Search Results (94)

Double-skin façade (DSF) systems are important for energy efficiency because they effectively utilize natural ventilation and daylight. However, the uninterrupted vertical gaps in these systems may pose safety risks in the event of a fire by causing the rapid spread of smoke and hot gases. This study presents a hybrid approach that combines computational fluid dynamics (CFD)-based simulations and machine learning (ML) techniques to predict heat flow and fire-room control-volume heat release rate (FR-HRR). Within the scope of the study, 400 different scenarios were modeled with different combinations of basic natural ventilation design parameters consisting of gap width, gap height, window opening area, and air inlet and outlet area. The data obtained were evaluated with different ML models, including Fine Tree, Bagged Tree, Support Vector Machine, and Artificial Neural Network models; in particular, the Fine Tree model gave the most successful results with high accuracy rates (R² = 0.99 for FR-HRR; R² = 0.91 for heat flow). The analysis showed that DSF gap width provided a dominant model-based contribution within the investigated CFD-generated dataset. This approach provides a preliminary CFD-informed ML framework for the rapid comparative assessment of fire-related responses in open-boundary naturally ventilated DSF configurations during the early design stage. Full article

Background/Objectives: This study evaluated the effectiveness of Cotinus coggygria (Smoke Tree) Flower Water mouthwash in reducing halitosis and improving oral hygiene parameters among adolescents undergoing fixed orthodontic treatment. Methods: A double-blind, randomized, placebo-controlled, parallel-group clinical trial was conducted with 30 individuals undergoing treatment with fixed orthodontic appliances. Participants were allocated (1:1) into two groups: Group A received the Cotinus coggygria mouthwash, while Group B received the placebo mouthwash. Hydrogen sulfide (H₂S) concentration in breath, measured by the OralChroma^TM II device, was the primary outcome. Secondary outcomes included dimethyl sulfide [(CH₃)₂S] and methyl mercaptan (CH₃SH) levels, assessed with the same device, and oral hygiene status evaluated using the Modified Silness & Löe Plaque (PI-M) as well as the Silness & Löe Gingival (GI) indices. Normality of the data distribution was assessed using the Shapiro–Wilk test. Statistical analyses were conducted using the Mann–Whitney U test and Student’s t-test. Results: A statistically significant reduction in H₂S levels was observed in the C. coggygria group compared to placebo (p = 0.014). Median H₂S levels decreased from 147.00 ppb at baseline (T0) to 35.00 ppb at follow-up (T1) after 2 weeks. A statistically significant reduction in total VSC levels was also observed in the C. coggygria group compared to placebo (p < 0.001). Median total VSC levels decreased from 254.00 ppb at baseline (T0) to 105.00 ppb at follow-up (T1) after 2 weeks. No significant differences were found between groups for the other volatile sulfur compounds. A Significant improvements were noted in periodontal parameters in favor of the C. coggygria group. The Gingival Index decreased from 2.0 to 1.3 (p < 0.001; 95% CI: −0.7 to −0.2), and the Plaque Index (PI-M) decreased from 1.6 to 1.0 (p = 0.001; 95% CI: −0.7 to −0.3). Conclusions: Cotinus coggygria mouthwash appeared to be an effective adjunct for managing halitosis and improving oral hygiene parameters in adolescents undergoing fixed orthodontic treatment. No adverse effects were reported. Full article

Background: Past workplace exposure to asbestos in combination with tobacco smoking has increased the risk of lung cancer for some residents in an area within the Friuli Venezia Giulia region, Northeast Italy. In light of studies showing that lung cancer screening (LCS) with low-dose computed tomography (LDCT) can reduce mortality, local stakeholders and decision-makers decided to assess the potential benefits, harms and cost-effectiveness of a single round of LCS with LDCT versus standard care among people aged 55 to 80 who were formerly exposed to asbestos and with at least 10 pack-years of smoking. Methods: An economic model was developed using a decision tree connected to a Markov cohort model. The primary outcome was the incremental cost per additional quality-adjusted life year (QALY). Other outcomes included the number of life years saved, the number of deaths averted and overdiagnosis. Results: Per 10,000 people screened, the intervention led to 395 additional QALYs (95% credible interval: 129 to 831) and incremental total costs of EUR 1,086,345 (95% credible interval: −852,607 to 2,155,826). The incremental cost per QALY gained was EUR 2750. There was a probability of cost-effectiveness of 99.5% relative to a threshold of EUR 25,000. Conclusions: The model estimated that the intervention was cost-effective. The model’s simplifications and limitations should be considered when interpreting the findings in relation to policy-making decisions. Further research could include the costs and benefits of incidental findings and could assess the cost-effectiveness of repeated rounds of screening for the same population. Full article

