Search Results (15,148)

Indoor air pollution from gas stove combustion remains a public health concern, given links to adverse cardiorespiratory health effects, yet few studies have characterized or compared the air quality impacts of different gas-based cooking fuels. We investigated kitchen-level concentrations of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) concentrations in four homes in Central Pennsylvania that used natural gas and/or biogas fueled stoves. We conducted time-resolved kitchen monitoring and assessed pollutant concentrations during cooking and non-cooking periods. We applied linear mixed-effect regression models with kitchen-level random effects and time-varying covariates to estimate the influence of fuel type on indoor air quality. During cooking, mean kitchen NO₂ concentrations during cooking were more than 160% higher in homes using natural gas compared with biogas (95% confidence interval [CI]: 109.4%, 211.1%), although both levels remained below the WHO guideline. PM_2.5 concentrations showed limited sensitivity to fuel type, with modest differences observed. Adjusted mixed-effect regression models revealed attenuated but consistent associations, with natural gas use increasing NO₂ exposure by 2.8 ppb, or 60.3% (95% CI: 1.7, 4.6 ppb). These findings suggest further research into understanding the exposure and health benefits of alternative fuels in residential kitchen settings is merited. Full article

Citizen science provides a valuable approach for tracking plastic pollution; however, its effectiveness is often limited by methodological inconsistencies, concerns about data quality, and a persistent gap between data collection and policy implementation. This systematic review addresses the key question: What constitutes a comprehensive set of best practices for addressing these issues and enhancing the scientific and societal impact of citizen science in monitoring plastic pollution from source to sea? Analyzing 84 studies, from beach cleanups to microplastic sampling, this review synthesizes best practices and identifies remaining gaps. It presents a structured framework designed to enhance data quality and volunteer participation. Key challenges include the ‘microplastic analytical bottleneck,’ the ‘digital divide,’ and notable geographical and demographic disparities that hinder the integration of policies. While citizen science is effective for large-scale data collection, its main challenge is translating data into actionable policies. The main contribution of this review is a series of practical recommendations aimed at improving methodological consistency, ensuring fair volunteer participation, and facilitating the transition from citizen data to evidence-based environmental management, thereby enhancing the effectiveness and impact of citizen science. Full article

Polymer worm gears are increasingly utilized in electric power steering (EPS) systems due to their favorable manufacturing features and performance. Ensuring consistent mechanical properties under various operating conditions is critical for steering reliability throughout a vehicle’s lifespan. This study investigates the durability of injection-molded polyamide 66 worm gears within a Pinion-EPS configuration, where torque from the assist motor is transmitted through a worm–worm gear set to the rack and ultimately to the vehicle wheels. Given the complexity of steering maneuvers and the absence of mechanical integrity in steer-by-wire systems, durability testing becomes essential to understand if the considered worm gear for a certain steering system application provides safety and the needed performance within a specified product service life. This paper compares multiple testing methodologies. Traditional approaches, such as maximum torque and rotational speed, prove insufficient for comprehensive durability assessment, especially considering the thermal sensitivity of polymer materials. The findings highlight the limitations of conventional testing methods and emphasize the need for application-specific testing methods that reflect real-world boundary conditions. This research contributes to the development of more accurate and reliable evaluation techniques for polymer gear components in modern EPS systems, with implications for both conventional and autonomous vehicle platforms. Full article

The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase classification. The study addressed these limitations by combining active and passive remote sensing and developing a machine learning framework for cloud detection and cloud-top thermodynamic phase classification. Utilizing the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud product from 2021 as the truth reference, the model was trained with spatiotemporally collocated datasets from FY3D/MERSI-II (Medium Resolution Spectral Imager-II) and CALIOP. The AdaBoost (Adaptive Boosting) machine learning algorithm was employed to construct the model, with considerations for six distinct Arctic surface types to enhance its performance. The accuracy test results showed that the cloud detection model achieved an accuracy of 0.92, and the cloud recognition model achieved an accuracy of 0.93. The inversion performance of the final model was then rigorously evaluated using a completely independent dataset collected in July 2022. Our findings demonstrated that our model results align well with results from CALIOP, and the detection and identification outcomes across various surface scenarios show high consistency with the actual situations displayed in false-color images. Full article

