Search Results (13,568)

Small-cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumour that can metastasise early, may show resistance to systemic treatment, and has a poor prognosis. The use of tobacco products is closely related to the duration of their use, and approximately 95% of those diagnosed have a history of smoking. No satisfactory progress has been made in the prognosis with current treatment methods up to the present day. The treatment approach has traditionally involved long-term chemotherapy (CT) and radiotherapy (RT), and recent literature has focused on immunotherapy and genetic advancements. Surgery can only be performed in cases detected at an early stage. Although both chemotherapy and radiotherapy are indispensable options for most patients, their impact on prognosis and survival is limited. Although promising developments are expected in immunotherapy, its impact on survival is still very limited, lasting only about 2 months. In patients undergoing surgical resection as part of their treatment, overall survival (OS) ranges from 34 to 69 months. OS for 1 year is 84.8–93.8%, for 3 years is 60–71.2%, and for 5 years is 51.1–63.8%. The five-year survival rates are reported as follows: stage I 31–63.8%, stage II 25–65.5%, stage III 15–27.8%, and stage IV 0%. In this study, the prognosis and factors affecting prognosis in SCLC were investigated in light of current literature from a surgical perspective, and predictions were attempted to be made to lay the groundwork for personalised treatment approaches. Compared to non-small-cell lung cancer, the number of studies is quite limited. Most of the surgical case series were conducted in the past, retrospectively, and involved a small number of patients. Advances in immunotherapy are promising. In the early stages, resection and subsequent chemotherapy may be the main treatment. Full article

In recent decades, China has witnessed remarkable growth in housing construction, yet housing-related complaints have not declined significantly, highlighting the gap between housing quality and public expectations. Against this background, this study analyzes 32,277 national surveys to unpack residential satisfaction with green-livable communities in China. Entropy and standard-deviation weighting identified 16 priority indicators; artificial neural networks revealed weak direct influence of basic demographics on satisfaction, highlighting non-linear demand patterns. While 65–75% of respondents are satisfied with most attributes, significant city-level gaps persist—Beijing peaks near 90%, Chongqing falls below 50%. Dissatisfaction converges on three domains: infrastructure (parking, barrier-free access), building performance (leakage, noise, thermal defects) and smart systems (security, energy, health monitoring). Residents’ improvement priorities have shifted from basic shelter to health safety, smart technology, humanistic care and ecological amenities. A “basic-security + quality-upgrade” strategy is proposed: short-term repairs of common defects, medium-term smart-sustainable upgrades and long-term participatory governance. The findings not only enrich the theoretical framework of community satisfaction research but also provide practical guidance for enhancing community quality and meeting residents’ expectations in the context of China’s rapid urbanization and housing development. Full article

The use of highly active combined antiretroviral therapy (cART) has increased life expectancy in people living with HIV (PLWH). As a result of ongoing monitoring and surveillance in established HIV out-patient clinics, thyroid dysfunction amongst this population has become increasingly reported. In this narrative review, primary studies, case reports, and meta-analyses published on PubMed, Embase, and Cochrane were analysed. The most reported thyroid dysfunction is subclinical hypothyroidism (SCH). The prevalence of subclinical hypothyroidism was as high as 40% in PLWH with CD4 T-cell count < 350 cells/mm³, which is a level indicating a state of immunosuppression. Some less commonly reported thyroid dysfunctional conditions include overt hyperthyroidism and thyroid malignancy. Reports have linked the development of thyroid dysfunction to the use of cART, leading to immune reconstitution inflammatory syndrome (IRIS), which has also been linked to the development of Grave’s disease (GD). It is also important to check for thyroid malignancy, as PLWH are prone to having a high risk of developing non-AIDS-related or -defining cancer (NADC). Most research suggests symptom-driven monitoring. However, evidence also suggests that monitoring with cART status change, monitoring for patients with significant comorbidities, or with immune reconstitution may be useful. The screening should include Free Thyroxine (FT4), triiodothyronine (FT3), and thyroid-stimulating hormone (TSH) testing. Furthermore, vigilance for Grave’s disease and performing thyroid antibody checks are advised, especially once the reconstitution of T-cells is achieved. Full article

