Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series. Most of the existing work on 1D-CNN treats the kernel size as a hyper-parameter and tries to find the proper kernel size through a grid search which is time-consuming and is inefficient. This paper theoretically analyses how kernel size impacts the performance of 1D-CNN. Considering the importance of kernel size, we propose a novel Omni-Scale 1D-CNN (OS-CNN) architecture to capture the proper kernel size during the model learning period. A specific design for kernel size configuration is developed which enables us to assemble very few kernel-size options to represent more receptive fields. The proposed OS-CNN method is evaluated using the UCR archive with 85 datasets. The experiment results demonstrate that our method is a stronger baseline in multiple performance indicators, including the critical difference diagram, counts of wins, and average accuracy. We also published the experimental source codes at GitHub (https://github.com/Wensi-Tang/OS-CNN/)

OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning

We propose OmniLytics, a blockchain-based secure data trading marketplace for machine learning applications. Utilizing OmniLytics, many distributed data owners can contribute their private data to collectively train an ML model requested by some model owners, and receive compensation for data contribution. OmniLytics enables such model training while simultaneously providing 1) model security against curious data owners; 2) data security against the curious model and data owners; 3) resilience to malicious data owners who provide faulty results to poison model training; and 4) resilience to malicious model owners who intend to evade payment. OmniLytics is implemented as a blockchain smart contract to guarantee the atomicity of payment. In OmniLytics, a model owner splits its model into the private and public parts and publishes the public part on the contract. Through the execution of the contract, the participating data owners securely aggregate their locally trained models to update the model owner\u27s public model and receive reimbursement through the contract. We implement a working prototype of OmniLytics on Ethereum blockchain and perform extensive experiments to measure its gas cost, execution time, and model quality under various parameter combinations. For training a CNN on the MNIST dataset, the MO is able to boost its model accuracy from 62% to 83% within 500ms in blockchain processing time.This demonstrates the effectiveness of OmniLytics for practical deployment

Establishment and verification of a nomogram that predicts the risk for coronary slow flow

BackgroundCoronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors.MethodsIn this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration.ResultsN-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram.ConclusionThe constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

Intermodal transportation of full and empty containers in harbor-inland regions based on revenue management

Abstract Introduction The transport of full containers and repositioning of empty containers are essential for the operation of a container logistic enterprise. In a large inland region, railway and road transportation take their own advantages for container transportation in terms of transport efficiency and costs. The combination of the two modes creates larger possibility for logistic enterprise to provide efficient service and reduce operation costs. Thus, this paper aims to optimize the inland intermodal transportation of full and empty containers. Methods Different from cost minimum model, an integer programming model is proposed based on revenue management, making the model more reasonable in practice where logistic enterprises have the right to reject transportation demands from customers considering transport profits and capacity limitations. The real cases of transportation networks of Northeast China and parts of the southern Europe are applied to evaluate and describe the model and performance. Results The results indicate that the proposed intermodal transportation optimization for both full and empty containers is feasible and profitable for logistic enterprises. The efficiency of container usage increases since we take the conversion of containers during transportation into account. Results also imply an inappropriate pricing strategy at certain inland depots in the case. Conclusions This paper provides an optimization method for the intermodal transportation of full and empty containers in harbor-inland regions / hinterlands. Different from cost minimization in the literature, this paper models the transportation problem based on revenue management. That is, the model aims to maximize profits of logistic enterprise with the specific constraints in container transportation, as well as demand rejection. The model is proofed to be feasible in the real transportation networks and can create more profits for transportation enterprises. The analysis of real cases show that the total profits can be enlarged by considering the conversion of containers during transportation. In addition, it is verified that the optimization of inland transportation of empty and full containers reduces the operating costs of logistic enterprises. Moreover, results uncover the fact that a governed pricing policy at Suifenhe is not reasonable and may deteriorate the future export environment there. For the case based on part of southern Europe, it seems that the port, customer, inland transport operator and possibly container shipping company in Europe may benefit by establishing appropriate inland depots

Comparison of clinical efficacy of robot-assisted and freehand core decompression in the treatment of osteonecrosis of the femoral head: a systematic review and meta-analysis