Laser Powder Bed Fusion (L-PBF) parts combine geometric freedom with process-induced rough surfaces that challenge residual-stress metrology. We evaluated the accuracy of the incremental hole-drilling (IHD) method with electronic speckle pattern interferometry (ESPI) by applying defined stresses via four-point bending to stress-relieved AlSi10Mg coupons, rather than measuring unknown process stresses. Flat specimens (2 mm, thin per ASTM E837) were analyzed on up-skin, side-skin, and CNC-milled surfaces; thin-specimen calibration coefficients were used. After a preliminary inter-specimen check (three specimens per surface; spread < 8 MPa), one representative specimen per surface was tested with three drill sites to assess intra-specimen uniformity. Measured IHD–ESPI stresses agreed best at 70 MPa: deviations were ~4.1% (up-skin), 6.0% (side-skin), and 6.24% (CNC-milled). At 10 MPa the relative errors increased (23.6%, 18.4%, and 1.40%), consistent with reduced ESPI signal-to-noise and fixture compliance in the low-stress regime. At 140 MPa, deviations rose again (21.1%, 14.3%, and 13.1%), reflecting operation near the ~60% Rp0.2 elastic limit of hole-drilling and potential local plasticity. Surface-dependent artifacts also mattered as follows: the side-skin required no coating and performed comparably to CNC-milled, whereas the up-skin’s roughness plus matting spray introduced fringe distortions and chip/coating debris near the hole. This controlled study indicates that IHD–ESPI can provide reliable results on L-PBF AlSi10Mg in the mid-stress range when surface preparation, coating, and rig compliance are carefully managed. Limitations include excluding down-skin surfaces and testing only one specimen per condition; thus, results should be generalized cautiously. Full article

Background/Objectives: This study aimed to systematically evaluate the accuracy and clinical relevance of ChatGPT-4’s answers to a set of questions on upper tract urothelial carcinoma (UTUC) as adjudged by expert urologists. To juxtapose performance, one question set consisted of queries derived from the 2025 EAU (European Association of Urology) guidelines, and the other was a miscellaneous set of frequently asked patient-oriented questions. Methods: Seventy-seven questions were posed to ChatGPT-4 in English: 60 were systematically selected from the 2025 EAU UTUC guidelines, and 17 were compiled as frequently asked questions (FAQs) from reputable urology sources. Two board-certified urologists independently scored the given responses, employing (a) binary scoring (correct = 1, incorrect = 0) and (b) detailed accuracy scoring (1 = completely accurate, 2 = accurate but inadequate, 3 = mixed accurate–misleading, 4 = completely inaccurate). Comparative analyses used the Mann–Whitney U test with effect size estimation. Results: Overall, 71 of 77 responses (92.2%) were correct. Accuracy rates were 90.0% (54/60) for EAU guideline questions and 100.0% (17/17) for the FAQs. The mean accuracy score for the guideline-based questions was 1.28 ± 0.74, compared with 1.00 ± 0.00 for the FAQs. Differences between the groups were not statistically significant (p = 0.094, r = 0.191). A subgroup analysis showed perfect accuracy (100%) in four EAU categories—Classification and Staging Systems, Diagnosis, Disease Management, and Metastatic Disease Management—while the Follow-up category had the lowest accuracy (25% correct, mean score = 2.75), indicating domain-specific limitations. Conclusions: ChatGPT-4 demonstrated high overall accuracy, particularly for patient-oriented UTUC questions, but showed reduced reliability in complex, guideline-specific areas, especially follow-up protocols. The model shows promise as an educational tool for patients but cannot replace expert clinical judgment for decision-making. These findings have important implications for the integration of AI tools in urological practice and highlight the need for domain-specific optimization. Full article

