Search Results (8,674)

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

Detecting previously unseen anomalies in sensor data is a challenging problem for artificial intelligence when sensor-specific and deployment-specific characteristics of the time series need to be learned from a short calibration period. From the application point of view, this challenge becomes increasingly important because many applications are gravitating towards utilising low-cost sensors for Internet of Things deployments. While these sensors offer cost-effectiveness and customisation, their data quality does not match that of their high-end counterparts. To improve sensor data quality while addressing the challenges of anomaly detection in Internet of Things applications, we present an anomaly detection framework that learns a normality model of sensor data. The framework models the typical behaviour of individual sensors, which is crucial for the reliable detection of sensor data anomalies, especially when dealing with sensors observing significantly different signal characteristics. Our framework learns sensor-specific normality models from a small set of anomaly-free training data while employing an unsupervised feature engineering approach to select statistically significant features. The selected features are subsequently used to train a Local Outlier Factor anomaly detection model, which adaptively determines the boundary separating normal data from anomalies. The proposed anomaly detection framework is evaluated on three real-world public environmental monitoring datasets with heterogeneous sensor readings. The sensor-specific normality models are learned from extremely short calibration periods (as short as the first 3 days or 10% of the total recorded data) and outperform four other state-of-the-art anomaly detection approaches with respect to F1-score (between 5.4% and 9.3% better) and Matthews correlation coefficient (between 4.0% and 7.6% better). Full article

The impact of seasonality on the aroma quality of tea has been documented in various tea types, but not specifically in oolong tea. This study is the first to explore the complex relationships between seasonality, volatile compounds, and aroma quality in oolong tea. Using Headspace Solid-Phase Microextraction Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS)-based untargeted metabolomics, we analyzed 266 samples of Tieguanyin oolong tea. The data identified linalool, linalool oxides (trans-linalool oxide (furanoid) and trans-linalool oxide (pyranoid)), and their metabolites (diendiol I; hotrienol) as key seasonal discriminants. Four out of the top ten key differential compounds for distinguishing aroma scores were metabolites from fatty acid degradation, namely trans-3-hexenyl butyrate, trans-2-hexenyl hexanoate, hexyl hexanoate, and hexyl 2-methyl butyrate. Approximately one-fifth of the seasonal discriminant volatile compounds were significant in influencing aroma quality. Overall, the impact of seasonality on the aroma quality of finished Tieguanyin oolong tea is marginal. These findings enhance our understanding of the interplay between seasonal variations, volatile composition, and aroma quality in oolong tea. Full article

Background: The education process for newly diagnosed Type 1 diabetes mellitus (T1D) patients and their families, primarily led by diabetes specialist nurses, is essential for gaining knowledge about the disease and its management. However, few assessment tools have been employed to evaluate long-term knowledge retention among T1D patients years after diagnosis. Methods: We developed a 20-question test to assess the knowledge of patients and their families at the conclusion of the initial education process and again 6–12 months later. Demographic and clinical data were also collected. Statistical analyses included comparisons between the first and second test results, as well as evaluation of potential contributing factors. The internal consistency and construct validity of the questionnaire were evaluated. Results: Forty-four patients completed both assessments, with a median interval of 11.5 months between them. The average score on the first test was 88.6, which declined to 82.7 on the second assessment (p < 0.001). In univariate analysis, factors positively associated with higher scores included Jewish ethnicity, lower HbA1c levels, and shorter hospitalization duration. Multivariate analysis revealed that parents had lower odds of experiencing a significant score decline compared to patients. Cronbach’s alpha was 0.69, and Principal Component Analysis (PCA) identified eight components accounting for 67.1% of the total variance. Conclusions: Healthcare providers should consider offering re-education to patients and their families approximately one year after diagnosis, with particular attention to high-risk populations during the initial education phase. Further studies are needed to examine this tool’s performance in larger cohorts. Full article

