Search Results (1,427)

Purpose: Preclinical studies suggest that interleukin-1β (IL-1β) influences tumor behavior in non-small cell lung cancer (NSCLC). While the CANTOS trial demonstrated reduced lung cancer incidence with IL-1β inhibition, the CANOPY trials failed to show survival benefit when combined with chemoimmunotherapy. The role of IL-1β in NSCLC with oncogenic mutations remains unclear. We evaluated the prognostic and predictive significance of IL-1β expression across NSCLC subtypes. Methods: We analyzed 21,698 NSCLC tumors profiled by Caris Life Sciences using DNA and RNA next-generation sequencing. IL-1β expression was stratified into quartiles (Q1: lowest 25%, Q4: highest 25%). Real-world overall survival (OS) and time on treatment (TOT) were obtained from insurance claims. Statistical comparisons used Chi-square, Fisher’s exact, or Mann–Whitney U tests. Survival outcomes were assessed with Cox models. Results: Across unselected NSCLC patients, low IL-1β expression (Q1) was associated with modestly longer OS versus high expression (Q4) (median OS 19.5 vs. 17.4 months; HR 0.94; p < 0.0001). This effect was more pronounced in EGFR-mutant adenocarcinoma (36.7 vs. 27.2 months; HR 0.76; p < 0.001) and ALK fusion-positive NSCLC (53.0 vs. 35.2 months; HR 0.62; p = 0.002). In NSCLC without targetable mutations, IL-1β expression was not prognostic. In KRAS-mutant adenocarcinoma, high IL-1β expression was associated with modestly longer TOT on immunotherapy (7.4 vs. 6.4 months; HR 1.15; p = 0.041), but not OS. High IL-1β expression correlated positively with TP53 mutation, TMB-high, and PD-L1 expression and inversely with EGFR, KRAS, BRAF, ERBB2, KEAP1, and STK11 mutations. Conclusions: IL-1β expression is a potential prognostic and predictive biomarker in NSCLC, associated with survival outcomes in defined molecular subsets. These findings suggest that IL-1β-targeted strategies may be particularly relevant in EGFR- or ALK-altered tumors. Full article

Background/Objectives: Existing research has highlighted the stress associated with the transition from student to practitioner among newly hired nurses, often resulting in diminished professional quality of life (ProQOL). However, there remains a dearth of understanding regarding the impact of the teaching methods during the COVID-19 pandemic on this transition period. This study aims to test a model assessing the mediating role of ProQOL in the association between structural empowerment and successful transition among newly hired nurses who underwent education during the COVID-19 pandemic. Methods: This study utilized a cross-sectional correlational design and was conducted in two university hospitals and four government hospitals in Saudi Arabia. The study sample was selected using purposive sampling. The Casey–Fink Graduate Nurse Experience Survey, the Arabic version of the ProQOL version 5, and the Conditions for Workplace Effectiveness Questionnaire Second Arabic version were used in the study. Data were analyzed using the Statistical Package for Social Sciences (SPSS) Version 28.0.1.1. The model was examined using Hayes’ process macro. Results: Structural empowerment significantly predicts successful transitions, both directly and indirectly through its impact on ProQOL. Conclusions: Nurse managers should employ optimal strategies and innovative structures within orientation programs to effectively facilitate the transition of Saudi graduate nurses. Moreover, nursing leaders and policymakers should leverage the increased attention garnered during the COVID-19 pandemic to enhance structural empowerment among newly hired nurses, thereby improving their transition and overall well-being. Structural empowerment was a direct and indirect predictor of successful transitions. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