Bridge Structural Health Monitoring (BSHM) is vital for assessing structural integrity and operational safety. Traditional wired systems are limited by high installation costs and complexity, while existing wireless systems still face issues with cost, synchronization, and reliability. Moreover, cloud-based methods for extreme event detection struggle to meet real-time and bandwidth constraints in edge environments. To address these challenges, this study proposes a lightweight wireless BSHM system based on edge computing, enabling local data acquisition and real-time intelligent detection of extreme events. The system consists of wireless sensor nodes for front-end acceleration data collection and an intelligent hub for data storage, visualization, and earthquake recognition. Acceleration data are converted into time–frequency images to train a MobileNetV2-based model. With model quantization and Neural Processing Unit (NPU) acceleration, efficient on-device inference is achieved. Experiments on a laboratory steel bridge verify the system’s high acquisition accuracy, precise clock synchronization, and strong anti-interference performance. Compared with inference on a general-purpose ARM CPU running the unquantized model, the quantized model deployed on the NPU achieves a 26× speedup in inference, a 35% reduction in power consumption, and less than 1% accuracy loss. This solution provides a cost-effective, reliable BSHM framework for small-to-medium-sized bridges, offering local intelligence and rapid response with strong potential for real-world applications. Full article

Persistent challenges in software documentation, particularly limitations in generality, simplicity, and efficiency of existing models, impede effective software development. To address these, this research proposes a novel phase-based and holistic software documentation model (PDA-SDD). This model was subsequently evaluated using a digital questionnaire distributed to 150 software development and documentation experts, achieving a 48% response rate (n = 72). The evaluation focused on assessing the proposed model’s generality, simplicity, and efficiency. Findings indicate that while certain sub-models (e.g., SRSD, RLD) were positively received across all criteria and the overall model demonstrated strong perceived generality and efficiency in specific aspects, areas for improvement were identified, particularly regarding terminological consistency and user-friendliness. This study contributes to the understanding of the complexities in achieving a universally effective software documentation model and highlights key considerations for future research and development in this critical area of software engineering. Full article

Accurate sea clutter modeling is crucial for clutter suppression in edge radar processing. On resource-constrained edge radar platforms, spatiotemporal statistics, together with device-level computation and memory limits, hinder the learning of representative clutter features. This study presents a transformer-based generative adversarial model for sea clutter modeling. The core design of this work uses axial attention to factorize self-attention along pulse and range, preserving long-range dependencies under a reduced attention cost. It also introduces a two-dimensional variable-length spatiotemporal window that retains temporal and spatial coherence across observation lengths. Extensive experiments are conducted to verify the efficacy of the proposed method with quantitative criteria, including a cosine similarity score, spectral-parameter error, and amplitude–distribution distances. Compared with CNN-based GAN, the proposed model achieves a high consistency with real clutter in marginal amplitude distributions, spectral characteristics, and spatiotemporal correlation patterns, while incurring a lower cost than standard multi-head self-attention. The experimental results show that the proposed method achieves improvements of 9.22% and 7.8% over the traditional AR and WaveGAN methods in terms of the similarity metric, respectively. Full article

To address the limitations of conventional magnetically coupled resonant wireless power transfer (MCR-WPT) systems in multi-frequency multi-load applications—specifically inadequate load power independence and high complexity inconstant-voltage/constant-current (CV/CC) control—this paper proposes a variable-structure dual-frequency dual-load wireless power transfer system by first establishing its mathematical model and implementing hybrid-frequency modulation for multi-frequency output, then developing an improved T/LCC hybrid resonant topology by deriving parameter design conditions for compensation network reconfiguration under CV/CC requirements, subsequently employing an orthogonal planar solenoid coupling mechanism and frequency-division demodulation to achieve load-independent power regulation across wide load ranges for enhanced stability, and finally constructing a 120 W dual-frequency dual-load prototype to validate the system’s CV/CC characteristics, where simulations and experimental results demonstrate stronger consistency with theoretical predictions. Full article

