Search Results (178)

The impact hammer, equipped with a force transducer, is a portable and practical tool for inducing measurable excitations in structural health monitoring (SHM). However, its reliability is often limited by uncontrolled factors such as swing power, angle, impact location, and operator consistency, particularly in nonlinear structures operating at low frequencies. While many researchers have avoided hammer testing by instead using better controlled drop mass systems or operational modal analysis (OMA) techniques, this study presents a new experimental modal analysis (EMA) approach that improves the accuracy of impact hammer testing: variable impulse hammer impact testing (VIHIT) using a single-input single-output (SISO) roving hammer and single fixed accelerometer. For a mode of interest, the imaginary component of the frequency response function (FRF) is evaluated at each test location using multiple impulses of varying magnitude. This output quantity exhibits an inverse power relationship with the input autopower spectral density (APSD) at the modal frequency. Evaluating the trend at a reference input APSD from sufficiently excited tests produces a very accurate mode shape for that input. For a given structure, nonlinear damping ratios vary with excitation and can be extracted using inverse FRF analysis. This method addresses variability in impact hammer testing by establishing reproducible trends for different impulse levels and test locations. Application to degraded timber beams demonstrated reductions in mode shape variability relative to conventional averaging and revealed impulse-dependent damping ratios ranging from approximately 0.02 to 0.04, highlighting the method’s ability to characterize nonlinear dynamic behavior. The result is a more accurate approach for extracting modal properties and mode shapes and characterizing nonlinear dynamic behavior using a SISO roving impact hammer system. Full article

The impact of wind on disdrometer measurements has not yet been demonstrated through controlled reproducible physical experiments. This study aims to provide quantitative evidence of the deviation in raindrop trajectories approaching the sensing area of an optical disdrometer (the Thies Clima LPM) when immersed in a wind flow with a known velocity and direction relative to the sensor orientation. To this end, water drops with diameters between 0.9 mm and 1 mm were released in a wind tunnel and directed towards the instrument’s sensing area. Their trajectories were measured using a high-speed camera and compared with those expected in undisturbed conditions, as well as with the airflow field around the instrument body as measured in previous studies. This experiment provided the first direct measurement of the deviation in individual drop trajectories induced by wind near the Thies Clima LPM, a disdrometer commonly used in hydrological studies and applications. The effect of the non-radially symmetric geometry of the instrument on wind direction was observed, identifying the configuration most affected (parallel to the laser beam). The repeatability of the drop releasing system was checked by releasing multiple drops from the same position. This allowed attributing differences in the observed trajectories to a variation in the drop diameter. The collected dataset can be used to validate numerical models of the wind-induced bias of disdrometers and to develop adjustment functions for field measurements. Full article

Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a transparent nine-stage reconciliation pipeline applied to 51 years (1975–2025) of national HRGC incident data from the Federal Railroad Administration Form 57 and Form 71 datasets. The hierarchical pipeline integrated deterministic alignment and multistage inference methods to produce an audited, geographically consistent dataset. The study formalizes four longitudinal county-level cumulative exposure indices that characterize spatiotemporal patterns of incident concentration relative to static population and infrastructure denominators. These metrics include accumulated incidents per million population (AIPM), accumulated incidents per crossing (AIPC), crossings per million population (CPM), and crossings per 100 square miles (CPHSM). All four metrics exhibited pronounced right-skewness: AIPM, CPM, and CPHSM approximated exponential forms, and AIPC approximated a log-normal form. Statistical tests detected statistically significant tail deviations in three metrics; CPM did not reject the exponential fit at conventional significance levels. Spatial analysis shows coherent regional concentration in incident rates in the Central Plains and lower Mississippi corridors. The national time series exhibits a late-1970s plateau, sustained exponential decline beginning around 1980, and stabilization but persistent incident rates after 2001. Population-normalized AIPM remained statistically indistinguishable between the reconciled and record-dropped datasets; however, crossing-based metrics changed materially when reconstructing denominators from the reconciled crossing universe. Statistical comparisons confirmed that incident-only denominators introduced substantial measurement bias in local risk assessment. State-level rank reversals persisted even when omnibus distributional tests failed to reject equality. By formalizing multistage data cleaning and quantifying its analytical impact over an unprecedented longitudinal horizon, this study establishes denominator integrity and geographic reconciliation as prerequisites for valid HRGC exposure assessment and provides a framework for future predictive modeling. Full article

