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An axiomatic system engineering design method based on NSGA-II algorithm applied to complex systems
Water in the ‘Devil’s Comet’ shares a source with Earth’s oceans
Clinical study on the effect of low-intensity pulsed ultrasound on healing of proximal sesamoid bone fractures in Yili horses
Abstract The incidence of sports injuries in horses is increasing, thus accurate assessment, diagnosis, and treatment are critical. Among common sports-related injuries, proximal sesamoid bone fractures (PSBFs) are one of the most frequent types. To investigate the effects of low-intensity pulsed ultrasound (LIPUS) on imaging and hematological parameters of PSBFs, providing a clinical reference for the diagnosis and treatment of PSBFs in racehorses. After clinical diagnosis and radiographic examination confirmed the disease and its location, the affected horses were randomly divided into two groups: the ultrasound group received daily LIPUS treatment, while the control group received no intervention. After a four-week treatment, lameness grading, imaging, and hematological parameters were reassessed to evaluate PSBF healing and changes in blood indicators. Both imaging and hematological examinations play important clinical roles in the early diagnosis and later-stage evaluation of diseases. In this study, following LIPUS treatment, the affected horses showed significantly increased ALP activity in the serum, and higher levels of serum calcium (Ca), phosphorus (P), and the calcium-phosphorus product (Ca × P) (P < 0.05). Additionally, analysis of inflammatory cytokines revealed that the expression of pro-inflammatory markers, TNF-α, IL-1β, and IL-6, were markedly elevated during the early stages of fracture but gradually returned to normal as treatment progressed. These findings suggest that LIPUS can accelerate healing by alleviating inflammation at the fracture site. This study found that LIPUS treatment of PSBFs significantly improved microcirculation at the fracture site and promoted efficient healing through enhanced callus formation and accelerated bone repair. This favorable outcome of LIPUS therapy provides a valuable clinical reference for future diagnosis and treatment of PSBFs.
Multi-locus genome-wide association study reveals genomic regions underlying root system architecture traits early growth stage of sorghum germplasm
Time to develop aspiration pneumonia and its predictors among stroke patients admitted at specialized hospitals in Western Amhara during the armed conflict period, 2024
Elemental logging evidence for paleoenvironmental reconstruction of the ordovician strata in the Western Ordos basin, China
The art of precision in unveiling hydrogen solubility in bines through data-driven modeling
Author Correction: FOXF1 inhibits invasion and metastasis of lung adenocarcinoma cells and enhances anti-tumor immunity via MFAP4/FAK signal axis
Author Correction: Discovery of novel HBV core protein inhibitors by high throughput virtual screening
The evolution of discrimination under finite memory constraints
Viscous and thermal effects on wave propagation in a micropolar viscothermoelastic medium with impedance considerations
Atomic dynamics of gas-dependent oxide reducibility
Effects of altering walking foot strike pattern in combination with lower extremity strengthening on lower limb alignment in females with patellofemoral pain: a randomized controlled trial
Deep transfer learning and attention based P2.5 forecasting in Delhi using a decade of winter season data
Abstract This investigation focuses on the phenomenon of air pollution in the metropolitan area of Delhi, with a particular emphasis on the stubble-burning season, during which concentrations of PM 2.5 reach their peak, presenting significant health hazards. Utilizing a comprehensive dataset spanning a decade (2012–2022), this study analyzes the influence of meteorological conditions, urban emissions, and seasonal biomass combustion. It amalgamates historical PM 2.5 concentration data, relevant meteorological variables, and FIRECOUNT data to capture the temporal and pollution dynamics. Feature selection based on CorrXGBoost was utilized to find and keep the most significant predictors, hence decreasing model complexity while maintaining predictive efficacy. The proposed hybrid TL-LSTM-MHA Long Short-Term Memory (LSTM) model, augmented with Multi-Head Attention, is employed, harnessing transfer learning techniques to facilitate enhanced computational efficiency and generalization capabilities. The model demonstrated good performance (MAE = 4.38, RMSE = 5.80, R2 = 0.9972) and was extensively verified using tenfold cross-validation to ensure robustness towards overfitting and non-stationary effects. Statistical significance tests, particularly the Wilcoxon signed-rank test, were used to confirm the performance disparities among model variations, therefore substantiating the roles of essential architectural elements. Attention weight visualization and head-wise interpretability studies demonstrated unique patterns in feature significance across heads. The model’s efficacy was also assessed against traditional and contemporary state-of-the-art methods tested on similar PM 2.5 forecasting tasks, demonstrating its enhanced accuracy. This research provides predictive insights pertinent to regulatory decision-making about seasonal air quality management encountered in Delhi. The scalability of the proposed framework is demonstrated by comparing it to conventional and transfer learning-based models.