Background/Objectives: This study aimed to determine the prevalence of aphthous ulcers following periodontal surgery and to identify demographic, behavioral, and clinical predictors of ulcer history before surgery and ulcer development after surgery. Methods: A cross-sectional study was conducted among 227 adult patients undergoing periodontal surgical procedures between November 2024 and May 2025. Demographic, medical, behavioral, and oral health data were collected. Postoperative follow-up at 1 and 2 weeks included a standardized clinical assessment of aphthous ulcers. Statistical analyses included descriptive statistics, chi-square tests, and Chi-squared Automatic Interaction Detection (CHAID) decision tree modeling. Results: Aphthous ulcers developed in 47 patients (20.7%), predominantly within the first postoperative week. CHAID analysis identified age, marital status, and smoking as predictors of preoperative ulcer history (classification accuracy: 73.6%), whereas age and family history predicted postoperative ulcer development (79.4%). Periodontal procedure type was significantly associated with postoperative medication prescription (χ² = 300.45, p < 0.001), suture selection (χ² = 69.19, p = 0.024), and ulcer number (χ² = 48.43, p = 0.031), but not ulcer size or anatomical location. Most ulcers were minor and primarily involved the buccal mucosa. Conclusions: Postoperative aphthous ulceration is a common complication of periodontal surgery, affecting approximately one-fifth of patients. Distinct risk profiles for pre- and post-surgical ulceration highlight the roles of patient-related susceptibility and surgical complexity. These findings support the use of structured risk stratification to guide preoperative counseling and targeted postoperative management. Full article

This study reports the synthesis and detailed characterization of pullulan-isononanoate (Pull-Iso), as well as the preparation and characterization of Pull-Iso films incorporating liposomes loaded with silibinin (SB) and smoke tree (Cotinus coggygria) extract (STExt), to explore the physicochemical and functional properties of pullulan-based biomaterials for potential biomedical applications. Pullulan was successfully esterified with isononanoic acid chloride, as confirmed by ¹H and ¹³C NMR (Nuclear Magnetic Resonance) and Fourier Transform Infrared (FTIR) spectroscopy. Modification significantly reduced the glass transition temperature (Tg), indicating enhanced chain mobility due to the introduction of bulky side chains. Prepared liposomes, embedding SB and extracted smoke tree compounds, exhibited particle sizes ~2000 nm with moderate polydispersity (~0.340) and zeta potential values around –20 mV, demonstrating lower colloidal stability over 60 days, thereby justifying their encapsulation within films. Optical microscopy revealed uniform liposome dispersion in Pull-Iso film with 0.5 g of liposomes, while higher liposome loading (0.75 g of liposomes) induced aggregation and microstructural irregularities. Mechanical analysis showed a reduction in tensile strength and strain at higher liposome content. The incorporation of liposomes encapsulating STExt and SB significantly enhanced the antioxidant activity of Pull-Iso-based films in a concentration-dependent manner, as demonstrated by DPPH and ABTS radical scavenging assays. These preliminary findings suggest that pullulan esterification and controlled liposome incorporation may enable the development of flexible, bioactive-loaded films, which could represent a promising platform for advanced wound dressing applications, warranting further investigation. Full article

