Prediction System Research Articles

Design of a hazard prediction system with intelligent multimodal fusion based on artificial intelligence & internet of things technology: taking a crib as an example

Problem How to design an intelligent multimodal fusion hazard prediction system using AIoT (Artificial Intelligence & Internet of Things) technology to predict some potential dangers. This article will provide some ideas and methods. Introduction This article designs an intelligent multimodal fusion hazard prediction system based on AIoT technology. The system mainly consists of an IoT hardware device and an AI multimodal and multi-dimensional Hazard Prediction Algorithm. Method This article will take baby cribs as an example, using this system to empower traditional baby cribs and transform them into intelligent baby cribs. Results In this example, the system can detect the real-time status of the baby and predict upcoming dangers, including kicking the quilt, wetting the bed, fever, crying, climbing over the crib, and turning over. Hypothesis Assuming that intelligent systems can detect the status of infants in real-time and make predictions before potential dangers occur, to promptly alert parents. Perhaps it can prevent some irreversible dangers from occurring. Background This system solves the pain point problem of parents having to take care of their babies after working hard. Purpose In this scenario, the system provides a design scheme for an intelligent multimodal fusion hazard prediction system with a temporary care function. The application of this system to baby cribs not only reduces the burden on parents but also ensures the safety and comfort of the baby. Transferability At the same time, the system has transferability, and its design concept can be transferred to other application scenarios, such as hospital care for infants, young children, or elderly patients, as well as childcare stations for abandoned children in society. Significance This innovative system design scheme has a positive significance for family harmony and social development.

Prediction of CRT response using a novel CRT responder score in an Asian patient population

Abstract Introduction Cardiac Resynchronization Therapy (CRT) guidelines, derived from trials among U.S. and European patients, assign weaker recommendations to those with midrange QRS duration (<150ms). Patient height and body size may modulate CRT efficacy. Moreover, these factors may affect CRT utilization and efficacy in Asia. Aim The purpose of this analysis was to determine the prediction scoring system model for CRT responders in a Japanese patient population. Methods We analyzed the database of the Multicenter Prospective Pilot Study to Test LV Intracardiac Conduction Time as a Predictor of CRT Response (BIO|SELECT) which was a single-arm study conducted at 28 hospitals in Japan. This study enrolled patients eligible for a de-novo CRT-defibrillator (CRT-D) based on current guideline indications, who had no recent or planned cardiac surgery and were planned to receive a Sentus ProMRI OTW quadripolar LV lead and a BIOTRONIK CRT-D with multipole pacing feature. Patients were followed for 7 months after implantation. Four accepted parameters to define CRT response [LVESV (cut-off -15%), LVEF (+5%), BNP (-25%), and NYHA class (one class improvement)] were combined into a composite benefit index (CBI) by forming the normalized sum, with CBI = 4.0 meaning response. The CBI was found to be more robust than the individual 4 parameters due to the reduced influence of noise and measurement errors in single parameters. Several factors were studied to create the scoring tool. The CRT responder score was the sum of these individual scores (range 0-100). To construct a predictive scoring system, logistic regression analysis was performed with a backward selection procedure. In addition, the Akaike information criterion (AIC) was applied to determine the best model among those tested. The score was then assigned employing the β coefficient of each variable divided by the reference value. The model's calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. Results A total of 196 patients were enrolled in this study (mean age 69 years, mean LVEF 30%, left bundle-branch block [LBBB] 58%). The Ooccurrence of CRT response during the 7-month follow-up was 54%. Future CRT responders were significantly predicted by low age (< 70 years)(18 points), female (19 points), prior heart failure hospitalization (16 points), body mass index (<20) (17 points), QRS/height (<0.858)(30 points). This score predicted CRT response well (area under curve 0.72; 95% CI 0.643-0.798). When the cut-off of 63 points or more was applied the sensitivity was 0.56, the specificity was 0.72, and the negative predictive value was 0.58. Conclusions The CRT responder score is a novel approach for the prediction of CRT response, but should be validated and further improved to increase its predictive value. This approach highlights the role of preventing the development of severe heart failure and could help to identify individuals who might benefit from CRT.