Haemophilic arthropathy is caused by repeated joint bleeding episodes, primarily affecting knees, ankles and elbows. Conservative options should be considered prior to surgery, as well as postural evaluation, since any functional overload promotes the development of new bleeding. The aim of this study is to verify the use of foot orthoses in combination with postural rehabilitation, assessing the incidence of spontaneous haemarthroses and haematomas. In total, 15 patients were enrolled and randomly divided into two groups: 8 in group A, composed of patients who were prescribed foot orthoses and a 20-session rehabilitation program, and 7 in group B, composed of patients who were instructed to use foot orthoses only. All patients were evaluated at baseline (T0), at 3 months (T1—end of the rehabilitation program), and at 12 months (T2), using the following scales: Functional Independence Score in Haemophilia (FISH), Haemophilia Joint Health Score (HJHS) and Numerical Rating Scale (NRS). During the 12 months between the first and the last assessment, no patient in group A developed hemarthroses or hematomas, while one case of hemarthrosis was recorded in group B. The HJHS improved significantly (≤0.05) in group A at both T1 and T2, while in group B it improved significantly only in T2. As for FISH, it showed significant improvements in both groups at T1 and T2. NRS showed a significant reduction only at T2 in both groups (p-value T0–T1 0.3 in group A e 0.8 in group B). No patient reported any adverse effects from the use of orthotic insoles. The combination of postural rehabilitation, the use of foot orthoses and pharmacological prophylaxis could improve functioning and joint status in patients affected by haemophilic arthopathy, delaying or preventing new hemarthroses by improving the distribution of joint loads and the modification of musculoskeletal system’s characteristics. Full article

Background/Objectives: Neuromodulation is under investigation as a possibly effective add-on therapy in Alzheimer’s disease (AD). While transcranial pulse stimulation (TPS) has shown positive short-term effects, long-term effects have not yet been fully explored. This study aims to evaluate the long-term feasibility, safety, and potential cognitive benefits of TPS over one year in patients with Alzheimer’s disease, focusing on domains such as memory, speech, orientation, visuo-construction, and depressive symptoms. Methods: We analyzed preliminary data from the first ten out of thirty-five patients enrolled in a prospective TPS study who completed one year of follow-up and were included in a dedicated long-term database. The protocol consisted of six initial TPS sessions over two weeks, followed by monthly booster sessions delivering 6000 pulses each for twelve months. Patients underwent regular neuropsychological assessments using the Alzheimer Disease Assessment Scale (ADAS), Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Beck Depression Inventory (BDI-II). All adverse events (AEs) were documented and monitored throughout the study. Results: Adverse events occurred in less than 1% of stimulation sessions and mainly included mild focal pain or transient unpleasant sensations, as well as some systemic behavioral or vigilance changes, particularly in patients with underlying medical conditions, with some potentially related to the device’s stimulation as adverse device reactions (ADRs). Cognitive test results showed significant improvement after the initial stimulation cycle (ADAS total improved significantly after the first stimulation cycle (M_pre = 28.44, M_post = 18.56; p = 0.001, d = 0.80, 95% CI (0.36, 1.25)), with stable scores across all domains over one year. Improvements were most notable in memory, speech, and mood. Conclusions: TPS appears to be a generally safe and feasible add-on treatment for AD, although careful patient selection and monitoring are advised. While a considerable number of participants were lost to follow-up for various reasons, adverse events and lack of treatment effect were unlikely primary causes. A multifocal stimulation approach (F-TOP2) is proposed to enhance effects across more cognitive domains. Full article

(1) Background and objectives: Large language models (LLMs) such as GPT, Mistral, and LLaMA exhibit strong capabilities in text generation, yet assessing the quality of their reasoning—particularly in open-ended and argumentative contexts—remains a persistent challenge. This study introduces Dialectical Agent, an internally developed modular framework designed to evaluate reasoning through a structured three-stage process: opinion, counterargument, and synthesis. The framework enables transparent and comparative analysis of how different LLMs handle dialectical reasoning. (2) Methods: Each stage is executed by a single model, and final syntheses are scored via two independent LLM evaluators (LLaMA 3.1 and GPT-4o) based on a rubric with four dimensions: clarity, coherence, originality, and dialecticality. In parallel, a rule-based semantic analyzer detects rhetorical anomalies and ethical values. All outputs and metadata are stored in a Neo4j graph database for structured exploration. (3) Results: The system was applied to four open-weight models (Gemma 7B, Mistral 7B, Dolphin-Mistral, Zephyr 7B) across ten open-ended prompts on ethical, political, and technological topics. The results show consistent stylistic and semantic variation across models, with moderate inter-rater agreement. Semantic diagnostics revealed differences in value expression and rhetorical flaws not captured by rubric scores. (4) Originality: The framework is, to our knowledge, the first to integrate multi-stage reasoning, rubric-based and semantic evaluation, and graph-based storage into a single system. It enables replicable, interpretable, and multidimensional assessment of generative reasoning—supporting researchers, developers, and educators working with LLMs in high-stakes contexts. Full article