The growing demand for sustainable solutions stimulates the building sector to develop environmentally friendly building materials. However, innovative natural-based options used in residential buildings must also comply with safety standards. This study examines the thermal and fire performance of insulation boards produced from tree bark fibers of two hardwood species, Tilia spp. (Lime) and Robinia pseudoacacia (Black Locust). The samples were fabricated using a wet process without adhesives and fire retardants, achieving thermal conductivity coefficient values of 0.055–0.057 W/m·K at densities ranging from 218 to 231 kg/m³. Density profiling revealed a characteristic vertical gradient associated with wet processing, while wettability measurements indicated hydrophobic surface behavior. Fire tests showed species-dependent behavior: Black Locust panels exhibited smaller damaged zones and lower maximum temperatures, whereas Lime panels showed deeper thermal degradation. No board ignition was observed, and smoke release remained moderate and consistent. Overall, these findings highlight the potential of bark-based insulation boards as sustainable alternatives in building applications. However, further optimization with larger sample sets and the integration of natural flame retardants is recommended to improve performance and safety. Full article

Importance: Patient with head and neck cancer of the oropharynx (HNC-OROP) undergo curative-intent definitive or post-operative radiation therapy. The systemic inflammation response index (SIRI) has independent prognostic capacity in HNC-OROP. We hypothesized that the use of SIRI may produce a parsimonious model of HNC-OROP outcomes. Objective: We aimed to investigate the prognostic utility of systemic inflammatory response index (SIRI) in oropharyngeal head and neck cancer patients who underwent radiation therapy. Design, Setting, and Participants: Random survival forest (RSF) machine learning was used to model survival in 568 oropharyngeal cancer patients in this retrospective cohort study. SIRI was calculated via pre-treatment bloodwork. Model validation was performed in an external cohort of 421 oropharyngeal cancer patients. Exposures: Exposure was curative-intent definitive or post-operative radiation therapy for head and neck cancer of the oropharynx (HNC-OROP). Results: This is a retrospective study with 568 and 421 patients in the Roswell Park and external Ohio State University cohorts. We evaluated full and reduced RSF models and a robust decision tree model. The C-index of the models was 0.758 (RSF full), 0.725 (RSF reduced), and 0.702 (decision tree). The incorporation of SIRI (with performance status and smoking history) into a machine learning model identified three risk-groups that significantly stratified overall survival (p < 0.0001). These findings were validated in the external validation cohort (p = 0.0019). Progression-free survival was also significantly different for the three groups in the validation cohort (p = 0.0025). Conclusions and Relevance: An integrated machine learning model using SIRI, performance status, and smoking history was successfully developed and externally validated in oropharyngeal head and neck cancer patients. Full article

Objectives: Determining individuals’ addiction levels plays a crucial role in facilitating more effective smoking cessation. For this purpose, the Fagerstrom Test for Nicotine Dependence (FTND) is used all over the World as a traditional testing method. It can be subjective and may influence the evaluation results. This study’s key innovation is the use of physiological signals to provide an objective classification of addiction levels, addressing the limitations of the inherently subjective Fagerström Test for Nicotine Dependence (FTND). Methods: Physiological parameters were recorded from 123 voluntary participants (both male and female) aged between 18 and 60 for 120 s using the Masimo Rad-G pulse oximeter and the Hartman–Veroval blood pressure monitor. All participants were categorized into four addiction groups: healthy, lightly addicted, moderately addicted, or heavily addicted with the help of FTND. The recorded data were classified using Decision Tree, KNN, and SVM methods. SMOTE and class-weighting techniques were used to eliminate class imbalance. Also, the PCA technique was applied for dimensionality reduction, and the k-fold cross-validation method was employed to enhance the reliability of the machine learning algorithms. Results: Machine learning methods, when evaluated using the SMOTE with a (7380×7) sample of physiological signals recorded every 2 s from 123 participants, showed a high recall of 98.74%, specificity of 99.58%, precision of 98.79%, F-score of 98.74%, and accuracy of 98.75%. Also, it is extracted that there is a direct relationship between physiological parameters and smoking addiction levels. Conclusions: The study’s core novelty lies in leveraging non-invasive physiological signals to objectively classify addiction levels, addressing the subjectivity of the Fagerström Test for Nicotine Dependence (FTND). This study provides a proof-of-concept for the feasibility of using machine learning and physiological signals to assess addiction levels. The results indicate that the approach is promising. Full article