Next-generation public health surveillance: extreme heat event prediction and monitoring system

Abstract Background Extreme Heat Events (EHEs) are a growing threat to public health. The increase in the frequency of occurrence of once rarer EHEs and the rise in their average temperatures are dangerously drastic for public health outcomes. Despite this, existing public health surveillance (PHS) systems fail to leverage IoT and AI technologies to facilitate real-time, continuous monitoring of EHE indicators and its associated health risks. This study closes this gap by proposing a comprehensive PHS system that can, in real-time monitor and predict EHE indicators to provide timely alerts to public health authorities. Methods The EHE PHS system collects EHE detection metrics, including indoor temperature and humidity levels, from IoT sensors and thermostats to map them across Canada, primarily focusing on low-income communities. The system also integrates historical climate data (surface temperature, humidity, wind speed and direction, and air quality indicators) from Environment Canada. The system uses the data to train initial prediction models including CNN, LSTM and GNN. The validated model will be integrated into the system, enabling real-time EHE prediction. The output will be displayed in user-friendly visualizations on the EHE PHS system’s dashboard capabilities. Results The system allows for the analysis of real-time data through dashboards and provides alerts when certain indicators (e.g. excessive or prolonged heat) detrimental to public health outcomes are detected. The alerts enable valuable lead time for public health authorities to implement proactive measures, such as issuing heat advisories and deploying resources to vulnerable areas. Conclusions The EHE PHS system will serve as a blueprint for global public health researchers, utilizing IoT and AI technologies for proactive crisis prevention. This knowledge will inform the development of heat-resilient policies and set a precedent for global public health crisis prevention. Key messages • The proposed EHE PHS system uses AI and IoT technologies, to enhance EHE prediction, risk identification, and real-time monitoring for effective public health interventions. • By integrating advanced predictive models and environmental data, the system facilitates early detection of EHE risk, which can significantly mitigate the health impact on vulnerable populations.

Effect of spironolactone wash-out on albuminuria after long-term treatment in individuals with type 2 diabetes and high risk of kidney disease-An observational follow-up of the PRIORITY study.

This study aimed to explore the effect of discontinuation of long-term spironolactone treatment on markers of kidney function in individuals with type 2 diabetes (T2D) at high risk of kidney disease enrolled in the Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria (PRIORITY) study. An observational study following the nested randomised part of the PRIORITY study was conducted. A total of 115 individuals with T2D and normoalbuminuria but high risk for progression based on urinary proteomics, randomised to daily spironolactone (n = 50) or placebo (n = 65) for a median of 2.5 years, were re-examined approximately 6 weeks after the final visit in the PRIORITY study. Primary endpoint was relative change in geometric mean of urinary albumin-creatinine ratio (UACR) between the final visit in PRIORITY (baseline) and follow-up. Secondary endpoints were change in estimated glomerular filtration rate (eGFR), systolic blood pressure (SBP) and serum potassium. No change in UACR was observed in neither the spironolactone (geometric mean change: 17%; 95% CI -12, 55; p = 0.28) nor the placebo (5%; 95% CI -13, 26; p = 0.63) group at follow-up. No difference in UACR between the groups was observed at follow-up (relative difference in geometric mean: 11%, 95% CI -26, 67; p = 0.60). For eGFR and SBP, an increase after discontinuation of spironolactone was observed, as well as for SBP after placebo discontinuation. Potassium levels were lower after discontinuation of spironolactone, but higher after placebo discontinuation (all p < 0.05). UACR did not change after discontinuation of long-term treatment with spironolactone. However, an increase in eGFR was observed supporting a haemodynamic effect of spironolactone in the kidneys.

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

Co-simulation Architecture and Platform Establishment Method for Cloud-Based Predictive Cruise Control System

The New Healthcare: AI-Driven Population Stratification, Proactive Medicine, and Predictive Analysis