Background: The majority of injuries among older adults occur due to unexpected and sudden incidents in the home environment. This study aimed to develop a protocol for the design of the health belief model-based program for preventing unintentional home injuries in older adults and to evaluate the effectiveness of the program. Methods: The study proposed in this protocol, Safety and Home Injury Prevention for Seniors (SHIPs), is a single-blind, parallel-group, randomized controlled trial. A total of 54 Korean older adults (≥65 years) will be randomly assigned to either (1) the intervention group (n = 27), which will receive the SHIPs program, or (2) the control group (n = 27), which will attend four lecture-only sessions. The efficacy of the program will be assessed via tests performed at baseline, 1 week after program completion, and 1 month after program completion, and analyses of the changes in injury occurrences, risk factors, preventive behaviors, beliefs about safety and injury prevention, psychological health, physiological function, and health-related quality of life. Expected Results: The SHIPs intervention is expected to reduce home injuries and enhance awareness and preventive behaviors among community-dwelling older adults. It may also improve their physical and psychological health and overall quality of life. Conclusions: The SHIPs intervention may serve as an effective community-based strategy to promote injury prevention and improve the overall well-being of older adults. Full article

To accurately evaluate the health condition of the cables of a cross-sea cable-stayed bridge under typhoon effects and to improve the efficiency of damage identification, an accurate bridge damage identification method combining convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. A numerical model of the cable-stayed bridge was first established in ANSYS. Based on the monitoring data of Super Typhoon Mujigae, a three-dimensional fluctuating wind field was generated by harmonic synthesis. Through transient analysis, the static and dynamic responses of the cable-stayed bridge under typhoon loads were analyzed, and the critical cable locations most susceptible to damage were identified. Subsequently, the acceleration signals of the structural damage states under typhoon were extracted, and the damage-sensitive features were obtained through the Hilbert transform. Finally, an intelligent damage identification model for cable-stayed bridges was established by combining CNN and BiLSTM, and the identification results were compared with those obtained using CNN and BiLSTM individually. The results indicate that the neural network model combining CNN and BiLSTM performs significantly better than either CNN or BiLSTM alone in predicting both the location and degree of damage. Compared with the standalone CNN and BiLSTM models, the proposed hybrid CNN–BiLSTM network improves the accuracy of damage-location identification by 1.6% and 2.42%, respectively, and achieves an overall damage-degree identification accuracy exceeding 98%. The findings of this study provide theoretical and practical support for the intelligent operation and maintenance of cable-stayed bridges in coastal regions. The proposed approach is expected to serve as a valuable reference for evaluating large-span bridge structures under extreme wind conditions. Full article

A tracking method based on adaptive morphological correlation and neural predictive models is presented. The morphological correlation filters are optimized according to the aggregated binary dissimilarity-to-matching ratio criterion and are adapted online to appearance variations of the target across frames. Morphological correlation filtering enables reliable detection and accurate localization of the target in the scene. Furthermore, trained neural models predict the target’s expected location in subsequent frames and estimate its bounding box from the correlation response. Effective stages for drift correction and tracker reinitialization are also proposed. Performance evaluation results for the proposed tracking method on four image datasets are presented and discussed using objective measures of detection rate (DR), location accuracy in terms of normalized location error (NLE), and region-of-support estimation in terms of intersection over union (IoU). The results indicate a maximum average performance of 90.1% in DR, 0.754 in IoU, and 0.004 in NLE on a single dataset, and 83.9%, 0.694, and 0.015, respectively, across all four datasets. In addition, the results obtained with the proposed tracking method are compared with those of five widely used correlation filter-based trackers. The results show that the suggested morphological-correlation filtering, combined with trained neural models, generalizes well across diverse tracking conditions. Full article