Superhydrophilic micro/nano-porous media have extensive applications in electronic thermal management and energy storage systems. Predicting two-phase pressure drop in complex porous structures is of great importance for system design and optimization while remaining highly challenging. This study systematically investigates the two-phase flow resistance characteristics of bi-porous microstructures with multiple particle sizes and porosities under varying boiling regimes. Experimentally, porous samples were fabricated via vacuum sintering using nickel powders and pore-forming agents (CaCl₂), which exhibit superhydrophilicity and enhanced wicking characteristics. A visualized experimental platform was constructed to investigate the impact of pore size combinations, flow velocities, and boiling states on pressure drop. The dataset obtained through multi-factor saturated boiling experiments was further used to derive a semi-empirical model for the two-phase flow pressure drop based on the classic Kozeny-Carman (K-C) and Akagi-Chisholm (A-C) correlations. Results show that the pore size combinations and boiling states have a significant impact on the resistance performance. The proposed model achieves an average prediction deviation below 15.7%, confirming its reliability and its effectiveness as a design framework for optimizing high-capillary-force porous wicks in advanced thermal management systems. Full article

Background/Objectives: Reproducible evaluation of aerosol dispersibility remains a key challenge in the development of dry powder inhalers (DPIs), where small variations in particle cohesion, morphology, or device resistance can lead to large differences in aerodynamic performance. In passive DPIs, the forces required for powder fluidization and aerosolization arise from the interaction of patient inspiratory airflow with device geometry and must overcome strong interparticle cohesive forces to enable effective lung delivery. Cascade impaction is the gold standard for determining aerodynamic particle size distribution (APSD), but its low throughput and experimental burden limit its utility for systematic formulation and device screening. Prior studies have explored laser diffraction-based particle sizing under varying dispersion energies as indirect metrics of powder dispersibility. Here, we extend this approach by introducing a mathematically rigorous, distribution-based framework that applies the first-order Wasserstein distance (Earth Mover’s Distance) to quantify relative dispersibility with respect to a material-specific maximally dispersed reference state. Methods: Mannitol, trehalose, and inulin were spray-dried under matched conditions to generate model dry powders. Particle size distributions were measured by laser diffraction (Sympatec HELOS/R) using both a RODOS dry dispersion module to define a maximally dispersed reference state and an INHALER module to generate aerosols under clinically relevant dispersion conditions spanning multiple device resistances and pressure drops. For each condition, the Wasserstein-1 distance (W₁) was computed between cumulative volume-based size distributions obtained under reference and inhaler-based dispersion. Cascade impaction was used as an orthogonal method to characterize aerodynamic performance under a representative dispersion condition. Results: W₁ captured formulation-, device-, and flow-dependent differences in dispersibility that were not readily separable by visual inspection of particle size distributions alone. Crystalline mannitol exhibited the largest and most flow-rate-dependent W₁ values, whereas amorphous trehalose and polymeric inulin showed smaller W₁ values with distinct, non-monotonic pressure responses that depended on device resistance. W₁ qualitatively aligned with cascade impaction metrics, exhibiting a positive association with mass median aerodynamic diameter and an inverse association with fine particle fraction, while also demonstrating that efficient dose emission can occur despite incomplete deagglomeration. Conclusions: This study establishes the Wasserstein distance as a physically interpretable, formulation-agnostic metric for quantifying aerosol dispersibility relative to a material-specific reference state. This framework enables systematic comparison of dispersion efficiency across devices and operating conditions using standard laser diffraction data and provides a reproducible basis for mechanistic optimization of DPI formulations and inhaler designs. Full article