Abstract Background The Covid-19 pandemic has highlighted structural and functional weakness of local health services. Italy carried out its reorganization with DM 77/2022, financed by Mission 6 PNRR of the CE. The model adopted aims to move from a reactive to proactive medicine, to be implemented close to the places where citizens live. The priority objective is to know data on the progress of chronic pathologies and non self-sufficiency using advanced digitalization tools. Methods 14 types of administrative data from 2022 to 2023, concerning 180000 (ISTAT data) people in the Lecce Social Health District (DSS Lecce), were analyzed with AI (Chat GPT). Results Thanks to algorithms, 6 categories of clinical complexity were identified. The analysis showed that the population assisted is 198696, for temporary presences, with the most represented age group being 50-55 years. Births decreased by 36% in 10 years (from 1681 to 1076), with 2197 deaths in 2023. 42,38% of the population (average age 38,58 years) did not use healthcare resources, while 57,62% (average age 54,19 years) required them. 48646 patients have cardiovascular disease, but only 10% with heart failure were actively monitored. 25047 have metabolic disease, 11195 are diabetics, 5929 have chronic respiratory diseases, 8167 chronic neurological diseases and 907 are cancer patients. Increasing age leads to polymorbidity and show signs of social, economic and health fragility. Conclusions the strategic objective of the Lecce DSS was management of chronicity according to specific care pathways, PDTA. Create healthcare teams capable of being case managers depending on the level of complexity (family doctors, nurses, etc). With the 2024 data there will be 3 consecutive years of analysis which will allow the development of predictive systems to identify population clusters at risk of increasing complexity and in need of proactive medical interventions, use of telemedicine with the support of a Territorial Operations Center. Key messages • AI-driven stratification supports personalized health plans to improve healthcare delivery, patient outcomes, and optimize resource allocation. • A new model of Population Health Management supported by Artificial Intelligence.

Evaluating the Effectiveness of Predictive Policing in Nigeria: A Qualitative study of law Enforcement Practices in Lagos State

This qualitative study evaluates the effectiveness of predictive policing technologies by examining the implementation within law enforcement operations in Lagos State. The research aims to explore how predictive systems influence crime prevention, and decision-making processes in policing. Drawing on semi-structured interviews with NDLEA officers, the study reveals the perceived benefits and challenges of integrating predictive policing tools into traditional methods. Through thematic analysis of interviews and review of secondary sources, the research identifies key themes such as the tension between technology and human judgement, and the potential for over-reliance on system predictions. The findings indicate that while predictive policing can improve efficiency in crime hotspot identification, its efficacy is undermined by concerns over privacy, and fairness. The study concludes that for predictive policing to be effective, it must come with strong regulatory frameworks and ethical guidelines to ensure support from technologies. This research contributes to the growing discourse on the intersection of technology and law enforcement, offering insights into how predictive policing can evolve to better serve the people.

NEWS, SIRS and qSOFA criteria for predicting sepsis and sepsis with high risk of death in emergency room: A comparison study and improved predictive models based on local data from CETAT and MIMIC-IV databases

Early identification of sepsis in emergency room patients is critical for initiating timely interventions, highlighting the need for effective predictive scoring systems. A retrospective observational study was conducted using data from the CETAT database collected between December 2019 and October 2021. The study evaluated how well the SIRS, qSOFA, and NEWS scoring systems, along with logistic regression models, predict sepsis and high-risk sepsis in emergency room patients. The logistic regression models were further optimized by incorporating additional features based on local data.A total of 12,799 patients were analyzed, including 1,360 sepsis cases, of which 373 were classified as high-risk sepsis. The NEWS score demonstrated superior predictive performance compared to qSOFA and SIRS, with an AUC-ROC of 0.737 (95% CI 0.72–0.75) for sepsis and 0.653 (95% CI 0.62–0.69) for high-risk sepsis. After optimization, the NEWS-based model improved to an AUC-ROC of 0.756 (95% CI 0.74–0.77) for sepsis and 0.718 (95% CI 0.69–0.75) for high-risk sepsis. Further enhancement was observed with the inclusion of additional clinical variables, resulting in AUC-ROC values of 0.834 (95% CI 0.82–0.85) for sepsis and 0.756 (95% CI 0.73–0.78) for high-risk sepsis. Data from the MIMIC-IV database, which included sepsis status and relevant variables for SIRS, qSOFA, and NEWS score calculations, confirmed that the optimized NEWS-based model improved the sepsis prediction AUC-ROC from 0.690 (95% CI 0.68–0.70) to 0.708 (95% CI 0.70–0.72), and consistently outperformed qSOFA and SIRS in sepsis prediction.

Air quality prediction and control systems using machine learning and adaptive neuro-fuzzy inference system

Accurately predicting air quality concentrations is a challenging task due to the complex interactions of pollutants and their reliance on nonlinear processes. This study introduces an innovative approach in environmental engineering, employing artificial intelligence techniques to forecast air quality in Semnan, Iran. Comprehensive data on seven different pollutants was initially collected and analyzed. Then, several machine learning (ML) models were rigorously evaluated for their performance, and a detailed analysis was conducted. By incorporating these advanced technologies, the study aims to create a reliable framework for air quality prediction, with a particular focus on the case study in Iran. The results indicated that the adaptive neuro-fuzzy inference system (ANFIS) was the most effective method for predicting air quality across different seasons, showing high reliability across all datasets.