Electrical cable insulation, mainly composed of polymeric materials, progressively deteriorates under thermal, electrical, mechanical, and environmental stress factors. This degradation reduces dielectric strength, thermal stability, and mechanical integrity, thereby increasing susceptibility to failure modes such as partial discharges, arcing, and surface tracking—recognized precursors of fire ignition. This review consolidates current knowledge on the degradation pathways of cable insulation and their direct link to fire hazards. Emphasis is placed on mechanisms including thermal-oxidative aging, electrical treeing, surface tracking, and thermal conductivity decline, as well as the complex interactions introduced by flame-retardant additives. A bibliometric analysis of 217 publications reveals strong clustering around material degradation phenomena, while underlining underexplored areas such as ignition mechanisms, diagnostic monitoring, and system-level fire modeling. Comparative experimental findings further demonstrate how insulation aging modifies ignition thresholds, heat release rates, and smoke toxicity. By integrating perspectives from materials science, electrical engineering, and fire dynamics, this review establishes the nexus between aging mechanisms and fire hazards. Full article

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration. Full article

Early wildfire detection in remote areas remains a critical challenge due to limited connectivity, intermittent solar energy, and the need for autonomous, long-term operation. Existing systems often rely on fixed sensing schedules or cloud connectivity, making them impractical for energy-constrained deployments. We introduce EcoWild, a reinforcement learning-driven cyber-physical system for energy-adaptive wildfire detection on solar-powered edge devices. EcoWild combines a decision tree-based fire risk estimator, lightweight on-device smoke detection, and a reinforcement learning agent that dynamically adjusts sensing and communication strategies based on battery levels, solar input, and estimated fire risk. The system models realistic solar harvesting, battery dynamics, and communication costs to ensure sustainable operation on embedded platforms. We evaluate EcoWild using real-world solar, weather, and fire image datasets in a high-fidelity simulation environment. Results show that EcoWild consistently maintains responsiveness while avoiding battery depletion under diverse conditions. Compared to static baselines, it achieves 2.4× to 7.7× faster detection, maintains moderate energy consumption, and avoids system failure due to battery depletion across 125 deployment scenarios. Full article

Cotinus coggygria is a widespread medicinal and aromatic species known for its ecological plasticity, pharmacological potential, and cultivation prospects. Despite its broad distribution across heterogeneous habitats, little is known about how local ecological and pedochemical factors influence its physiological traits and secondary metabolite production. This study addresses this knowledge gap by analyzing the eco-physiological and phytochemical variability of C. coggygria across six natural populations differing in substrate type and geochemical conditions. The research reveals significant inter-population variability in element accumulation, oxidative stress markers, morphometric traits, and the qualitative and quantitative composition of essential oils and phenolic compounds. Soil analyses demonstrated notable differences in element concentrations (e.g., Ca, Fe, Co, Zn) across localities, correlating with geochemical conditions. Morphological traits, such as leaf size and petiole length, varied significantly, with pronounced differences observed in plants from thermophilous and metalliferous habitats. Oxidative stress, indicated by malondialdehyde (MDA) levels, was highest in populations from thermophilous habitats. Phenolic compound analysis revealed locality-specific differences, with plants from thermophilous habitats exhibiting the highest concentrations of gallic acid, catechin, and rutin. Essential oil yield and composition also varied: leaves from metalliferous habitats had the highest monoterpene hydrocarbon content, while bark samples from thermophilous habitats showed elevated sesquiterpene levels. This comprehensive analysis underscores the interplay between habitat-specific conditions and the physiological and biochemical processes of C. coggygria. The findings provide valuable insights for optimizing substrate conditions and ecological management, with implications for the cultivation of the species to enhance the synthesis of bioactive compounds. These results support sustainable land use practices and the development of high-value plant-based products, offering significant implications for agriculture, pharmacology, and ecosystem restoration. Future studies should further explore the genetic and biochemical mechanisms underlying this species’ adaptability and resource optimization in heterogeneous environments. Full article

