Search Results (198)

The aim of this research was to identify the 50 articles most frequently referenced concerning patellofemoral instability (PFI) and to analyze their features. A search was performed in the Thomson ISI Web of Science using keywords such as “patellofemoral instability,” “patellar instability,” “patellar dislocation,” and “patella luxation.” This research included all publications related to PFI, covering aspects such as diagnostic and both nonoperative and operative treatment. The citation counts for the 50 articles ranged from 165 to 1024 citations. Notably, the top ten articles received a minimum of 348 citations each. In total, 84% (n = 42) of the studies were clinical, while the remainder consisted of basic science investigations (including three anatomical and five biomechanical studies). The predominant level of evidence was IV, accounting for 32%. The American Journal of Sport Medicine was responsible for publishing 34% of these articles. Most of the research took place in the United States and twelve additional countries. The years when the most-referenced papers were published spanned from 1985 to 2020, with the 2000s representing the highest share of articles (74%), and the years between 2006 and 2010 showing the peak quantity of articles (n = 15). This article provides a building block in the PFI management. The selection of these articles is useful for learning more about current trends on PFI and anticipating future developments. Full article

The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition. Full article

To address the issue of voice leakage during the rapid deployment of meeting rooms, a piezoelectric flexible interference structure (PFIS) for active sound masking is developed in this paper. The PFIS uses rubber as the base, allowing it to bend or fold, offering good flexibility. The PFIS generates vibration through direct contact with the target object, without the need for adhesives or installation, fulfilling the need for rapid deployment. The experiment studied the driving of PFIS under three types of interference signals, analyzing the interference performance of PFIS by combining the vibration response of the surface of the table. The results show that the vibration response generated by PFIS on the surface of the table is significantly greater than when only a human voice is present. When a 3.5 kg weight is added to the surface of PFIS, its vibration performance increases by 5.6 times. Furthermore, increasing the voltage enhances the vibration interference effect of the PFIS across the entire frequency range; after adding weight, the vibration interference performance of the PFIS is significantly improved for frequencies above 2500 Hz. It has been verified that PFIS has strong vibration interference performance, effectively masking the vibrations of objects under human voice, providing a new technical solution for information security protection in sensitive areas. Full article

The growing reliance on solar energy necessitates robust and interpretable forecasting models for stable grid management. Current research frequently employs Explainable AI (XAI) to glean insights from complex black-box models, yet the reliability and consistency of these explanations remain largely unvalidated. Inconsistent feature attributions can mislead grid operators by incorrectly identifying the dominant drivers of solar generation, thereby affecting operational planning, reserve allocation, and trust in AI-assisted decision-making. This study addresses this critical gap by conducting a systematic statistical evaluation of feature rankings generated by multiple XAI methods, including model-agnostic (SHAP, PDP, PFI, ALE) and model-specific (Split- and Gain-based) techniques, within a time-series regression context. Using a LightGBM model for one-day-ahead solar power forecasting across four sites in Calgary, Canada, we evaluate consensus and divergence using the Friedman test, Kendall’s W, and Spearman’s rank correlation. To ensure the generalizability of our findings, we further validate the results using a CatBoost model. Our results show a strong overall agreement across methods (Kendall’s W: 0.90–0.94), with no statistically significant difference in ranking (p > 0.05). However, pairwise analysis reveals that the “Split” method frequently diverges from other techniques, exhibiting lower correlation scores. These findings suggest that while XAI consensus is high, relying on a single method—particularly the split count—poses risks. We recommend employing multi-method XAI and using agreement as an explicit diagnostic to ensure transparent and reliable solar energy predictions. Full article