Background: Conflicts are an inevitable part of interpersonal relationships, and personality traits influence how they are resolved. In the nursing work environment, conflicts often arise from poor communication and stress, negatively impacting nurses’ well-being and quality of care. The “Big Five” personality model highlights how traits such as extraversion, agreeableness, and emotional stability shape conflict approaches. Understanding these traits aids in developing effective conflict management strategies. This study investigates intragroup conflicts among nurses by identifying their types and examining how sociodemographic factors and personality traits predict their occurrence. The aim is to provide insights that support targeted interventions and improve team dynamics in nursing practice. Methods: The study was conducted as a cross-sectional analysis within the University Hospital Centre Osijek from March to August 2024, involving nurses and technicians. Data was collected using structured questionnaires with clearly defined inclusion and exclusion criteria. The questionnaire included the Process Conflict Scale, the Big Five Inventory, and a Demographic questionnaire. Appropriate statistical analyses were conducted, including descriptive statistics, normality testing with the Kolmogorov–Smirnov test, non-parametric Spearman and Point-Biserial correlations, and linear regression to examine predictors of intragroup conflicts. All assumptions for regression were met, with significance set at p < 0.05, and analyses were performed using JASP software version 0.17.2.1. Results: The research reveals significant differences among various types of team conflicts, where personality traits such as neuroticism increase, while conscientiousness decreases conflicts. The professional competence of respondents also positively correlates with logistical conflicts, and personality explains the variance in conflicts among nurses. Conclusions: Intragroup conflicts among nurses, particularly task-related, stem from communication issues and high care standards. Neuroticism negatively affects team dynamics, while conscientiousness can reduce conflicts but may also lead to disagreements if expectations are unmet. Education on conflict management and clearly defined roles can improve teamwork and quality of care. Full article

This paper reports a study conducting an in-depth analysis of the impacts of ethical training on the adoption of AI tools among computer science students in higher vocational colleges. These students will serve as the pivotal human factor for advancing the field of AI. Aiming to explore practical models for integrating AI ethics into computer science education, the research seeks to promote more responsible and effective AI application and therefore become a positive influence in the field. Employing a mixed-methods approach, the study included 105 students aged 20–24 from a vocational college in Guangdong Province, a developed region in China. Based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, a five-point Likert scale was used to evaluate the participants’ perceptions of AI tool usage based on ethical principles. The Structural Equation Modeling (SEM) results indicate that while participants are motivated to adopt AI technologies in certain aspects, performance expectancy negatively impacts their intention and actual usage. After systematically studying and understanding AI ethics, participants attribute a high proportion of responsibility (84.89%) to objective factors and prioritized safety (27.11%) among eight ethical principles. Statistical analysis shows that habit (β = 0.478, p < 0.001) and hedonic motivation (β = 0.239, p = 0.004) significantly influence behavioral intention. Additionally, social influence (β = 0.234, p = 0.008) affects use behavior. Findings regarding factors that influence AI usage can inform a strategic framework for the integration of ethical instruction in AI applications. These findings have significant implications for curriculum design, policy formulation, and the establishment of ethical guidelines for AI deployment in higher educational contexts. Full article

The invasive plant Heracleum sosnowskyi Manden. is a valuable source of a number of bioactive metabolites that can be used in the pharmaceutical industry and medicine and may have some other applications as well. Today, there is a need to summarize data on these substances as well as analyze the toxicological profile of the metabolites of H. sosnowskyi. In this study, we collected a dataset of 225 metabolites of H. sosnowskyi from different literature sources and performed cluster analysis of their chemical structures; we revealed five main clusters of compounds: terpenoids, aromatic compounds, polyaromatic compounds, fatty acids, and furanocoumarins. In order to fill the gaps in the experimental data on the toxicity of the studied substances, we used machine learning (ML) algorithms previously designed for high-accuracy prediction of toxicity end-points. The ML-based approach allowed us to fill in up to 90% of the missing median lethal dose $L{D}_{50}$ (mouse) data for the studied molecules. The validity of each predicted value was confirmed by analyzing the applicability domain of the used ML models. For the calculations and ML modeling, we used the Syntelly chemoinformatics platform. For the most toxic compounds—hydroxycoumarins and furanocoumarins of H. sosnowskyi—the values for hepatotoxicity, drug-induced liver injury (DILI), cardiotoxicity, and carcinogenicity were predicted. Based on the analysis of $L{D}_{50}$ values for the mouse animal model, the greatest toxicity for furanocoumarins is expected with the intravenous route of administration (62–450 mg/kg), which can cause drug-induced liver injury. At the same time, the data do not show high cardiotoxicity risks for the studied furanocoumarins. Based on the presented results, we discuss prospects of using some of the compounds as pharmaceutical agents. Full article