Distinct maternofetal immune signatures delineate preterm birth onset following urinary tract infection.

Preterm birth is the leading cause of infant mortality resulting in over one million neonatal deaths annually. Maternal urinary tract infection (UTI) during pregnancy increases risk for preterm birth; however, biological processes mediating UTI-associated preterm birth are not well-described. We established a murine maternal UTI model in which challenge with uropathogenic E. coli resulted in preterm birth in about half of dams. Dams experiencing preterm birth displayed excessive bladder inflammation and altered uteroplacental T cell polarization compared to non-laboring infected dams, with no differences in bacterial burdens. Additional factors associated with preterm birth included higher proportions of male fetuses and lower maternal serum IL-10. Furthermore, exogenous maternal IL-10 treatment absolved UTI-associated preterm birth but contributed to fetal growth restriction in this model. Using urine samples from a cohort of human pregnancies with or without UTI, we correlated urinary cytokines with birth outcomes and urine culture status. These analyses yielded a non-invasive, highly predictive three-model system for evaluating preterm birth risk implicating cytokines IL-10, IL-15, IL-1β, and IL-1RA. Our unique bimodal murine model coupled with patient samples provides a platform to investigate immunological and microbial factors governing UTI-associated preterm birth, revealing novel therapeutic opportunities to predict or prevent preterm birth.

A multi-step regularity assessment and joint prediction system for ordering time series based on entropy and deep learning

Customer maintenance is of vital importance to the enterprise management. Valuable assessment and efficient prediction for customer ordering behavior can offer better decision-making and reduce business costs significantly. According to existing studies about customer behavior regularity segment and demand prediction most focus on e-commerce and other fields with large amount of data, making them not suitable for small enterprises and data features like sparsity and outliers are not mined when doing regularity quantification. Additionally, more and more complex network structures for demand prediction are proposed, which builds on the assumption that all the samples have predictive value, ignoring the fine-grained analysis of different time series regularity with high cost. To deal with the above issues, a multi-step regularity assessment and joint prediction system for ordering time series is proposed. For extracting features, comprehensive assessment of customer regularity based on entropy weight method with the result of predictability quantification using K-Means clustering algorithm, real entropy, LZW algorithm and anomaly detection adopting Isolation Forest algorithm not only gives an objective result to ‘how high the regularity of customers is’, filling the gap in the field of regularity quantification, but also provides a theoretical basis for demand prediction models selection. Prediction models: Random Forest regression, XGBoost, CNN and LSTM network are experimented with sMAPE and MSLE for performance evaluation to verify the effectiveness of the proposed regularity quantitation method. Moreover, a merged CNN-BiLSTM neural network model is established for predicting those customers with low regularity and difficult to predict by traditional machine leaning algorithms, which performs better on the data set compared to others. Random Forest is still used for prediction of customers with high regularity due to its high training efficiency. Finally, the results of prediction, regularity quantification, and classification are output from the intelligent system, which is capable of providing scientific basis for corporate strategy decision and has highly extendibility in other enterprises and fields for follow-up research.

Neural networks pipeline for quality management in IVF laboratory

This study introduces a novel neural network-based pipeline for predicting clinical pregnancy rates in IVF treatments, integrating both clinical and laboratory data. We developed a metamodel combining deep neural networks and Kolmogorov-Arnold networks, leveraging their complementary strengths to enhance predictive accuracy and interpretability. The metamodel achieved robust performance metrics after training and fitting on 11500 clinical cases: accuracy = 0.72, AUC = 0.75, F1 score = 0.60, and Matthews Correlation Coefficient of 0.42. According to morpho-kinetical embryo evaluation, our model’s PRC of 0.66 significantly improves over existing time-lapse systems for pregnancy prediction, demonstrating better handling of imbalanced clinical data. The metamodel’s calibration metrics (Brier score = 0.20, expected calibration error = 0.06, maximum calibration error = 0.12, Hosmer-Lemeshow test p-value = 0.06) indicate robust reliability in predicting clinical pregnancy outcomes. We validated the model’s reproducibility using an independent dataset of 665 treatment cycles, showing close alignment between predicted and actual pregnancy rates (58.9% vs. 59.1%). With the Bayesian method, we proposed a robust framework for integrating historical data with real-time predictions from neural networks, enabling a transition from retrospective to prospective analysis. Our approach extends beyond conventional embryo selection, incorporating post-analytical phase evaluation in the IVF laboratory. This comprehensive framework enables detailed analysis across different patient subpopulations and time periods, facilitating the identification of systemic issues and IVF protocol optimization. The model’s ability to track pregnancy probabilities over time and staff members allows for both outcome prediction and retrospective and prospective assessment of IVF treatment efficacy, providing a data-driven strategy for continuous improvement in assisted reproductive technology.

