarm Optimization (PSO) to encode the offloading strategy and using Genetic Algorithm (GA) for particle update. The extensive experiments prove that the average performances of EPBN outperform the others with 17.23%, 23.97%, and 20.67% on micro-P, micro-R, and micro-F1 respectively, and the PSO-GA ensures that the offloading latency is reduced by nearly 5% compared to the suboptimal algorithm.