This study presents a detailed evaluation of how inlet air filter differential pressure (Filter DP) affects the operational performance of a gas turbine, focusing on its influence on power generation and thermal efficiency. Real operating data combined with machine learning (ML) techniques were used. Following the installation of new filters, the turbine operated for 10,000 h, and 4438 h under base-load conditions were selected for modeling. The impact of Filter DP was examined using Multiple Linear Regression (MLR), Quadratic Support Vector Regression (SVR), Regression Tree, and Artificial Neural Network (ANN) models, allowing both linear and nonlinear behavior to be captured. Results show that each 1 mbar increase in Filter DP leads to roughly a 1.67 MW drop in power output and a 0.094% reduction in thermal efficiency. Additionally, higher Filter DP raises fuel consumption and causes an extra 0.45 kgCO₂e of emissions per 1 MWh of electricity produced. These findings underline that even small increases in inlet pressure loss significantly affect economic and environmental performance. Filter fouling increases natural gas demand, CO₂e emissions, and overall carbon footprint. The ML-based approach enhances predictive maintenance by enabling early detection of filter degradation and supporting more efficient and sustainable turbine operation. Full article

Lennox–Gastaut syndrome (LGS) is a rare and severe developmental and epileptic encephalopathy characterized by multiple drug-resistant seizure types, mandatory tonic seizures, cognitive and behavioral impairment, and distinctive electroencephalographic features, including slow spike–wave discharges and generalized paroxysmal fast activity. Despite decades of therapeutic advances, LGS remains associated with profound lifelong disability and the absence of a single disease-defining molecular mechanism. Recent advances in genetics, neurophysiology, and network neuroscience have reframed LGS as a convergent network encephalopathy, in which diverse genetic, structural, metabolic, immune, and acquired insults funnel into shared molecular hubs, leading to thalamocortical network dysfunction. This framework helps explain the limited efficacy of purely syndrome-based treatments. This review synthesizes current evidence on electroclinical phenotyping, molecular and network pathogenesis, and contemporary diagnostic workflows and proposes a molecule-to-precision-therapy framework for LGS. We critically appraise pharmacologic, dietary, surgical, and neuromodulatory therapies, emphasizing drop seizures as a major driver of morbidity. Among available treatments, cannabidiol shows the most consistent and clinically meaningful efficacy for drop seizures, with benefits extending beyond seizure counts to seizure-free days and caregiver-relevant outcomes. Finally, we highlight key gaps and future directions, including etiology-stratified trials, network-guided interventions, and outcome measures that capture long-term developmental and quality-of-life impacts. Full article

Background: Chronic post-stroke spasticity often limits gait despite best-practice botulinum-toxin intramuscular injections (BTIs), whose benefit is constrained by short duration, dose ceilings, and tachyphylaxis. Cryoneurolysis (CNL) induces a reversible axonotmesis with preserved endoneurium, potentially providing longer tone reduction with fewer adverse effects, but its impact on whole-gait quality and its compatibility with implanted functional electrical stimulation (FES) remain poorly documented. Case presentation: A 43-year-old man, 12 years after right middle cerebral artery stroke, walked independently with an implanted common peroneal FES system but complained of effortful gait with left-knee “locking” and drop foot without FES. Multiple BTI series to triceps surae and quadriceps yielded only transient benefit. Two ultrasound-guided CNL sessions targeted tibial (soleus, medial gastrocnemius) and femoral (rectus femoris, vastus intermedius) motor branches. Quantitative gait analysis and fine-wire electromyography (EMG) were performed at baseline, 6 weeks after each CNL, and at 6 months, with and without FES. CNL produced immediate and sustained reductions in triceps surae and quadriceps overactivity, resolution of genu recurvatum, normalization of stiff-knee gait, improved ankle dorsiflexion, and increased swing phase knee flexion (>50°). Gait Deviation Index rose from 69 to 80 and Gillette Gait Index decreased by more than 50%, with preserved strength and without adverse events. Conclusions: Targeted, sequential CNL of tibial and femoral motor branches can safely deliver durable, clinically meaningful gait improvements when BTI has reached its ceiling and can act synergistically with implanted FES. Quantitative gait analysis and EMG sharpen clinical decision-making in spasticity management. Full article