Rolling bearings are one of the core components of industrial equipment. Owing to the rapid development of deep learning methods, a multitude of data-driven remaining useful life (RUL) estimation approaches have been proposed recently. However, several challenges persist in existing methods: the limited accuracy of traditional data-driven models, instability in sequence prediction, and poor adaptability to diverse operational environments. To address these issues, we propose a novel prognostics approach integrating three key components: time-intrinsic mode functions-derived feature representation (TIR) sequences, a one-dimensional temporal feature convolution–reallocation network (TFCR) with a flexible configuration scheme, and a physics-based Kalman filtering method. The approach first converts denoised signals into TIR-sequences using windowed empirical mode decomposition (EMD). The TFCR network then extracts hidden high-dimensional features from these sequences and maps them to the initial RUL. Finally, physics-based Kalman filtering is applied to enhance prediction stability and enforce physical constraints, producing refined RUL estimates. The experimental results based on the XJTU-SY dataset show the superiority of the proposed approach and further prove the feasibility of this method in bearing RUL estimation. Full article

Background/Objectives: The purpose of this review was to determine the impact of osteoporosis on outcomes after surgery for cervical deformity. Cervical deformity involves abnormal curvature or misalignment of the cervical spine, often resulting in a significant loss of quality of life and requiring surgical correction. While osteoporosis has been associated with hardware failure including screw loosening and cage migration in spine surgery, its role in cervical deformity remains unclear. Existing studies report mixed findings with regard to postoperative sequelae in patients with osteoporosis undergoing surgical correction of cervical deformity. Methods: A systematic review using PRISMA guidelines and MeSH terms involving spine surgery for cervical deformity and osteoporosis was performed. The Medline (PubMed) database was searched from 1990 to August 2022 using the following terms: “osteoporosis” AND “cervical” AND (“outcomes” OR “revision” OR “reoperation” OR “complication”). This review focused on radiographic outcomes, as well as post-operative complications. Results: Eight studies were included in the final analysis. Three papers assessed risk factors for the development of post-operative distal junctional kyphosis (DJK), but only one found osteoporosis as a predictor for DJK. Although three studies found that osteoporosis was not significantly associated with the incidence of surgical complications, one highlights osteoporosis as a predictor of complications at 90 days postoperatively (p < 0.001) and another associates osteoporosis with overall poor outcomes (p = 0.021). Furthermore, one study assessing the relationship between osteoporosis and reoperation found no association. Conclusions: Overall, our systematic review suggests that in patients undergoing surgery for cervical deformity, osteoporosis is not predictive of the need for reoperation or the development of postoperative complications, such as DJK, dysphagia, superficial infection, and others. These findings highlight the need for further study regarding the role of osteoporosis in surgical correction of cervical deformity. Full article

Childhood obesity has emerged as one of the most pressing public health challenges of the 21st century. Early-onset obesity is associated with an increased risk of developing numerous comorbidities later in life. Despite extensive research into its multifactorial etiology—including genetic, behavioral, environmental, and socioeconomic factors—the precise molecular mechanisms underlying the development and persistence of obesity in the pediatric population remain incompletely understood. Proteomics offers promising insights into these mechanisms. The application of proteomics in pediatric obesity research has grown, enabling the identification of proteins that reflect dynamic changes in metabolic and inflammatory pathways. This advancement allows clinicians to move beyond traditional anthropometric measurements toward personalized approaches with notification of early complications of obesity. A systematic search was conducted across PubMed, Scopus, and Web of Science for studies published between 2010 and 2025. Inclusion criteria: human studies, participants aged 0–18, proteomic analysis of obesity, and biomarkers. Data extraction and quality assessment followed standardized protocols. From 239 articles, 20 were included. Key dysregulated proteins include APOA1, CLU, and HP. LC-MS/MS was the predominant technique used. Some biomarkers were predictive for obesity complications in children. Proteomics holds clinical potential for early detection and personalized treatment of pediatric obesity. Standardized methodologies and longitudinal studies are needed for translation into clinical practice. Full article

