Search Results (2,894)

Background: One’s dietary pattern throughout life is established during the perinatal period, especially in the intrauterine environment. This study aims to analyze whether maternal and birth characteristics are associated with food consumption in young adults using baseline data from the Nutritionists’ Health Study (NutriHS). Methods: We employed cross-sectional analysis of data from 386 undergraduate nutrition students and nutritionists. Current food consumption was evaluated as per the NOVA classification. The maternal and birth factors included maternal age, parity, type of childbirth, health problems during pregnancy, prematurity, and birth weight, and multiple correspondence analysis of these variables was performed to identify patterns in them. Results: The energy contribution of ultra-processed foods was positively associated with the pattern characterized by participants whose mothers were 19 years of age or younger, primiparous, and had a vaginal delivery (β = 0.48; 95% confidence interval = 0.02, 1.66). Conclusions: We concluded that maternal age at birth was associated with the dietary patterns of adult children. Participants whose mothers were 19 or younger at birth had significantly higher consumption of ultra-processed foods in adulthood compared to those whose mothers were older. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

Background: Creatine supplementation is widely used to enhance exercise performance, mainly resistance training adaptations, yet its differential effects on upper- and lower-body strength and muscular power remain unclear across populations. Objective: This systematic review and meta-analysis aimed to quantify the effects of creatine supplementation in studies that included different exercise modalities or no exercise on upper- and lower-body muscular strength and power in adults. Methods: A comprehensive search of PubMed, Scopus, and Web of Science was conducted through 21 September 2024 to identify randomized controlled trials evaluating the effects of creatine supplementation on strength (bench/chest press, leg press, and handgrip) and power (upper and lower body). Weighted mean differences (WMDs) and 95% confidence intervals (CIs) were calculated using random-effects modeling. Subgroup analyses examined the influence of age, sex, training status, dose, duration, and training frequency. Results: A total of 69 studies with 1937 participants were included for analysis. Creatine plus resistance training produced small but statistically significant improvements in bench and chest press strength [WMD = 1.43 kg, p = 0.002], squat strength [WMD = 5.64 kg, p = 0.001], vertical jump [WMD = 1.48 cm, p = 0.01], and Wingate peak power [WMD = 47.81 Watts, p = 0.004] when compared to the placebo. Additionally, creatine supplementation combined with exercise training revealed no significant differences in handgrip strength [WMD = 4.26 kg, p = 0.10] and leg press strength [WMD = 3.129 kg, p = 0.11], when compared with the placebo. Furthermore, subgroup analysis based on age revealed significant increases in bench and chest press [WMD = 1.81 kg, p = 0.002], leg press [WMD = 8.30 kg, p = 0.004], and squat strength [WMD = 6.46 kg, p = 0.001] for younger adults but not for older adults. Subgroup analyses by sex revealed significant increases in leg press strength [WMD = 9.79 kg, p = 0.001], squat strength [WMD = 6.43 kg, p = 0.001], vertical jump [WMD = 1.52 cm, p = 0.04], and Wingate peak power [WMD = 55.31 Watts, p = 0.001] in males, but this was not observed in females. Conclusions: This meta-analysis indicates that creatine supplementation, especially when combined with resistance training, significantly improves strength in key compound lifts such as the bench or chest press and squat, as well as muscular power, but effects are not uniform across all measures. Benefits were most consistent in younger adults and males, while older adults and females showed smaller or non-significant changes in several outcomes. No overall improvement was observed for handgrip strength or leg press strength, suggesting that the ergogenic effects may be more pronounced in certain multi-joint compound exercises like the squat and bench press. Although the leg press is also a multi-joint exercise, results for this measure were mixed in our analysis, which may reflect differences in study design, participant characteristics, or variability in testing protocols. The sensitivity of strength tests to detect changes appears to vary, with smaller or more isolated measures showing less responsiveness. More well-powered trials in underrepresented groups, particularly women and older adults, are needed to clarify population-specific responses. Full article