Heart rate variability (HRV), which quantitatively characterizes fluctuations in beat-to-beat intervals, serves as a critical indicator of cardiovascular and autonomic nervous system health. The inherent ability of non-contact methods to eliminate the need for subject contact effectively mitigates user burden and facilitates scalable long-term monitoring, thus attracting considerable research interest in non-contact HRV sensing. In this study, we propose a novel algorithm for HRV extraction utilizing FMCW millimeter-wave radar. First, we developed a calibration-free 3D target positioning module that captures subjects’ micro-motion signals through the integration of digital beamforming, moving target indication filtering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering techniques. Second, we established separate phase-based mathematical models for respiratory and cardiac vibrations to enable systematic signal separation. Third, we implemented the Second Order Spectral Sparse Separation Algorithm Using Lagrangian Multipliers, thereby achieving robust heartbeat extraction in the presence of respiratory movements and noise. Heartbeat events are identified via peak detection on the recovered cardiac signal, from which inter-beat intervals and HRV metrics are subsequently derived. Compared to state-of-the-art algorithms and traditional filter bank approaches, the proposed method demonstrated an over 50% reduction in average IBI (Inter-Beat Interval) estimation error, while maintaining consistent accuracy across all test scenarios. However, it should be noted that the method is currently applicable only to scenarios with limited subject movement and has been validated in offline mode, but a discussion addressing these two issues is provided at the end. Full article

Diabetes is a common chronic disease. Untreated diabetes may lead to complications such as nephropathy, neuropathy, retinopathy, and macroangiopathies. The main goal in treating diabetes is to limit the development of vascular complications. The FGF (fibroblast growth factor) family, with its potential as a biomarker for diabetic complications, offers a promising avenue for future research and treatment. The study aimed to analyze and compare the concentrations of selected fibroblast growth factors, FGF-2, FGF-19, FGF-22, and FGF-23, in the plasma of patients with type 1 and type 2 diabetes with those of the control group. The study group consisted of 73 patients, including 33 people with type 1 diabetes (18 M and 15 W) aged 18 to 68 years and 40 with type 2 diabetes (20 M and 20 W) aged 25 to 90. The control group consisted of 41 healthy individuals (23 men and 18 women) aged 21 to 56. The FGF-2, FGF-19, FGF-22, and FGF-23 concentrations were measured using ELISA. The study observed a significant relationship between the levels of FGF19 and FGF22 in the serum of patients with type 1 and type 2 diabetes, as well as in the control group (p < 0.001; p < 0.001). Statistical analysis revealed a significant relationship between FGF-2 and FGF-22 concentrations and hypertension (p = 0.03; p = 0.01). A statistically significant difference was also found between the concentrations of FGF-19 and FGF-22 (p = 0.001; p < 0.001) in the serum of people with normal weight and people with overweight and obesity. A significant correlation was also observed between the concentrations of FGF-22 and FGF-23 and arthritis (p = 0.01; p = 0.02). FGF-2, FGF-19, FGF-22, and FGF-23 likely significantly impact diabetes and its complications. In the future, they could serve as biomarkers for diabetic complications, aiding in diagnosis, patient monitoring, and even predicting potential complications for individuals. However, more research in this area is necessary. Full article

Large language models (LLMs) have advanced rapidly as tools for automating code generation in scientific research, yet their ability to interpret and use unfamiliar Python APIs for complex computational experiments remains poorly characterized. This study systematically benchmarks a selection of state-of-the-art LLMs in generating functional Python code for two increasingly challenging scenarios: conversational data analysis with the ParShift library, and synthetic data generation and clustering using pyclugen and scikit-learn. Both experiments use structured, zero-shot prompts specifying detailed requirements but omitting in-context examples. Model outputs are evaluated quantitatively for functional correctness and prompt compliance over multiple runs, and qualitatively by analyzing the errors produced when code execution fails. Results show that only a small subset of models consistently generate correct, executable code. GPT-4.1 achieved a 100% success rate across all runs in both experimental tasks, whereas most other models succeeded in fewer than half of the runs, with only Grok-3 and Mistral-Large approaching comparable performance. In addition to benchmarking LLM performance, this approach helps identify shortcomings in third-party libraries, such as unclear documentation or obscure implementation bugs. Overall, these findings highlight current limitations of LLMs for end-to-end scientific automation and emphasize the need for careful prompt design, comprehensive library documentation, and continued advances in language model capabilities. Full article