The cotton bollworm, Helicoverpa armigera (Hübner), a cosmopolitan agricultural pest, inflicts severe impacts on global agriculture. As a poikilotherm, it was highly susceptible to climate change, yet critical gaps persist in understanding how its sensitivity interacts with climatic shifts—knowledge essential for integrated pest management (IPM). We, therefore, analyzed H. armigera’s susceptibility to temperature variations using long-term pest population and meteorological data from Maigaiti and Bachu Counties (southern Xinjiang) and Shawan County (northern Xinjiang). The results showed H. armigera populations increased overall, with reduced interannual fluctuation magnitude. The main meteorological factors influencing the interannual population changes of H. armigera in Maigaiti, Bachu, and Shawan were T_max difference in winter (98.0%), T_min difference in May (80.7%), and T_min difference in July (99.4%), respectively. Higher winter temperature (particularly February) reduced the spring population sizes across all three regions, with only the population in Bachu showing a significant correlation. For annual populations, warmer winter caused a significant decline in Bachu, a marked increase in Maigaiti, and a non-significant rise in Shawan. Summer temperature below 33 °C boosted populations in all regions; above 33 °C, the Maigaiti population declined non-significantly, while the Bachu population dropped significantly. Climate warming advanced the pest’s first appearance, delayed its disappearance, and extended its active period, increasing population size—a trend projected to intensify in the future. Maigaiti and Shawan populations were governed by T_max in winter and T_min in July, respectively, whereas the Bachu population was constrained by temperature differences during multiple key growth and development periods throughout the year. These divergent regulatory patterns and climatic responses reflect varying vulnerability levels, providing a theoretical basis for targeted H. armigera control. Full article

Modern electronic devices, such as smartphones and drones, integrate various sensors to enable diverse sensor-based applications. Yet, sensor measurements exhibit significant variations across different device models, even under the same environment. These variations arise from sensor biases, which occur in three different types: offset bias (additive constant errors), scale bias (multiplicative proportional errors), and drift bias (time-dependent or temperature-dependent errors). Among the biases, in this paper we specifically target offset bias, which has the greatest impact in typical smartphone usage scenarios. This generally leads to performance degradation in sensor-based applications across various device models and instances. To understand the characteristics of the offset bias, we categorize sensors into sensors with and without absolute reference values. Sensors with absolute references enable direct calibration using theoretical true values, while sensors with relative references require different approaches depending on how sensor applications process the data. For scalar-based applications that determine the current state by comparing a sensor measurement against a pre-defined reference, the offset biases can be removed by the existing procedures using reference devices. However, for sequence-based applications that determine the current state by analyzing relative changes in a sequence, the offset bias issue has not been addressed yet. We propose initial value removal and mean removal algorithms that statically and dynamically remove the offset biases from the sensor data sequences for these sequence-based applications. We evaluate our bias normalization algorithms for two different use cases in a geomagnetic-based indoor positioning system (IPS). First, we evaluate the impact of our bias normalization algorithms on the positioning performance of our LSTM-based IPS. Without bias normalization, although the reference device (Galaxy S23 Plus) showed an average positioning error of 0.6 m, the other three smartphone models (Galaxy S22 Plus, iPhone 15, and iPhone 16 Pro) exhibited much worse positioning performance, with errors of 2.48 m, 18.21 m, and 13.13 m. However, after applying our bias normalization, the average positioning errors of all models dropped below 0.68 m. Second, we also evaluate the impact of the bias normalization on detecting whether the position of a smartphone is in a pocket or in a hand-held state. For this, we analyze the sequence of light sensor measurements. We improved the detection accuracy from 42.3% to 97.6% with bias normalization across all device models without requiring individual threshold settings. Full article