Background: The association between dietary vitamin A intake and cancer risk remains unclear. There may be under-researched links between dietary vitamin A and cancer. This study aimed to clarify this relationship and a possible reference vitamin A intake. Methods: We conducted a hospital-based case–control study. Exposure data was determined from participants, including 3758 incident cancer cases (esophagus, stomach, colon, rectum, lung, breast, and other cancers) and 2995 hospital controls before the day of surgery treatment at the same hospitals. Dietary vitamin A intake was assessed using a validated semi-quantitative food frequency questionnaire. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the association between vitamin A intake and cancer risk. Restricted cubic splines suggest a safe range of vitamin A intake of 85.3–104.0 µg/day, which is a reference quantile. Results: We found a U-shaped association between vitamin A intake and cancer incidence compared to the reference. Both the lowest and highest intakes were associated with an increased cancer risk, with OR (95% CI) values 1.98 (1.57, 2.49) and 2.06 (1.66, 2.56), respectively. This U-shaped pattern was consistent across subgroups defined by sex, body mass index, smoking status, alcohol consumption, blood type A, and cancers of the esophagus, stomach, breast, and rectum, but not lung and colon cancer. The U-shaped relationship remained after adjusting for dietary vitamin A intake per kg of body weight and vitamin A–energy residual estimation adjustment. Confidence intervals were wider at the highest exposure levels. Conclusions: We observed a U-shaped relationship between vitamin A intake and the risk of cancer incidence, with a reference dietary vitamin A intake of 85.3–104.0 µg/day. These findings warrant further investigation to understand the mechanisms of this U-shaped association. Full article

Background/Objectives: Civic engagement may be a critical determinant of prognostic outcomes in persons with diabetes. Persons with diabetes are at increased risk for mobility impairment. Little research has explored the role of mobility difficulties in voting participation among persons with diabetes. Methods: To examine whether mobility difficulties influence voting participation in U.S. local elections among persons with diabetes, data was obtained from the 2022 National Health Interview Survey for persons with diabetes (n = 1398). The independent variable was mobility difficulties, defined as difficulty walking or climbing stairs. The dependent variable, voting participation, was assessed by an indication as to whether respondents voted in the last local elections. Weighted logistic regression assessed the influence of mobility difficulties on voting participation adjusting for potentially influential covariates among persons with diabetes. Results: Among persons with diabetes, less than one fifth (18.8%) voted in the last local election and half (48.9%) reported difficulties walking or climbing stairs. In models adjusting for covariates, persons with diabetes who indicated they had difficulties in walking or climbing stairs were significantly less likely to indicate they had voted in the last local election in comparison with those without mobility difficulties (adjusted odds ratio (AOR) = 0.638, 95% confidence interval (CI) = (0.443, 0.918)). Persons with diabetes who were female, married, had graduated from college or technical school, or rated their health as good/very good were significantly more likely to report having voted in a local election. Conclusions: Initiatives are needed to foster voting participation among persons with diabetes and mobility difficulties. Full article

Background/Objectives: There is a need for prediction models which enable weighing benefits against risks of different treatment strategies for individual Hodgkin lymphoma (HL) patients. Therefore, we aimed to predict absolute risk of progression, first relapse or death (PRD) with and without incorporating HL treatment. Methods: The prognostic and treatment information of 2343 patients treated for classical HL at ages 15–60 years between 2008 and 2018 in the Netherlands was used to predict absolute risk of PRD up to 5 years after diagnosis using Cox proportional hazard models allowing for time-varying coefficients. Models were externally validated in 1675 patients treated for classical HL in Denmark between 2000 and 2018. Results: In early stages, gender, leukocyte, and lymphocyte counts were associated with risk of PRD. Additionally, receiving >4 cycles of ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) or ABVD plus radiotherapy predicted lower risk of relapse compared with receiving ≤4 cycles of ABVD. In advanced stages, age, albumin and leukocyte counts predicted PRD risk. Receiving (escalated) BEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, prednisone) predicted lower PRD risk compared to ABVD. In Danish patients treated between 2008 and 2018, adding treatment information improved 5-year Inverse Probability of Censoring Weighted (IPCW) Area Under the Curve (AUC) values from 0.63 (95% Confidence Interval (CI): 0.55–0.72) to 0.71 (95% CI: 0.63–0.79) in early stages (p-value = 0.04) and from 0.59 (95% CI: 0.52–0.65) to 0.62 (95% CI: 0.55–0.68) in advanced stages (p-value = 0.33). Conclusions: We developed well calibrated models with reasonable discrimination, not only incorporating pre-treatment prognostic factors but also treatment effect enabling the prediction of absolute risk of first relapse/progression. Full article