Background: Despite the well-documented physical and psychological benefits of regular physical activity (PA) and exercise, participation remains insufficient in adults with cystic fibrosis (pwCF). In the general population, PA enjoyment is a key determinant of sustained engagement, yet its predictors in CF populations remain underexplored. Objective: We aimed to examine associations between clinical parameters, health-related quality of life (HRQoL), motivation and PA enjoyment in adult pwCF. We hypothesised that higher intrinsic motivation and better HRQoL would predict greater enjoyment, independent of clinical parameters. Methods: In this cross-sectional study, 197 adult pwCF (mean age = 36.6 ± 11.9 years) from a single centre completed validated questionnaires assessing PA and exercise enjoyment (Physical Activity Enjoyment Scale, PACES), motivation (Behavioral Regulation in Exercise Questionnaire-2, BREQ-2), and HRQoL (Cystic Fibrosis Questionnaire-Revised, CFQ-R). Hierarchical regression was conducted in three steps: clinical variables (Model 1), added HRQoL domains (Model 2), and motivational variables (Model 3). Results: The complete model explained 68.4% of the variance in PA and exercise enjoyment (R² = 0.684, p < 0.001). Intrinsic motivation was the strongest positive predictor (β = 6.228, p < 0.001), while external regulation negatively predicted enjoyment (β = −1.932, p = 0.030). Among HRQoL domains, only health perception remained significant (β = 0.081, p = 0.038). Clinical variables alone accounted for minimal variance (R² = 0.023, p = 0.370). Conclusions: Intrinsic motivation was the most robust predictor of PA and exercise enjoyment, outweighing clinical and most HRQoL factors. These findings support autonomy-supportive strategies to foster internal motivation and enhance long-term PA and exercise participation in adult pwCF. Full article

Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL prediction, they often face two major challenges: an insufficient generalization ability when distribution gaps exist between training data and real-world application scenarios, and the difficulty of comprehensively capturing complex equipment degradation processes with single-modal data. A key challenge in current research is how to effectively fuse multimodal data and leverage transfer learning to address RUL prediction in small-sample and cross-condition scenarios. This paper proposes an innovative deep multimodal fine-tuning regression (DMFR) framework to address these issues. First, the DMFR framework utilizes a Convolutional Neural Network (CNN) and a Transformer Network to extract distinct modal features, thereby achieving a more comprehensive understanding of data degradation patterns. Second, a fusion layer is employed to seamlessly integrate these multimodal features, extracting fused information to identify latent features, which are subsequently utilized in the predictor. Third, a two-stage training algorithm combining supervised pre-training and fine-tuning is proposed to accomplish transfer alignment from the source domain to the target domain. This paper utilized the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbine engine dataset publicly released by NASA to conduct comparative transfer experiments on various RUL prediction methods. The experimental results demonstrate significant performance improvements across all tasks. Full article

As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primarily fall into two main categories: physics-based and data-driven approaches. Physics-based methods mainly rely on extensive prior knowledge, limiting their scalability, while data-driven methods (including statistical analysis and machine learning) struggle with handling high-dimensional data and suboptimal modeling of multi-scale temporal dependencies. To address these challenges and enhance prediction accuracy and robustness, we propose a novel hybrid deep learning framework (CLSTM-TCN) integrating 2D Convolutional Neural Network (2D-CNN), Long Short-Term Memory (LSTM) network, and Temporal Convolutional Network (TCN) modules. The CLSTM-TCN framework follows a progressive feature refinement logic: 2D-CNN first extracts short-term local features and inter-feature interactions from input data; the LSTM network then models long-term temporal dependencies in time series to strengthen global temporal dynamics representation; and TCN ultimately captures multi-scale temporal features via dilated convolutions, overcoming the limitations of the LSTM network in long-range dependency modeling while enabling parallel computing. Validated on the NASA C-MAPSS data set (focusing on FD001), the CLSTM-TCN model achieves a root mean square error (RMSE) of 13.35 and a score function (score) of 219. Compared to the CNN-LSTM, CNN-TCN, and LSTM-TCN models, it reduces the RMSE by 27.94%, 30.79%, and 30.88%, respectively, and significantly outperforms the traditional single-model methods (e.g., standalone CNN or LSTM network). Notably, the model maintains stability across diverse operational conditions, with RMSE fluctuations capped within 15% for all test cases. Ablation studies confirm the synergistic effect of each module: removing 2D-CNN, LSTM, or TCN leads to an increase in the RMSE and score. This framework effectively handles high-dimensional data and multi-scale temporal dependencies, providing an accurate and robust solution for aeroengine RUL prediction. While current performance is validated under single operating conditions, ongoing efforts to optimize hyperparameter tuning, enhance adaptability to complex operating scenarios, and integrate uncertainty analysis will further strengthen its practical value in aircraft health management. Full article