Background: Malnutrition and unintended weight loss are frequent in cancer patients and linked to poorer outcomes. However, data on long-term weight trajectories, particularly comparing different cancer entities, remain limited. Methods: In this retrospective, multicenter study, we analyzed 145 patients diagnosed with either head and neck cancer (HNC; n = 48) or high-grade B-cell lymphoma (HGBCL; n = 97). Body weight, C-reactive protein (CrP), albumin, and modified Glasgow Prognostic Score (mGPS) were assessed at diagnosis and at 3, 6, 9, and 12 months. Clinically relevant weight loss was defined as >5% from baseline. Survival analyses were performed for HGBCL patients. Results: Weight loss was common in both cohorts, affecting 32.2% at 3 months and persisting in 42.3% at 12 months. Nearly half of HNC patients had sustained >5% weight loss at one year, whereas HGBCL patients were more likely to regain weight, with significantly higher rates of weight gain at 6 and 12 months (p = 0.04 and p = 0.02). At baseline, HGBCL patients showed elevated CrP and lower albumin compared to HNC (both p < 0.001). Weight loss at 6 months was significantly associated with reduced overall survival in HGBCL (p < 0.01). Both Δweight at 6 months and mGPS emerged as useful prognostic indicators. Conclusions: This study reveals distinct patterns of weight change and systemic inflammation between HNC and HGBCL patients during the first year after diagnosis. These findings highlight the need for entity-specific nutritional monitoring and tailored supportive care strategies extending into survivorship. Prospective studies integrating body composition analyses are warranted to better guide long-term management. Full article

This study aimed to determine the factors associated with the occurrence of the first-wave dominant follicle (DF) in the ovary ipsilateral to the corpus luteum (CL) in lactating dairy cows and dairy heifers. A total of 505 estruses were investigated (lactating dairy cows, n = 361; dairy heifers, n = 144). The locations of the preovulatory follicle (PF) and regressed CL were examined at the estrus, and the locations of the first-wave dominant follicle (DF) and newly formed CL were examined seven days after estrus using transrectal ultrasonography. Then, cows were classified into two groups: the first-wave DF in the ovary ipsilateral to the CL (IG) and the first-wave DF in the ovary contralateral to the CL (CG). To evaluate the factors which affect the occurrence of IG and CG, binominal logistic regression analysis was conducted; the location of the PF and regressing CL, season (warm: June–September; cool: October–May), live weight, days in milk at estrus, daily milk production, and body condition score were used as independent variables. The occurrence rate of IG was significantly higher when the PF was located contralateral to the regressing CL (lactating dairy cows, 63.4%; dairy heifers, 58.6%) rather than ipsilateral (lactating dairy cows, 44.9%; dairy heifers, 35.1%). The IG occurrence rate was significantly higher with an increase in daily milk production (<30 kg, 47.3%; 30–40 kg, 55.2%; >40 kg, 60.5%) in lactating dairy cows. In conclusion, the occurrence of IG was associated with relative locations of the PF and regressing CL in lactating dairy cows and dairy heifers and with the level of milk production in lactating dairy cows. Full article

As anonymity-enabling technologies such as VPNs and proxies become increasingly exploited for malicious purposes, detecting traffic associated with such services emerges as a critical first step in anticipating potential cyber threats. This study analyses a network traffic dataset focused on anonymised IP addresses—not direct attacks—to evaluate and compare explainable, interpretable, and opaque machine learning models. Through advanced preprocessing and feature engineering, we examine the trade-off between model performance and transparency in the early detection of suspicious connections. We evaluate explainable ML-based models such as k-nearest neighbours, fuzzy algorithms, decision trees, and random forests, alongside interpretable models like naïve Bayes, support vector machines, and non-interpretable algorithms such as neural networks. Results show that neural networks achieve the highest performance, with a macro F1-score of 0.8786, but explainable models like HFER offer strong performance (macro F1-score = 0.6106) with greater interpretability. The choice of algorithm depends on project-specific needs: neural networks excel in accuracy, while explainable algorithms are preferred for resource efficiency and transparency, as stated in this work. This work underscores the importance of aligning cybersecurity strategies with operational requirements, providing insights into balancing performance with interpretability. Full article