Objectives: An analysis based on epidemiological material to show whether the term Metabolic Syndrome (MS) should be adopted when aiming at predicting coronary heart disease (CHD) and major cardiovascular disease (CVD) fatal events. Material and Methods: MS was defined according to the International Diabetes Federation (IDF) and risk factors were identified in the Italian Risk Factors and Life Expectancy (RIFLE) population study covering over 25,000 adult men from a pool of 19 Italian population samples. The original MS definition and the plain original units of measured risk factors were challenged in Cox proportional hazard models predicting 196 CHD and 412 major CVD fatal events in a seven-year follow-up. Parallel models were run including also total serum cholesterol as a covariate, an unfortunately excluded covariate in the MS definition. The performance of the various models was tested by the log-likelihood statistics treated with the Akaike Information Criterium (AIC). Results: Models using the plain measurements of the risk factors involved were systematically and significantly outperforming any other categorized score based on the IDF-MS classification. An intermediate role was played by a model where the predictive variable was a factor score (derived from a Factor Analysis) where the MS risk factors were linearly combined. The same models also including serum cholesterol provided a significantly better prediction when compared with those without serum cholesterol, based on AIC. Conclusions: The use of a subset of classical CVD risk factors classified according to the IDF-MS criteria adds nothing better than the exclusive use of the risk factors treated by traditional procedures. The addition of serum cholesterol definitely helps in the prediction of the CHD component of major CVD events. Its omission is erroneous. Full article

This study investigates the dynamic relationship between key macroeconomic indicators, specifically inflation (CPI), the Federal Funds Rate, GDP growth, unemployment, and money supply, and U.S. stock market returns, represented by the S&P 500 index, over the period January 2015 to June 2025. The objective is to understand how inflation and monetary policy affect market performance in both the short and long run. Using an Autoregressive Distributed Lag (ARDL) modeling framework and Error Correction Model (ECM), the study examines monthly S&P 500 returns alongside macroeconomic variables, accounting for lagged effects and potential cointegration. The model captures both immediate and delayed impacts, employing the Bounds Testing approach to confirm long-run equilibrium relationships. Results show significant mean-reversion in stock returns, a delayed negative impact of inflation and interest rate increases, and a positive contemporaneous response to GDP growth. Unemployment exhibits a counterintuitive positive effect on returns, suggesting forward-looking investor expectations. The money supply also positively influences equity prices, supporting liquidity-based asset pricing theories. This paper provides updated empirical evidence on macro-finance linkages and highlights the complex interplay of monetary policy, inflation, and market expectations in shaping U.S. equity returns. Full article

Background: Mental health is increasingly prioritized in Saudi Arabia, with growing interest in digital solutions. Objectives: The study’s objective was to assess awareness, acceptance, and use of mobile mental health applications among Saudis. Methods: A cross-sectional online survey, based on the UTAUT model, explored performance expectancy, effort expectancy, social influence, and privacy concerns among 1613 participants. Results: While 68.9% were aware of at least one mental health app, only 20% actively used them. Awareness was influenced by gender, age, employment, marital status, and region, whereas utilization depended on gender, age, education, region, and acceptance. Performance expectancy strongly predicted usage. Conclusions: Despite high awareness, usage of mobile mental health applications remains low in Saudi Arabia. Demographic factors affect awareness, and acceptance drives utilization. App developers should consider these factors to enhance engagement and effectiveness. Full article