Background/Objectives: The integration of strength training in grassroots youth soccer remains limited, often due to persistent myths regarding its safety and utility. This study evaluated the effectiveness of a tailored, playful strength training program in young players and analyzed the influence of the Relative Age Effect (RAE) on physical development and training response. Methods: A 14-week quasi-experimental pretest–posttest design was conducted with 27 federated male soccer players aged 9–10 years (experimental: n = 15; control: n = 12). The intervention consisted of twice-weekly, game-based strength training sessions integrated into the regular team routine. Outcomes included validated anthropometric (BMI) and functional (handgrip strength, standing long jump, Illinois agility test) indicators. RAE was analyzed according to birth quartile. Non-parametric statistical analyses and effect size (r) calculations were used. Results: The experimental group achieved significantly greater pre–post gains in handgrip strength than controls (right and left). Agility and standing long jump improved within the experimental group, but between-group differences were not significant. BMI decreased within the experimental group, yet the net between-group difference in BMI change was not significant in the context of a higher baseline BMI (p = 0.047). Although the Relative Age Effect (RAE) influenced baseline BMI, no moderating effect was detected on performance variables or training-induced changes in this sample. Conclusions: In U10 soccer players, a playful, context-integrated strength program produced superior gains in handgrip strength compared with usual practice. Improvements in agility and standing long jump were observed within the intervention group, but did not exceed those of the controls. BMI changes were not different between the groups and must be interpreted with caution, given baseline imbalances. In this sample, RAE did not moderate training response. Full article

Background: Hormone-receptor-positive (HR+)/HER2-negative (HER2−) breast cancer (BC) is characterized by low immunogenicity and an immunosuppressive microenvironment. These features likely contribute to the inconsistent clinical activity of immune checkpoint inhibitors (ICIs) in this BC subtype. We conducted a systematic review of clinical trials evaluating ICIs in HR+/HER2− BC patients, focusing on potential biomarkers of response and resistance to these drugs. Methods: We systematically searched in Medline via PubMed, EMBASE, and CENTRAL for phase II/III clinical trials published between 2013 and 2023, testing ICIs alone or in combination with other agents in HR+/HER2− BC patients at any stage. All the searches were performed up to 27 January 2024. Data on study characteristics, clinical outcomes, and biomarker profiles were extracted, and due to study heterogeneity, a narrative synthesis was performed, without risk-of-bias assessment or meta-analysis. Results: Twenty-five studies were included, with 3298 patients enrolled overall. Eighteen of these trials enrolled patients with advanced disease. All trials investigated ICI combination regimens, more frequently with chemotherapy, CDK4/6 inhibitors, or other immunotherapeutic agents. Most of the studies enrolling patients with advanced disease failed to show a significant clinical activity of ICIs. In the early setting, neoadjuvant chemo-immunotherapy with nivolumab or pembrolizumab increased the rate of complete responses compared to chemotherapy alone. Moreover, high programmed death-ligand 1 (PD-L1) expression, low ER (estrogen receptor), and high tumor-infiltrating lymphocyte (TIL) levels correlated with improved outcomes. Consistently, markers indicating enhanced immune activation, such as the MammaPrint High 2 (MP2) genomic signature, were associated with increased ICI sensitivity. Discussion: Despite the limited overall efficacy, ICIs may represent a viable therapeutic option for a selected subset of HR+/HER2− BC patients. However, this systematic review is limited by study heterogeneity and the inclusion of ongoing or immature trials, which prevents quantitative analysis and may affect future conclusions on ICIs in HR+/HER2− breast cancer. Finally, optimized combination strategies could enhance tumor immunogenicity, while predictive biomarkers such as PD-L1, TILs, or specific genomic signatures could identify responsive patients. Full article