The objective of the present study was to evaluate the association between liver cirrhosis (LC) and subsequent Heart failure (HF). This retrospective cohort study utilized data from the Disease Analyzer database (IQVIA) and included adults with a first-time diagnosis of LC in 1293 general practices in Germany between January 2005 and December 2023. A comparison cohort without liver diseases was matched to the cirrhosis group using 5:1 propensity score matching. Univariable Cox proportional hazards models were used to assess the association between alcoholic vs. non-alcoholic LC and HF. The final study cohort included 5530 patients with alcoholic LC and 27,650 matched patients without liver disease, as well as 7063 patients with non-alcoholic LC and 35,315 matched patients without liver disease. After up to 10 years of follow-up, HF was diagnosed in 20.9% of patients with alcoholic LC compared to 10.3% of matched cohort, and in 23.0% of patients with non-alcoholic LC, compared to 14.2% in matched cohort. Alcoholic LC (Hazard Ratio (HR): 2.07 (95% CI: 1.85–2.31) and non-alcoholic LC (HR: 1.70; 95% CI: 1.56–1.82) were associated with an increased risk of HF. The association was also stronger in men than in women. LC, both alcoholic and non-alcoholic, is significantly associated with an increased long-term risk of HF. The association is particularly pronounced in patients with alcoholic cirrhosis and in men. To the best of the authors’ knowledge, this is the first real-world evidence for the positive association between LC and subsequent HF from Europe. Full article

Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of $97\%$ based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article

Accounting for 15–30% of colorectal cancer cases, the serrated pathway remains poorly characterized compared to the adenoma–carcinoma sequence. It involves sessile serrated lesions as precursors and is characterized by BRAF mutations (BRAF^V600E), CpG island hypermethylation, and microsatellite instability (MSI). Using label-free proteomics, we compared normal tissue margins from patients with diverticular disease, sessile serrated lesions, low-grade adenomas, and high-grade adenomas. We identified S100A14 as significantly overexpressed in sessile serrated lesions compared to low-grade adenomas, high-grade adenomas, and normal tissues. This overexpression was confirmed by immunohistochemical scoring in an independent cohort. Gene expression analyses of public datasets showed higher S100A14 expression in BRAF^V600E-mutated and MSI-H colorectal cancers compared to microsatellite stable BRAF^wt tumors. This finding was confirmed by immunohistochemical scoring in an independent colorectal cancer cohort. Furthermore, single-cell RNA sequencing analysis from the Human Colon Cancer Atlas revealed that S100A14 expression in tumor cells positively correlated with the abundance of tumoral CD8⁺ cytotoxic T cells, particularly the CD8⁺ CXCL13⁺ subset, known for its association with a favorable response to immunotherapy. Collectively, our results demonstrate for the first time that S100A14 is a potential biomarker of serrated neoplasia and further suggests its potential role in predicting immunotherapy responses in colorectal cancer. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Background: Chronic stress in childhood and adolescence leads to excessive cortisol secretion, adipokines production and obesity with all the negative mental and physical effects on the health of individuals and adulthood. Objectives: The aim of the present non-randomized controlled trial was to investigate the effect of a stress management protocol with diaphragmatic breathing (DB) and physiotherapy exercise on stress, body composition, cardiorespiratory and metabolic markers of children and adolescents with morbid obesity. Methods: The study included 31 children and adolescents (5–18 years old) with morbid obesity (22 in the intervention arm and 9 controls). All participants completed anxiety questionnaires and a self-perception scale. Forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), blood pressure (BP) and SpO₂ were measured. Fasting glucose, uric acid, triglycerides, HbA1c, (AST/SGOT), (ALT/SGPT), HDL, LDL, insulin, ACTH, cortisol, HOMA-IR, 17-OH, S-DHEA, SHBG were assessed, and anthropometric measurements were also performed. Results: In the intervention group, 4 months after the treatment, an improvement was noted in the BMI, BMI z-score, waist-to-height ratio, FEV1, SpO₂, pulse and systolic BP. HDL increased, ALT/SGPT and insulin resistance improved. Positive changes were observed in temporary and permanent stress and self-esteem of children in the intervention group, including anxiety, self-perception, physical appearance, etc. Conclusions: A combined exercise and DB protocol has a positive effect on stress, by improving body composition, reducing insulin resistance, and ameliorating physical and mental health and quality of life of pediatric patients with morbid obesity. Full article