Remaining useful life (RUL) prediction is critical for ensuring the reliability and safety of industrial equipment. In recent years, Transformer-based models have been widely employed in RUL prediction tasks for rolling bearings, owing to their superior capability in capturing global features. However, Transformers exhibit limitations in extracting local temporal features, making it challenging to fully model the degradation process. To address this issue, this paper proposes a parallel hybrid prediction approach based on Transformer and Long Short-Term Memory (LSTM) networks. The proposed method begins by applying Empirical Mode Decomposition (EMD) to the raw vibration signals of rolling bearings, decomposing them into a series of Intrinsic Mode Functions (IMFs), from which statistical features are extracted. These features are then normalized and used to construct the input dataset for the model. In the model architecture, the LSTM network is employed to capture local temporal dependencies, while the Transformer module is utilized to model long-range relationships for RUL prediction. The performance of the proposed method is evaluated using mean absolute error (MAE) and root mean square error (RMSE). Experimental validation is conducted on the PHM2012 dataset, along with generalization experiments on the XJTU-SY dataset. The results demonstrate that the proposed Transformer–LSTM approach achieves high prediction accuracy and strong generalization performance, outperforming conventional methods such as LSTM and GRU. Full article

This paper proposes Feature fusion and Dilated causal convolution model for Bearing Remaining useful life Prediction (FDBRP), an integrated framework for accurate Remaining Useful Life (RUL) prediction of rolling bearings that combines three key innovations: (1) a data augmentation module employing sliding-window processing and two-dimensional feature concatenation with label normalization to enhance signal representation and improve model generalizability, (2) a feature fusion module incorporating an enhanced graph convolutional network for spatial modeling, an improved multi-scale temporal convolution for dynamic pattern extraction, and an efficient multi-scale attention mechanism to optimize spatiotemporal feature consistency, and (3) an optimized dilated convolution module utilizing interval sampling to expand the receptive field, and combines the residual connection structure to realize the regularization of the neural network and enhance the ability of the model to capture long-range dependencies. Experimental validation showcases the effectiveness of proposed approach, achieving a high average score of 0.756564 and demonstrating a lower average error of 10.903656 in RUL prediction for test bearings compared to state-of-the-art benchmarks. This highlights the superior RUL prediction capability of the proposed methodology. Full article

Although wind-assisted ship propulsion (WASP) is an effective technique for reducing the emissions of merchant ships, the best fuel type for complementing WASP remains an open question. This study presents a new original life cycle assessment method for ship fuels that uses a validated ship performance prediction model and actual operation conditions for a WASP ship. As a case study, the method is used to evaluate the fuel consumption and environmental impact of different fuels for a WASP ship operating in the Baltic Sea. Using a novel in-house-developed platform for predicting ship performance under actual operation conditions using hindcast data, the engine and fuel tank were sized while accounting for fluctuating weather conditions over a year. The results showed significant variation in the required fuel tank capacity across fuel types, with liquid hydrogen requiring the largest volume, followed by LNG and ammonia. Additionally, a well-to-wake life cycle assessment revealed that dual-fuel engines using green ammonia and hydrogen exhibit the lowest global warming potential (GWP), while grey ammonia and blue hydrogen have substantially higher GWP levels. Notably, NOx, SOx, and particulate matter emissions were consistently lower for dual-fuel and liquid natural gas scenarios than for single-fuel marine diesel oil engines. These results underscore the importance of selecting both an appropriate fuel type and production method to optimize environmental performance. This study advocates for transitioning to greener fuel options derived from sustainable pathways for WASP ships to mitigate the environmental impact of maritime operations and support global climate change efforts. Full article

