Return to work in young and middle-aged colorectal cancer survivors:Factors influencing self-efficacy,fear,resilience,and financial toxicity

BACKGROUND Return to work(RTW)serves as an indication for young and middle-aged colorectal cancer(CRC)survivors to resume their normal social lives.However,these survivors encounter significant challenges during their RTW process.Hence,scientific research is necessary to explore the barriers and facilitating factors of returning to work for young and middle-aged CRC survivors.AIM To examine the current RTW status among young and middle-aged CRC survivors and to analyze the impact of RTW self-efficacy(RTW-SE),fear of progression(FoP),eHealth literacy(eHL),family resilience(FR),and financial toxicity(FT)on their RTW outcomes.METHODS A cross-sectional investigation was adopted in this study.From September 2022 to February 2023,a total of 209 participants were recruited through a convenience sampling method from the gastrointestinal surgery department of a class A tertiary hospital in Chongqing.The investigation utilized a general information questionnaire alongside scales assessing RTW-SE,FoP,eHL,FR,and FT.To analyze the factors that influence RTW outcomes among young and middle-aged CRC survivors,Cox regression modeling and Kaplan-Meier survival analysis were used.RESULTS A total of 43.54%of the participants successfully returned to work,with an average RTW time of 100 days.Cox regression univariate analysis revealed that RTW-SE,FoP,eHL,FR,and FT were significantly different between the non-RTW and RTW groups(P<0.05).Furthermore,Cox regression multivariate analysis identified per capita family monthly income,job type,RTW-SE,and FR as independent influencing factors for RTW(P<0.05).CONCLUSION The RTW rate requires further improvement.Elevated levels of RTW-SE and FR were found to significantly increase RTW among young and middle-aged CRC survivors.Health professionals should focus on modifiable factors,such as RTW-SE and FR,to design targeted RTW support programs,thereby facilitating their timely reintegration into mainstream society.

The Innovation Research of Financial Early-Warning Index Measurement

The period economic fluctuation is vital for an enterprise to exist and further develop, it directly affect the enterprise financial health. So, it is significant to build up financial early-warning index and measure the warning condition that the enterprise faces and take the effective measures to eliminate. We criticize Altman’sZ calculating model and build up some new indexes for enterprise financial early-warning condition measuring and making sound decision.

An Early Warning Model of Financial Distress Prediction Based on Logistic-AHP-BP Neural Network Model

Ever since the appearance of"Implementation Measures for Suspending and Terminating the Listing of Loss-making Companies"in 2001,the delisting system has emerged.However,the proportion of delisted companies in China has never exceeded 1% each year.The number of delisted companies in the security market is far less than the number of companies with financial distress.The capital market lacks a good delisting system and investors lack risk identification capabilities.Financial risk is directly related to delisting risk.Therefore,an early warning model of financial distress prediction for China.s stock market can provide guidance to stakeholders such as listed companies and capital markets.This paper first explains the immature delisting system of China.s capital market and the overall high risk of listed companies.financial distress.Then,the paper further elaborates previous research on financial distress prediction model of listed companies and analyzes the advantages and disadvantages of different models.This paper chooses the Analytic Hierarchy Process(AHP)to screen out the main factors that affect the risk of financial distress.The main factors are included in Logistic regression model and BP neural network model for predicting financial distress of listed companies.The overall effect of two models are assessed and compared.Finally,this paper proposes policy implications according to empirical results.

Financial crisis early-warning model of listed companies based on predicted value

To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.

Construction and Optimization of a Financial Early Warning System Based on Big Data and Deep Learning Technology

New technologies such as big data,artificial intelligence,mobile internet,cloud computing,Internet of Things,and blockchain have brought about significant changes and development in the financial industry.Predicting the financial situation of enterprises,reducing the probability of uncertainty risks,and reducing the likelihood of financial crises have become important issues in enterprise financial crisis warning.In view of the issues in enterprise financial early warning systems such as lag,low accuracy,and high warning costs in data analysis,a financial early warning system based on big data and deep learning technology has been established,taking into account the different situations of listed and non-listed companies.This carries significance in improving the accuracy of enterprise financial early warning and promoting timely and effective decision-making.

Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring

Early warning of thermal runaway(TR)of lithium-ion batteries(LIBs)is a significant challenge in current application scenarios.Timely and effective TR early warning technology is urgently required considering the current fire safety situation of LIBs.In this work,we report an early warning method of TR with online electrochemical impedance spectroscopy(EIS)monitoring,which overcomes the shortcomings of warning methods based on traditional signals such as temperature,gas,and pressure with obvious delay and high cost.With in-situ data acquisition through accelerating rate calorimeter(ARC)-EIS test,the crucial features of TR were extracted using the RReliefF algorithm.TR mechanisms corresponding to the features at specific frequencies were analyzed.Finally,a three-level warning strategy for single battery,series module,and parallel module was formulated,which can successfully send out an early warning signal ahead of the self-heating temperature of battery under thermal abuse condition.The technology can provide a reliable basis for the timely intervention of battery thermal management and fire protection systems and is expected to be applied to electric vehicles and energy storage devices to realize early warning and improve battery safety.

Wearable electronic device for X-ray warning and health monitoring

The well-developed multifunctional wearable electronic device has fed the demand for human medicine and health monitoring in complex situations.However,the advancement of nuclear technology,especially irradiation medicine and safety inspections,has increased the exposure risk of irradiation safety workers.Traditional irradiation detectors are stiff and incompatible with the skin,and lack human health monitoring function,thus it’s vital to apply these flexible sensors for irradiation warning.Here,we report a novel composite gel device synthesized through solution processes by combining the Cs_(3)Cu_(2)I_(5):Zn nanoscintillator with the pre-patterned biocompatible gel,exhibiting a bi-functional response to motion/vibration sensing and sensitive irradiation warning.These wearable devices achieve a pressure sensitivity of up to 34 kPa^(-1)in a low-pressure range (0–3 kPa),a low limit of detection (LoD) down to 1.4 Pa,enabling health monitoring functions of pulse monitoring,finger bending,and elbow bending.Simultaneously,the device scintillates under X-ray irradiation among a wide dose rate range of 54–1167μGy_(air)s^(-1).The robust device shows no obvious signal loss after 4000 compression cycles and also excellent irradiation resistance over 50 days,broadening the path for designing and realizing new functional wearable devices.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

Effect of preload forces on multidimensional signal dynamic behaviours for battery early safety warning

Providing early safety warning for batteries in real-world applications is challenging.In this study,comprehensive thermal abuse experiments are conducted to clarify the multidimensional signal evolution of battery failure under various preload forces.The time-sequence relationship among expansion force,voltage,and temperature during thermal abuse under five categorised stages is revealed.Three characteristic peaks are identified for the expansion force,which correspond to venting,internal short-circuiting,and thermal runaway.In particular,an abnormal expansion force signal can be detected at temperatures as low as 42.4°C,followed by battery thermal runaway in approximately 6.5 min.Moreover,reducing the preload force can improve the effectiveness of the early-warning method via the expansion force.Specifically,reducing the preload force from 6000 to 1000 N prolongs the warning time(i.e.,227 to 398 s)before thermal runaway is triggered.Based on the results,a notable expansion force early-warning method is proposed that can successfully enable early safety warning approximately 375 s ahead of battery thermal runaway and effectively prevent failure propagation with module validation.This study provides a practical reference for the development of timely and accurate early-warning strategies as well as guidance for the design of safer battery systems.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND:This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores(EWSs)and three shock indices in early sepsis prediction in the emergency department(ED).METHODS:We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong.The primary outcome was sepsis(Sepsis-3 definition)within 48 h of ED presentation.Using c-statistics and the DeLong test,we compared 11 EWSs,including the National Early Warning Score 2(NEWS2),Modified Early Warning Score,and Worthing Physiological Scoring System(WPS),etc.,and three shock indices(the shock index[SI],modified shock index[MSI],and diastolic shock index[DSI]),with Systemic Inflammatory Response Syndrome(SIRS)and quick Sequential Organ Failure Assessment(qSOFA)in predicting the primary outcome,intensive care unit admission,and mortality at different time points.RESULTS:We analyzed 601 patients,of whom 166(27.6%)developed sepsis.NEWS2 had the highest point estimate(area under the receiver operating characteristic curve[AUROC]0.75,95%CI 0.70-0.79)and was significantly better than SIRS,qSOFA,other EWSs and shock indices,except WPS,at predicting the primary outcome.However,the pooled sensitivity and specificity of NEWS2≥5 for the prediction of sepsis were 0.45(95%CI 0.37-0.52)and 0.88(95%CI 0.85-0.91),respectively.The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.CONCLUSION:NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

ARHCS (Automatic Rainfall Half-Life Cluster System): A Landslides Early Warning System (LEWS) Using Cluster Analysis and Automatic Threshold Definition