Abstract Objective At present, the core decompression (CD) has become the main surgical procedure for the treatment of osteonecrosis of the femoral head (ONFH); however, the CD surgery requires high operator experience and repeated fluoroscopy increases the radiation damage to patients, and medical staff. This article compares the clinical efficacy of robot-assisted and freehand CD for ONFH by meta-analysis. Methods Computer searches of PubMed, Web of Science, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, China Science and Technology Journal Database, WanFang, and Chinese BioMedical Literature Database were conducted from the time of database inception to November 15, 2023. The literature on the clinical efficacy of robot-assisted and freehand CD in the treatment of ONFH was collected. Two researchers independently screened the literature according to the inclusion and exclusion criteria, extracted data, and strictly evaluated the quality of the included literature. Outcome measures encompassed operative duration, intraoperative blood loss volume, frequency of intraoperative fluoroscopies, visual analog scale (VAS) score, Harris hip score (HHS), complications, and radiographic progression. Data synthesis was carried out using Review Manager 5.4.1 software. The quality of evidence was evaluated according to Grades of Recommendation Assessment Development and Evaluation (GRADE) standards. Results Seven retrospective cohort studies involving 355 patients were included in the study. The results of meta-analysis showed that in the robot-assisted group, the operative duration (MD = -17.60, 95% CI: -23.41 to -11.78, P < 0.001), intraoperative blood loss volume (MD = -19.98, 95% CI: -28.84 to -11.11, P < 0.001), frequency of intraoperative fluoroscopies (MD = -6.60, 95% CI: -9.01 to -4.20, P < 0.001), and ΔVAS score (MD = -0.45, 95% CI: -0.67 to -0.22, P < 0.001) were significantly better than those in the freehand group. The GRADE evidence evaluation showed ΔVAS score as low quality and other indicators as very low quality. There was no significant difference in the terms of ΔHHS (MD = 0.51, 95% CI: -1.34 to 2.35, P = 0.59), complications (RR = 0.30, 95% CI: 0.03 to 2.74, P = 0.29), and radiographic progression (RR = 0.50, 95% CI: 0.25 to 1.02, P = 0.06) between the two groups. Conclusion There is limited evidence showing the benefit of robot-assisted therapy for treatment of ONFH patients, and much of it is of low quality. Therefore, caution should be exercised in interpreting these results. It is recommended that more high-quality studies be conducted to validate these findings in future studies. Systematic review registration https://www.crd.york.ac.uk/prospero/ #recordDetails, CRD42023420593

RESEARCH ARTICLE Effects of Interactions of Auxin-Producing Bacteria and Bacterial-Feeding Nematodes on Regulation of Peanut Growths

The influences of an IAA (indole-3-acetic acid)-producing bacterium (Bacillus megaterium) and two bacterial-feeding nematodes (Cephalobus sp. orMesorhabditis sp.) on the growth of peanut (Arachis hypogaea L. cv. Haihua 1) after various durations of time were investigat-ed in natural soils. The addition of bacteria and nematodes and incubation time all signifi-cantly affected plant growth, plant root growth, plant nutrient concentrations, soil nutrient concentrations, soil microorganisms and soil auxin concentration. The addition of nema-todes caused greater increases in these indices than those of bacteria, while the addition of the combination of bacteria and nematodes caused further increases. After 42-day growth, the increases in soil respiration differed between the additions of two kinds of nematodes because of differences in their life strategies. The effects of the bacteria and nematodes on the nutrient and hormone concentrations were responsible for the increases in plant growth. These results indicate the potential for promoting plant growth via the addition of nematodes and bacteria to soil

A data-driven hybrid control framework to improve transit performance

This paper presents a data-driven hybrid control (DDHC) framework that can arrange adaptive control strategies for vehicles to effectively improve the transit performance of the public transport system. The framework depicts a powerful combination of a data-driven control method that is used to imitate the control behaviour of dispatchers and a mathematical optimization method. Three components comprise the DDHC framework: a data-driven control module, a performance module, and an optimization module. The data-driven control module contains a random forest model which is adopted to justify whether to intervene in the operation of a bus line, and if so, which vehicles should be controlled and what type of control strategy should be taken – an acceleration strategy or deceleration strategy. The performance module including vehicle operation state models is used to describe the system evolution. The last component optimizes the specific control actions – which type of acceleration or deceleration strategy should be adopted – by minimizing total passenger travel time. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. The results show that the DDHC framework with reasonable parameters can suit the needs of real-time control in complex traffic environments