Background/Objectives: Malnutrition is a common but underrecognized complication in colorectal cancer (CRC), contributing to poor treatment outcomes and reduced quality of life. Regional data from the Gulf remains limited. This study assessed the prevalence of malnutrition and nutrition impact symptoms (NISs) among CRC patients in Kuwait. It evaluated the diagnostic performance of the PG-SGA Short Form (PG-SGA SF) in comparison to the full PG-SGA and the Malnutrition Screening Tool (MST). Methods: A cross-sectional study was conducted among 65 CRC outpatients at the Kuwait Cancer Control Center. Nutritional status was assessed using the full PG-SGA, PG-SGA SF, and MST. Dietary intake, anthropometry, biochemical parameters, and NISs were collected. Logistic regression identified independent predictors of malnutrition, and the performance of the tool was evaluated using kappa statistics and diagnostic accuracy metrics. Results: Malnutrition (PG-SGA B/C) was identified in 61.4% of patients. Loss of appetite, dry mouth, and nausea were significantly associated with malnutrition (p < 0.00385); dry mouth independently predicted malnutrition (OR: 17.65, 95% CI: 2.02–154.19, p = 0.009). BMI was not predictive, but reduced mid-arm circumference was significantly associated. PG-SGA SF showed strong agreement with the full PG-SGA (κ = 0.75), with high sensitivity (87.2%) and specificity (88.5%), outperforming MST (κ = 0.38). Only 23.5% of moderately malnourished patients were referred to a dietitian. Conclusions: Malnutrition and NIS are highly prevalent among Kuwaiti CRC patients. PG-SGA SF is a valid and efficient screening tool that should replace MST in oncology settings. Symptom-informed screening and structured referral protocols are crucial for enhancing nutrition care. Full article

While the machining of Inconel 718 has been widely studied, its cast counterpart Inconel 713LC remains underexplored, despite its relevance in high-temperature aerospace and energy components. This work presents a comprehensive investigation of dry milling behavior in Inconel 713LC, focusing on the interplay between tool wear, cutting forces, surface integrity, and chip formation across a broad range of cutting parameters. A stable process window was identified: 30–50 m/min cutting speed and 0.045–0.07 mm/tooth feed, where surface roughness remained below Ra 0.6 µm and tool life exceeded 10 min. Outside this window, rapid thermal and mechanical degradation occurred, leading to flank wear beyond the 550 µm limit and unstable chip morphology. The observed trends align with those in Inconel 718, allowing the cautious transfer of established strategies to cast alloys. By quantifying key process–performance relationships and validating predictive models for tool life and cutting forces, this study provides a foundation for optimizing the dry machining of cast superalloys. The results advance sustainable manufacturing practices by reducing reliance on cutting fluids while maintaining surface and dimensional integrity in demanding applications. Full article

Artificial intelligence (AI) plays a pivotal role in Industry 4.0, with predictive maintenance (PdM) emerging as a core application for improving operational efficiency by reducing unplanned downtime and extending asset life. Despite these advancements, the black-box nature of AI models remains a significant barrier to adoption, as industry stakeholders require systems that are both transparent and trustworthy. This study presents a systematic literature review examining how human-in-the-loop explainable AI (HITL-XAI) approaches can enhance the effectiveness and adoption of AI systems in PdM contexts. This review followed the PRISMA methodology, employing predefined search strings across Scopus, ProQuest, and EBSCO databases. Sixty-three peer-reviewed journal articles, published between 2019 and early 2025, were included in the final analysis. The selected studies span various domains, including industrial manufacturing, energy, and transportation, with findings synthesized through both descriptive and thematic analyses. A key gap identified is the limited empirical exploration of generative AI (GenAI) in improving the usability, interpretability, and trustworthiness of HITL-XAI systems in PdM applications. This review outlines actionable insights for integrating explainability and GenAI into existing rule-based PdM systems to support more adaptive and reliable maintenance strategies. Ultimately, the findings underscore the importance of designing HITL-XAI systems that not only demonstrate high model performance but are also effectively aligned with operational workflows and the cognitive needs of maintenance personnel. Full article