This study investigates the mechanical behavior and fatigue performance of orthotropic steel bridge decks, with a focus on rib-to-deck welded connections and the impact of geometric symmetry on stress distribution. Two full-scale models with full-penetration butt welds were tested under static compression loads, yielding failure forces of 27 kN (experimental) and 26 kN (analytical), with only a 3% difference. Finite element simulations using ANSYS 16.1 validated these results and enabled parametric studies. Rib plate thicknesses ranging from 5 mm to 9 mm were analyzed to assess their influence on stress distribution and deformation. The geometric ratio h′/tr, which reflects the symmetry of the trapezoidal rib web, was found to be a critical factor in stress behavior. At h′/tr = 38 (tr = 7 mm), compressive and tensile stresses are balanced, demonstrating a symmetric stress field; at h′/tr = 33 (tr = 8 mm), and fatigue performance at the RDW root drops by 47%. Increasing h′/tr improves fatigue life by increasing the number of load cycles to failure. Stress contours revealed that compressive stress concentrates in the rib plate above the weld toes, while tensile stress localizes at the RDW root. The study highlights how symmetric geometric configurations contribute to balanced stress fields and improved fatigue resistance. Multiple linear regression analysis (SPSS-25) produced predictive equations linking stress values to applied load and geometry, offering a reliable tool for estimating stress without full-scale simulations. These findings underscore the importance of optimizing h′/tr and leveraging structural symmetry to enhance resilience and fatigue resistance in welded joints. This research provides practical guidance for improving the design of orthotropic steel bridge decks and contributes to safer, longer-lasting infrastructure. Full article

Background: Recent advancements in Artificial Intelligence (AI) have the potential to address the challenges of mammographic screening programs by enhancing the performance of Computer-Aided Detection (CAD) systems, improving detection accuracy, and reducing false positive rates and recall rates. These systems were mostly investigated by control trials using cancer-enriched datasets and multiple readers. Objectives: This study aims to evaluate the real-world impact of AI integration on the performance of a breast cancer screening program. Methods: In January 2021, our mammography unit integrated an AI system (iCAD version 2.0) into its mammographic screening protocol. This study evaluates audit data of 31,176 mammograms interpreted between 2017 and 2021, comparing 24,373 mammograms prior to AI implementation and 6803 after the integration. Logistic regression analysis was used to assess the statistical significance of changes in key screening metrics, with a significance level of p < 0.05. Results: This study assesses the impact of artificial intelligence (AI) on mammographic screening. The cancer detection rate increased significantly from 6.2 per 1000 in 2019 to 9.3 per 1000 in 2021, with cancers detected on mammograms rising to 98%. Stage 1 cancer detection reached 100%, and the false negative rate dropped to 0%. Additionally, ductal carcinoma in situ (DCIS) detection decreased from 36.4% in 2019 to 20% in 2021. These findings highlight AI’s effectiveness in improving cancer detection accuracy and efficiency. Conclusions: The integration of AI into mammographic screening demonstrated promising results in improving cancer detection rates and reducing false negative rates. These findings highlight AI’s potential to enhance screening efficacy. Full article