Multimodal contrastive learning has achieved significant performance advantages in self-supervised skeleton-based action recognition. Previous methods are limited by modality imbalance, which reduces alignment accuracy and makes it difficult to combine important spatial–temporal frequency patterns, leading to confusion between modalities and weaker feature representations. To overcome these problems, we explore intra-modality feature-wise self-similarity and inter-modality instance-wise cross-consistency, and discover two inherent correlations that benefit recognition: (i) Global Perspective expresses how action semantics carry a broad and high-level understanding, which supports the use of globally discriminative feature representations. (ii) Focus Adaptation refers to the role of the frequency spectrum in guiding attention toward key joints by emphasizing compact and salient signal patterns. Building upon these insights, we propose a novel language–skeleton contrastive learning framework comprising two key components: (a) Feature Modulation, which constructs a skeleton–language action conceptual domain to minimize the expected information gain between vision and language modalities. (b) Frequency Feature Learning, which introduces a Frequency-domain Spatial–Temporal block (FreST) that focuses on sparse key human joints in the frequency domain with compact signal energy. Extensive experiments demonstrate the effectiveness of our method achieves remarkable action recognition performance on widely used benchmark datasets, including NTU RGB+D 60 and NTU RGB+D 120. Especially on the challenging PKU-MMD dataset, MICA has achieved at least a 4.6% improvement over classical methods such as CrosSCLR and AimCLR, effectively demonstrating its ability to capture internal and contextual attention information. Full article

This article presents a diagnostic method for transmit–receive modules (TRMs) in an airborne active phased array antenna (APAA). Given the spatial constraints of airborne radar systems, the diagnostic functionality was implemented using the peripheral probe method. To minimize the space, cost, and time required for modifications to the existing APAA, the side-lobe blanking (SLB) channel was employed as the probe. To prevent TRM saturation and to determine the fault detection threshold, an APAA-level test was performed using a movable anechoic chamber. The coupling level between the SLB antenna and TRM was maintained between −70 dB and −20 dB. With the result of the APAA-level test, a budget analysis on the signal path was performed, and the input attenuation level was determined. The received signal power was estimated at −40 dBm to −20 dBm. Based on the estimation, the detection threshold was determined as −50 dBm. For the operation of the diagnostic function, simple detection logic and associated control timing is implemented in the radar processor. The effectiveness of the proposed diagnostic method was validated by several test activities, including an anechoic chamber, a rooflab facility, and an actual fighter. The test result shows good agreement with the expectations. Full article

Reinforcement corrosion significantly reduces the load-bearing capacity, ductility, and energy dissipation of reinforced concrete (RC) structures, thereby increasing their seismic failure risk. To enhance the seismic performance of in-service RC structures, this study employs an FRP mesh–engineered cementitious composite (ECC) retrofitting method and develops a multi-objective optimization decision-making framework. A finite element model incorporating reinforcing steel corrosion, concrete deterioration, and bond–slip effects is first established and validated against experimental results. Based on this model, a six-story RC frame is selected as a case study, and eight alternative FRP mesh–ECC retrofitting schemes are designed. Five core indicators are quantified, namely annual collapse probability, expected annual loss, capital expenditure, carbon emissions, and downtime. The results indicate that FRP mesh–ECC retrofitting can significantly improve the seismic performance of corroded RC structures. The overall uniform retrofitting scheme (SCS-2) achieves the most significant improvements in seismic safety and economic performance, but they are associated with highest capital expenditure and carbon emission. Story-differentiated schemes (SCS-3 to SCS-6) provide a trade-off between performance enhancement and cost–emission control. While partial component-focused schemes (SCS-7 and SCS-8) cut cost and carbon but do not lower seismic downtime. Furthermore, the improved fuzzy-TOPSIS method with interval weights and Monte Carlo simulation indicates that the balanced scheme SCS-1 delivers the most robust performance across five dimensions, with a best probability close to 90%. The results confirm the potential of FRP mesh–ECC retrofitting at both component and structural levels and provide a practical reference for selecting seismic retrofitting strategies for existing RC structures. Full article