Extracting the shapes of individual tree crowns from high-resolution imagery can play a crucial role in many applications, including precision agriculture. We evaluated three CNN models—MASK R-CNN, YOLOv3, and SAM—and compared their tree crown results with OBIA-based reference datasets from UAV imagery for seven dates across one growing season. We found that YOLOv3 performed poorly across all dates; both MASK R-CNN and SAM performed well in May, June, September, and November (precision, recall, and F1 scores over 0.79). All models struggled in the early season imagery (e.g., March). MASK R-CNN outperformed other models in August (when there was smoke haze) and December (showing end-of-season variation in leaf color). SAM was the fastest model, and, as it required no training, it could cover more area in less time; MASK R-CNN was very accurate and customizable. In this paper, we aimed to contribute insight into which CNN model offers the best balance of accuracy and ease of implementation for orchard management tasks. We also evaluated its applicability within one software ecosystem, ESRI ArcGIS Pro, and showed how such an approach offers users a streamlined efficient way to detect objects in high-resolution UAV imagery. Full article

Smoke tree (Cotinus coggygria) is an important component of the urban landscape and represents red-leaf scenery in Beijing; however, Verticillium wilt, caused by Verticillium dahliae, has caused high mortality of smoke trees. Traditional control methods, such as chemical root irrigation and trunk injection, are problematic due to environmental pollution and potential plant damage. This study aimed to explore effective prevention and control methods for Verticillium wilt of smoke tree across different regions of red-leaf scenery in Beijing. In 2023, 240 smoke trees from the Pofengling Park of Beijing were selected for the study. Four different fungicides, a plant growth regulator and a biocontrol agent were tested. Three application methods (root irrigation, trunk spraying, and a combination of both) were used in the different trials. Based on the results of the 2023 trial, control trials were conducted under the disease classification in 2024 at key red-leaf scenery regions, such as Xiangshan Park, Xishan Park, and Pofengling Park. The bioagents of Bacillus subtilis root irrigation combined with the trunk spraying treatment group showed the best disease control effects. Calculated by the change in disease index in the treatment and blank groups, the corrective control effect in the treatment group reached 104.55%, and 60% of the plants remained healthy, indicating that this method of disease control was the most effective. Propiconazole root irrigation also had a significant effect on diseased smoke trees. Furthermore, validation experiments conducted in 2024 confirmed that various combinations of root irrigation and trunk spraying provided strong preventive and therapeutic effects on Verticillium wilt. In conclusion, the graded control measures demonstrated effective control of wilt at different disease index grades. This study offers an effective and practical solution for controlling Verticillium wilt, benefiting both environmental sustainability and landscape health. Full article

Background/Objectives: The workplace represents an ideal context for applying policies to foster a healthy lifestyle, guaranteeing advantages both to the individual and the company. Nevertheless, motivation to change one’s lifestyle remains an issue. This study aimed to determine subjects’ most valued intentions toward lifestyle changes and the target actions to improve lifestyles that they would be willing to invest in economically, information which might help design effective intervention programs. Methods: Classification trees were applied to 2762 employees/ex-employees (55.09 ± 13.80 years; 1107 females and 1655 males) of several Italian companies who voluntarily filled out an anonymous questionnaire on lifestyles (inquiring about, e.g., exercise, nutrition, smoking, and stress) to unveil specific subject typologies that are more likely associated with, e.g., manifesting a specific intention toward lifestyle changes and choosing the two most popular target actions resulting from the survey. Results: The main lifestyle aspect that respondents desired to improve was to become more physically active, and the most preferred tools chosen to improve their lifestyle were the possibility of having a medical specialist consultant to prescribe a tailored lifestyle program and buying a gym/swimming pool membership. Conclusions: This observational study might help tailor worksite health promotion and insurance services offered to employees, initiatives that may play an important role in fostering health/well-being and preventing chronic diseases in the more general population, especially in healthy or young subjects who are more prone to change their behavior if immediate benefits are seen instead of only advantages in the future. Full article