Background/Objectives: The Internet of Things (IoT), integrated with application software, has increasingly been used to support health management through monitoring indicators like physical activity, sleep, and heart rate, in pregnant and postpartum women. However, limited evidence exists regarding its effectiveness in improving health outcomes for pregnant and postpartum women. The objective of this systematic review and meta-analysis was to evaluate and synthesize the role of IoT in enhancing the health outcomes of pregnant and postpartum women. Methods: A systematic search was conducted on 13 February 2023, across CENTRAL, CINAHL, ClinicalTrials.gov, Embase, MEDLINE, PsycINFO, PubMed, and WHO ICTRP to identify all randomized controlled trials. Studies were included if they involved pregnant or postpartum women in high-income countries and used sensor-based data collection via smartphones or wearable devices. Two reviewers independently selected the studies, extracted data, and assessed the risk of bias using the Cochrane Collaboration’s risk of bias assessment tool 2.0. We performed a pairwise meta-analysis using a random effects model. The findings were reported according to PRISMA guidelines. Results: Seven studies with 1638 pregnant and postpartum women were included in this review. Of the seven included studies, half targeted women with gestational diabetes and the other half targeted obese women. A meta-analysis revealed that IoT interventions may reduce gestational weight gain in women with obesity with a mean difference of −3.35 kg (95% confidence interval (CI): −5.23 to−1.46; I² = 36%; two studies; 242 women; moderate certainty of evidence). Conclusions: This review suggested that IoT interventions may limit gestational weight gain in pregnant women with obesity. Future studies should evaluate the long-term effects of IoT-based interventions on maternal and neonatal health outcomes. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

Background: Spontaneous pneumothorax (SP) in adolescents has a high recurrence risk, particularly without surgical treatment. This study aimed to predict recurrence using machine learning (ML) algorithms applied to chest computed tomography (CT) and to visualize CT features associated with recurrence. Methods: We retrospectively reviewed 299 adolescents with conservatively managed SP from January 2018 to December 2022. Clinical risk factors were statistically analyzed. Chest CT images were evaluated using ML models, with performance assessed by AUC, accuracy, precision, recall, and F1 score. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visual interpretation. Results: Among 164 right-sided and 135 left-sided SP cases, recurrence occurred in 54 and 43 cases, respectively. Mean recurrence intervals were 10.5 ± 9.9 months (right) and 12.7 ± 9.1 months (left). Presence of blebs or bullae was significantly associated with recurrence (p < 0.001). Neural networks achieved the best performance (AUC: 0.970 right, 0.958 left). Grad-CAM confirmed the role of blebs/bullae and highlighted apical lung regions in recurrence, even in their absence. Conclusions: ML algorithms applied to chest CT demonstrate high accuracy in predicting SP recurrence in adolescents. Visual analyses support the clinical relevance of blebs/bullae and suggest a key role of apical lung regions in recurrence, even when blebs/bullae are absent. Full article