Development and validation of a predictive scoring system for hypoglycaemic agents for optimal control of blood glucose during glucocorticoid therapy.

Glucocorticoid (GC) treatments are often used. There is limited information on the prediction of hyperglycaemia after GC administration. This study aimed to identify the risk factors for hyperglycaemia after glucocorticoid (GC) administration and the need for hypoglycaemic agents to correct it and to develop and validate a novel scoring system for predicting GC-induced hyperglycaemia. In a development set, 508 adults receiving prednisolone (PSL) for the first time were divided into two groups based on treatment with or without hypoglycaemic agents. Clinical and laboratory parameters were compared, and risk factors were identified using logistic regression analysis after performing univariate analyses between the two groups. A point-addition scoring system with several categories and coefficients for each risk factor was constructed to predict the need for hypoglycaemic agents. The scoring system was then applied and validated on two validation sets: A and B. Older age, higher glycated haemoglobin percentage, body mass index and initial PSL dosage were identified as risk factors. The sensitivity, specificity and accuracy of the scoring system were 70.6%, 81.9% and 77.1% in the development set; 75.8%, 78.4% and 77.4% in validation set A; and 79.4%, 73.9% and 75.3% in validation set B respectively. By fitting the total score in the development set and the probability of hyperglycaemia to a logistic curve, a figure was created to show the probability of GC-induced hyperglycaemia in patients scheduled to receive GC. This scoring system is a novel, valid and reliable tool for predicting GC-induced hyperglycaemia and the need for hypoglycaemic agents to correct it.

A new prognostic scoring system for newly diagnosed multiple myeloma in the era of new drugs.

We developed a new predictive staging system to explore the heterogeneity of survival in newly diagnosed multiple myeloma (NDMM) patients in the real world. In this retrospective study, we evaluated the predictive value of cytogenetic abnormal and clinical data in 375 patients with NDMM at our center. Established a weighted MM prognostic scoring system risk model and validated its predicted PFS and OS by external cohort. Elevated lactate dehydrogenase levels (1 point), international staging system stage II/III (1 point), 1q21+ ≥ 52.75% (0.5 point), del (17p) ≥ 3.5% (0.5 point), and t (14;16) ≥ 35.25% (1 point) had independent prognostic significance. Patients were further divided into three risk groups: low (I) (score 0-0.5, 16.5%), intermediate (II) (score 1, 46.7%), and high (III) (score 1.5-3, 36.8%). In the training cohort, the 3-year PFS was 79.5% vs. 65.3% vs. 40.3% (p < 0.001), and the 3-year OS was 87.7% vs.70.1% vs. 55% (p < 0.001) for the three risk groups. In the external validation cohort, the 3-year PFS was 85.5% vs.61.2% vs. 43.1% (p < 0.001) and the 3-year OS was 91.4% vs.83.5% vs. 56.9% (p < 0.001) for the three risk groups. The risk stratification of this model shows good discrimination and calibration, and its application in clinical practice can improve the risk assessment of patients with NDMM and guide personalized treatment strategies.

RescueNow: Real-time SOS and Predictive Women's Safety System

This paper presents a comprehensive women’s safety application designed to address the growing concerns of personal safety through technological intervention. The system integrates multiple advanced features such as real-time SOS alerts, geofencing to detect high-risk areas, AI-driven crime predictions, and multiple SOS activation methods, including voice commands, shake detection, and button press. Additionally, the app employs silent recording functionality to gather evidence in distress situations and incorporates secure communication channels for contacting law enforcement. The application also supports community-driven safety ratings and crime reporting. Tested with real-world scenarios, the system demonstrates high accuracy in predictive analytics and provides a seamless, scalable solution for enhancing women’s safety. This project contributes to the field of personal safety technologies by introducing a multi-faceted, AI-enabled framework for real-time risk mitigation. Key Words: Women’s safety, SOS alerts, AI crime prediction, geofencing, real-time GPS tracking, predictive analytics, Firebase Cloud Messaging, Twilio, silent recording, crime reporting.

Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm

The need for real-time monitoring and alerting systems for space weather hazards has grown significantly in the last two decades. One of the most important challenges for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of an SPE, i.e., they are based on the classification approach. This work is oriented toward the successful implementation of onboard prediction systems, which is essential for the future of space exploration. We present a simple and efficient machine learning regression algorithm that is able to forecast the energetic proton flux up to 1 hr ahead by exploiting features derived from the electron flux only. This approach could be helpful in improving monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance.

A Novel Predictive Modeling for Student Attrition Utilizing Machine Learning and Sustainable Big Data Analytics

Student attrition poses significant societal and economic challenges, leading to unemployment, lower earnings, and other adverse outcomes for individuals and communities. To address this, predictive systems leveraging machine learning and big data aim to identify at-risk students early and intervene effectively. This study leverages big data and machine learning to identify key parameters influencing student dropout, develop a predictive model, and enable real-time monitoring and timely interventions by educational authorities. Two preliminary trials refined machine learning models, established evaluation standards, and optimized hyperparameters. These trials facilitated the systematic exploration of model performance and data quality assessment. Achieving close to 100% accuracy in dropout prediction, the study identifies academic performance as the primary influencer, with early-year subjects like Mechanics and Materials, Design of Machine Elements, and Instrumentation and Control having a significant impact. The longitudinal effect of these subjects on attrition underscores the importance of early intervention. Proposed solutions include early engagement and support or restructuring courses to better accommodate novice learners, aiming to reduce attrition rates.

Development and validation of a novel prognostic prediction system based on GLIM-defined malnutrition for colorectal cancer patients post-radical surgery.

Malnutrition often occurs in patients with colorectal cancer. This study aims to develop a predictive model based on GLIM criteria for patients with colorectal cancer who underwent radical surgery. From December 2015 to May 2021, patients with colorectal cancer who underwent radical surgery at our center were recruited for this study. We prospectively collected data on GLIM-defined malnutrition and other clinicopathological characteristics. Using Cox regeneration, we developed a novel nomogram for prognostic prediction, which was validated and compared to traditional nutritional factors for predictive accuracy. Among the 983 patients enrolled in this study, malnutrition was identified in 233 (23.70%) patients. Multivariate analysis indicated that GLIM-defined malnutrition is the independent risk factor for overall survival (HR = 1.793, 95% CI = 1.390-2.313 for moderate malnutrition and HR = 3.485, 95% CI = 2.087-5.818 for severe malnutrition). The novel nomogram based on the GLIM criteria demonstrated a better performance than existing criteria, with AUC of 0.729, 0.703, and 0.683 for 1-year, 3-year, and 5-year OS, respectively, in the validation cohort. In addition, the risk score determined by this system exhibited significantly poorer short-term and long-term clinical outcomes in high-risk groups in both malnourished and well-nourished patients. Combining handgrip strength, serum albumin level, and TNM stage would help improve the predictive effect of GLIM criteria for colorectal cancer patients post-radical surgery and benefit the individual prognostic prediction of colorectal cancer.

How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.

Assessment of impact load effect on self-sensing of cement composites incorporating hybrid silicon carbide-graphite under various environmental conditions

This study investigates the self-sensing abilities of hybrid silicon carbide-graphite cement composites (HSCGC) under various environmental conditions, focusing on dynamic impacts. Integrating silicon carbide (SiC) and graphite enhances the mechanical and electrical properties of these composites. Tests showed that composites with 30 % SiC and 2 % graphite had a 14.1 % increase in compressive strength and up to 85 % decrease in electrical resistivity. Water absorption reduced by 3.25 %, indicating better durability. Impact resistance ratios (IRR) varied with humidity and impact angles, with the highest IRR observed at 70 % humidity and a 30-degree angle. Self-sensing responses indicated that higher SiC and graphite content maintained stable conductivity, especially at 60 and 90-degree angles. Temperature changes affected resistivity, negligible at −10°C and increased at 45°C. The study concludes that HSCGCs are suitable for smart construction materials, capable of real-time environmental and structural monitoring, advancing predictive models and control systems.