A significant portion of Landslide Early Warning Systems (LEWS) relies on the definition of operational thresholds and the monitoring of cumulative rainfall for alert issuance. These thresholds can be obtained in various ways, but most often they are based on previous landslide data. This approach introduces several limitations. For instance, there is a requirement for the location to have been previously monitored in some way to have this type of information recorded. Another significant limitation is the need for information regarding the location and timing of incidents. Despite the current ease of obtaining location information (GPS, drone images, etc.), the timing of the event remains challenging to ascertain for a considerable portion of landslide data. Concerning rainfall monitoring, there are multiple ways to consider it, for instance, examining accumulations over various intervals (1 h, 6 h, 24 h, 72 h), as well as in the calculation of effective rainfall, which represents the precipitation that actually infiltrates the soil. However, in the vast majority of cases, both the thresholds and the rain monitoring approach are defined manually and subjectively, relying on the operators’ experience. This makes the process labor-intensive and time-consuming, hindering the establishment of a truly standardized and rapidly scalable methodology on a large scale. In this work, we propose a Landslides Early Warning System (LEWS) based on the concept of rainfall half-life and the determination of thresholds using Cluster Analysis and data inversion. The system is designed to be applied in extensive monitoring networks, such as the one utilized by Cemaden, Brazil’s National Center for Monitoring and Early Warning of Natural Disasters.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

The Impact of the Digital Economy on Financial Regulatory Costs: An Empirical Analysis Based on the Solow Growth Model

As a novel economic form,the digital economy is reshaping the financial regulatory landscape and significantly impacting regulatory costs.This paper incorporates the digital economy and financial regulatory costs into the classic Solow growth model,uncovering an inverted U-shaped relationship between them.A subsequent mechanism analysis explains the rationale behind this relationship.To empirically examine this relationship in China,the paper utilizes inter-provincial panel data from 2013 to 2021 and employs methodologies such as the two-way fixed effects and moderating effects models.These analyses have important implications for the sound and sustainable development of China’s financial industry.The findings indicate:(a)As China’s digital economy develops,its impact on financial regulatory costs follows an inverted U-shaped pattern,initially increasing and then declining.This conclusion remains valid after robustness tests.(b)The influence of the digital economy on regulatory costs depends on favorable external conditions.Specifically,the impact is more pronounced in regions and periods with better digital infrastructure and more abundant human capital.(c)Additionally,redundant resources moderate this impact,which can weaken the inverted U-shaped relationship.Our findings not only provide a theoretical foundation for understanding the impact of the digital economy on financial regulatory costs but also offer valuable policy insights for optimizing financial regulation in China.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Multi-Lever EarlyWarning forWind and Photovoltaic Power Ramp Events Based on Neural Network and Fuzzy Logic

With the increasing penetration of renewable energy in power system,renewable energy power ramp events(REPREs),dominated by wind power and photovoltaic power,pose significant threats to the secure and stable operation of power systems.This paper presents an early warning method for REPREs based on long short-term memory(LSTM)network and fuzzy logic.First,the warning levels of REPREs are defined by assessing the control costs of various power control measures.Then,the next 4-h power support capability of external grid is estimated by a tie line power predictionmodel,which is constructed based on the LSTMnetwork.Finally,considering the risk attitudes of dispatchers,fuzzy rules are employed to address the boundary value attribution of the early warning interval,improving the rationality of power ramp event early warning.Simulation results demonstrate that the proposed method can generate reasonable early warning levels for REPREs,guiding decision-making for control strategy.

Moderating effect of corporate financialization on the impact of climate policy on corporate green innovation:Evidence from China

With intensifying global climate change,humanity is confronted with unparalleled environmental challenges and risks.This study employs the staggered difference-in-difference model to examine the relationship between climate policy and green innovation in the corporate financialization context.Using Chinese-listed company data from 2008 to 2020,our analysis reveals a favorable correlation between China’s carbon emission trading policy(CCTP)and advancements in green innovation.Furthermore,we find that the level of corporate financialization moderates this correlation,diminishing the driving effect of CCTP on green innovation.Additionally,results of heterogeneity analysis show that this moderating consequence is more evident in non-state owned and low-digitization enterprises compared with state-owned and high-digitization ones.Our findings contribute to the existing literature by clarifying the interaction between CCTP,green innovation,and corporate financialization.Our research provides valuable insights for policymakers and stakeholders seeking to strengthen climate policies and encourages green innovation in different types of businesses.