Distinct PAEs are implicated in breast cancer progression through multiple molecular pathways. This study aims to elucidate the potential mechanisms in common by which PAEs promote breast cancer progression. Dibutyl phthalate (DBP), benzyl butyl phthalate (BBP), and diethylhexyl phthalate (DEHP) were selected as representative PAE compounds. Network toxicology guided the construction of a regulatory network centered on five key transcription factor-associated genes: TP53, CTNNB1, PPARA, ESR1, and CDKN2A. Differential expression and survival analyses confirmed the significant impact of these hub genes on breast cancer (p < 0.05). Molecular docking results revealed direct interactions between the three PAEs and hub targets, while BBP had the strongest PAE-hub gene interaction and DEHP had the weakest one. Computer-aided molecular design (CAMD), combined with molecular docking, found the importance of alkyl chains and phenyl in PAE-hub gene interaction. A group addition/subtraction controlled experiment revealed that the binding affinities of modified BBP variants to hub genes are all weaker than the unmodified parent. The drop was significant whether the C17 alkyl chain was lengthened to match DEHP (p = 0.026) or the phenyl group was removed (p = 0.022). The findings provide novel insights into the mechanism in common of PAE-promoting breast cancer and offer a foundation for the unified intervention strategies and the design of safer plasticizer alternatives. Full article

This study systematically investigated the structural damage and electrochemical performance changes in 18650 cylindrical lithium-ion batteries under multiple impacts through a 10 kg drop-hammer impact test. The experimental results showed that as the state of charge (SOC) increased from 25% to 75%, the battery’s stiffness increased and its impact resistance improved, but the electrolyte leakage intensified, with a higher risk of leakage at high SOCs. An increase in the impact force led to enhanced voltage fluctuations and a continuous increase in deformation. After an impact of 500 mm, the voltage decreased about 0.02 V, while after an impact of 1000 mm, it dropped about 0.04 V. Axial impacts caused a sudden voltage drop to 1.96 V, resulting in permanent failure; compared with planar impacts, cylindrical surface impacts are more likely to cause compression in the middle and warping at both ends, significantly increasing the risk of internal short circuits. CT scans revealed that the battery porosity can reach up to 3.09% under high impact energy, and the deformation rate can reach 28.39%. The research results provide a quantitative experimental basis for the impact-resistant design and safety assessment of power batteries. Full article

This study examines the influence of steel fiber reinforcement on the mechanical properties of geopolymer concrete incorporating different slag to fly ash binder ratios (75:25, 50:50, and 25:75). Three fiber contents (0%, 1%, and 2%) by volume were used to assess their impact on compressive strength, flexural strength, initial stiffness, and toughness. Compressive tests were conducted at 1, 7, and 28 days, while flexural behavior was evaluated through a four-point bending test at 28 days. The results showed that geopolymer concrete with 75% slag and 25% fly ash experienced the highest compressive strength and modulus of elasticity, regardless of the steel fiber content. The addition of 1% and 2% steel fiber content enhanced the compressive strength by 17.49% and 28.8%, respectively, compared to the control sample. The binder composition of geopolymer concrete plays a crucial role in determining its compressive strength. Reducing the slag content from 75% to 50% and then to 25% resulted in a 15.1% and 33% decrease in compressive strength, respectively. The load–displacement curves of the 2% fiber-reinforced beams display strain-hardening behavior. On the other hand, after the initial crack, a constant increase in load causes the specimen to experience progressive strain until it reaches its maximum load capacity. When the peak load is attained, the curve gradually drops due to a loss in load-carrying capacity known as post-peak softening. This behavior is attributed to steel’s ductility and is evident in specimens 75S25FA2 and 50S50FA2. Concrete with 75% slag and 25% fly ash demonstrated the highest peak load but the lowest ultimate displacement, indicating high strength but brittle behavior. In contrast, concrete with 75% fly ash and 25% slag showed the lowest peak load but the highest displacement. Across all binder ratios, the addition of steel fibers enhanced the flexural strength, initial stiffness, and toughness. This is attributed to the bridging action of steel fibers in concrete. Additionally, steel fiber-reinforced beams exhibited a ductile failure mode, characterized by multiple fine cracks throughout the midspan, whereas the control beams displayed a single vertical crack in the midspan, indicating a brittle failure mode. Full article