Maturity estimation before seed collection is necessary in reducing the costs of seed collection; it allows vigorous seeds to be collected, ensuring that maximum germination will be reached and producing quality planting stock. In addition to this, appropriate temperature, seed size, pH, light, and stress conditions also influence germination. Cones of Cedrus deodara were collected at different intervals to estimate the maturity of the cones. A seed germination test was conducted in the laboratory under constant temperature, seed size, pH, light conditions, and water and salinity stress conditions. Significant (p < 0.05) variations in cones, such as seed morphological characteristics, germination, and related parameters, of C. deodara at different maturity periods were observed. The morphological traits of cones, such as seed weight, seed length, seed width, and seed germination, increased with increasing maturity, while the cone weight, moisture contents, specific gravity, and seed moisture decreased with increasing maturity. A constant temperature of 15 °C to 20 °C (98.0% to 92.0%) and the use of large-sized seeds (99.0%) led to maximum germination. Lower concentrations of Polyethylene glycol (98.0%) and NaCl (78.0%) contributed to maximum seed germination. The germination of C. deodara is temperature-dependent and seed size, light, and high water and salinity stress significantly influence seed germination. Full article

Sample storage is a key factor in odour quantification. This study investigates the loss of odorous compounds in Nalophan™ sampling bags during storage, simulating real-world transport and storage conditions. The goal was to quantify compound leakage over time by varying operational parameters to identify the most significant losses. The tested compounds—sulphur, oxygenated, and hydrocarbon VOCs—were prepared in the laboratory at 10 ppm. Tests were conducted on 12 L Nalophan™ bags with sampling intervals of 0, 6, 30, 48, and 100 h, exceeding the EN 13725 guideline limits (30 h). To evaluate recovery, environmental and internal humidity and temperature were varied. Additionally, the adsorption surface was increased by inserting Nalophan™ flakes inside the bags. The results show that under ambient conditions, losses during 30 h are contained and are within the order of instrumental uncertainty for all the tested compounds. A higher ambient temperature and humidity did not significantly affect recovery. In contrast, internal humidity appeared to have a more noticeable effect, particularly affecting low molecular weight sulphur compounds and oxygenates. These findings suggest optimal storage strategies for olfactometric samples, highlighting that significant losses do not occur within the EN 13725:2022 storage time limits. Moreover, even exceeding these time limits, the observed losses remain limited to 100 h. Full article

The accurate prediction of the pavement structural modulus is crucial for maintenance planning and life-cycle assessment. While recent deep learning models have improved predictive accuracy using Falling Weight Deflectometer data, challenges remain in effectively structuring time-series inputs and ensuring robustness against noise measurement. This paper presents an integrated framework that combines systematic time-step modeling with perturbation-based robustness evaluation. Five distinct input sequencing strategies (Plan A through Plan E) were developed to investigate the impact of temporal structure on model performance. A hybrid Wide & Deep ResRNN architecture incorporating SimpleRNN, GRU, and LSTM components was designed to jointly predict four-layer moduli. To simulate real-world sensor uncertainty, Gaussian noise with ±3% variance was injected into inputs, allowing the Monte-Carlo-style estimation of confidence intervals. Experimental results revealed that time-step design plays a critical role in both prediction accuracy and robustness, with Plan D consistently achieving the best balance between accuracy and stability. These findings offer a practical and generalizable approach for deploying deep sequence models in pavement modulus prediction tasks, particularly under uncertain field conditions. Full article

To address the inefficiencies of traditional numerical simulations and the high cost of experimental validation in the aerodynamic–stealth integrated design of S-shaped inlets for aero-engines, this study proposes a novel parameter prediction model based on a fuzzy C-means (FCM) clustering and nonlinear dynamic adaptive particle swarm optimization-enhanced radial basis function neural network (NDAPSO-RBFNN). The FCM algorithm is applied to reduce the feature dimensionality of aerodynamic parameters and determine the optimal hidden layer structure of the RBF network using clustering validity indices. Meanwhile, the NDAPSO algorithm introduces a three-stage adaptive inertia weight mechanism to balance global exploration and local exploitation effectively. Simulation results demonstrate that the proposed model significantly improves training efficiency and generalization capability. Specifically, the model achieves a root mean square error (RMSE) of $3.81\times {10}^{-8}$ on the training set and $8.26\times {10}^{-8}$ on the test set, demonstrating robust predictive accuracy. Furthermore, $98.3\%$ of the predicted values fall within the $y=x\pm 3\beta$ confidence interval ($\beta =1.2\times {10}^{-7}$). Compared with traditional PSO-RBF models, the number of iterations of NDAPSO-RBF network is lower, the single prediction time of NDAPSO-RBF network is shorter, and the number of calls to the standard deviation of the NDAPSO-RBF network is lower. These results indicate that the proposed model not only provides a reliable and efficient surrogate modeling method for complex inlet flow fields but also offers a promising approach for real-time multi-objective aerodynamic–stealth optimization in aerospace applications. Full article

Background/Objectives: Suboptimal gestational weight gain (GWG) has been linked to increased risks of adverse maternal outcomes. Evidence linking diet in pregnancy to GWG remains limited. We assessed relationships between adherence to five dietary patterns (Planetary Health Diet [PHD], Dietary Approaches to Stop Hypertension [DASH], Alternate Mediterranean Diet [AMED], Healthy Eating Index [HEI], and Alternate Healthy Eating Index [AHEI]) and 2009 Institute of Medicine GWG categories. Methods: Women expecting singleton pregnancies participated in the NICHD Fetal Growth Studies and completed a food-frequency questionnaire (FFQ) at 8 to 13 weeks of gestation that captured their baseline diet. Adherence to each dietary pattern was calculated, with higher scores indicating greater adherence. Women were categorized into low, moderate or high adherence to each dietary pattern. Using multinomial logistic regression, we estimated adjusted odds ratios and 95% confidence intervals [OR (95% CIs)] of inadequate or excessive GWG (reference category: adequate), for high vs. low adherence to each dietary pattern. Results: In the full cohort, women with high vs. low adherence to DASH, AMED, HEI, or AHEI (but not PHD) had a 13% to 31% lowered odds of inadequate total GWG [ranging from 0.87 (0.58, 1.31) for AMED to 0.69 (0.48, 0.99) for DASH]. High adherence to DASH or HEI was associated with lower odds of inadequate first-trimester GWG, after correcting for multiple testing [DASH: 0.36 (0.22, 0.61), HEI: 0.49 (0.30, 0.79)]. No significant association was observed between any of the dietary patterns and excessive total and trimester-specific GWG. Conclusions: Greater adherence to several dietary patterns was associated with lowered odds of inadequate GWG. Future studies could characterize these diets objectively by identifying metabolite signatures and examining their associations with GWG. Full article

In response to the complex challenges posed by gob-side entry retaining in medium-thick coal seams—specifically, severe stress concentrations and unstable surrounding rock under composite roof structures—this study presents a comprehensive field–numerical investigation centered on the 5-200 working face of the Dianping Coal Mine, China. A three-dimensional coupled stress–displacement model was developed using FLAC3D to systematically evaluate the mechanical behavior of surrounding rock under varying roof cutting configurations. The parametric study considered roof cutting heights of 6 m, 8 m, and 10 m and cutting angles of 0°, 15°, and 25°, respectively. The results indicate that a roof cutting height of 8 m combined with a 15° inclination provides optimal stress redistribution: the high-stress zone within the coal rib is displaced 2–3 m deeper into the coal body, and roof subsidence is reduced from 2500 mm (no cutting) to approximately 200–300 mm. Field measurements corroborate these findings, showing that on the return airway side with roof cutting, initial and periodic weighting intervals increased by 4.0 m and 5.5 m, respectively, while support resistance was reduced by over 12%. These changes suggest a delayed main roof collapse and decreased dynamic loading on supports, facilitating safer roadway retention. Furthermore, surface monitoring reveals that roof cutting significantly suppresses mining-induced ground deformation. Compared to conventional longwall mining at the adjacent 5-210 face, the roof cutting approach at 5-200 resulted in notably narrower (0.05–0.2 m) and shallower (0.1–0.4 m) surface cracks, reflecting effective attenuation of stress transmission through the overburden. Taken together, the proposed roof cutting and pressure relief strategy enables both stress decoupling and energy dissipation in the overlying strata, while enhancing roadway stability, reducing support demand, and mitigating surface environmental impact. This work provides quantitative validation and engineering guidance for intelligent and low-impact coal mining practices in high-stress, geologically complex settings. Full article