Low-income individuals are unlikely to save relatively large sums on a regular basis; however, many still fall short of even the modest threshold required for long-term financial security. This study examines the determinants of benchmark-defined undersaving among retail e-payment agents (REAs) operating in the urban slums of Lagos, Nigeria. We use a contingent valuation survey, descriptive analysis, and logistic regression to examine how selected behavioural and demographic factors, alongside a 60-day experimental intervention—the Programmed Microsaving Scheme (PMSS), a hard daily commitment savings device—affect the likelihood of undersaving, defined as saving less than 12% of each REA’s average daily income. While the PMSS appears to have contributed to improvements in post-treatment saving participation and performance among REAs, it did not significantly increase the likelihood of reaching or exceeding the benchmark savings threshold. Consistent with this, average daily income, age, gender, marital status, education, and religion are statistically insignificant predictors of benchmark-defined undersaving. In contrast, self-control, measured using a literature-validated instrument, exhibits a statistically significant negative association with benchmark-defined undersaving, indicating that higher self-control reduces the likelihood of failing to meet the benchmark. Measured risk aversion similarly shows no significant association. Notably, this study introduces a novel 60-day PMSS, co-designed with REAs and neobanks to accommodate daily income savings—a characteristic of the informal sector largely overlooked in the literature on commitment savings devices. From a policy perspective, the findings suggest that while short-horizon commitment devices (such as the 60-day PMSS) and financial literacy are associated with improvements in microsavings among low-income daily earners, achieving benchmark-level saving might require longer-term and more adaptive mechanisms that address income volatility and mitigate other inherent risks. Full article

Vehicle emissions are key precursors to near-ground ozone and secondary aerosol formation. While China’s clean air actions have significantly reduced particulate pollution, ozone levels continue to rise in some city clusters, calling for a deeper understanding of volatile organic compound (VOC) emissions from gasoline vehicles. This study systematically evaluated the impacts of fuel composition (China 6b vs. Methyl tert-butyl ether -free (MTBE-free) gasoline), engine type (Port fuel injection (PFI) vs. Gasoline direct injection (GDI)), and ambient temperature (25 °C vs. −7 °C) on VOC emissions and ozone formation potential (OFP) under the World Harmonized Light-Duty Test Cycle (WLTC). Results of dynamometer experiments showed that MTBE-free gasoline reduced total VOC emissions by 47% compared to China 6b fuel, with aromatics accounting for 69% of this reduction. PFI vehicles exhibited higher VOC emissions than GDI vehicles at 25 °C, though this difference diminished at −7 °C. Low temperatures significantly increased VOC emissions and OFP, increasing by a factor of 10–13 compared to 25 °C. Aromatics were the dominant OFP contributors under all conditions. Our findings highlight the importance of fuel reformulation and temperature-specific emission controls in mitigating ozone pollution, particularly under cold-start conditions. Full article

Strawberry is widely cultivated due to its short growth cycle, high yield, and significant profits. In high-latitude cold regions, the planting area of overwintering strawberry has expanded rapidly in recent years. However, although daytime temperatures inside solar greenhouses rise quickly with solar radiation, plants are frequently subjected to persistent nocturnal low-temperature stress (nocturnal temperature below 10 °C). This stress restricts photosynthesis, delays growth, and markedly reduces yield. Therefore, accurately evaluating the tolerance of strawberry varieties to low nocturnal temperatures is crucial for unheated overwintering production in cold regions. This study selected Snow White, Benihoppe, and Kaorino as experimental materials for overwintering cultivation trials in a typical cold-region solar greenhouse. We measured and analyzed growth and development, photosynthetic characteristics, phenological traits, and fruit yield. Based on photosynthetic physiology and phenotypic traits, we constructed the Photosynthesis–Fluorescence Index (PFI), the Production–Phenotype Index (PPI), and the Nocturnal Cold Tolerance Index (NCTI). The results showed that Kaorino exhibited significantly higher values in all three indices compared with Benihoppe and Snow White. After exposure to low night temperatures, Kaorino exhibited rapid photosynthetic induction, strong maintenance of PSII activity, vigorous growth, early maturation, and high yield. Moreover, all three composite indices were strongly and positively correlated with total yield (R² > 0.97), demonstrating their effectiveness in distinguishing the nocturnal low-temperature tolerance of strawberry cultivars. These composite indices provide a scientifically robust method for selecting suitable cultivars for unheated overwinter strawberry production in high-latitude cold regions. Full article

Methanol is a liquid fuel with high oxygen content and the potential for a closed-loop carbon-neutral production cycle. To investigate the mixture formation and combustion characteristics of a heavy-duty Port Fuel Injection (PFI) methanol engine, a three-dimensional numerical simulation model was established using the CONVERGE 3.0 software. Multi-cycle simulations were performed to analyze the influence of wall film dynamics on engine performance. The results indicate that the “adhesion–evaporation” equilibrium of the intake port wall film determines the in-cylinder mixture concentration. Due to the high latent heat of vaporization of methanol, severe wall-wetting occurs during the initial cycles, causing the actual fuel intake to lag behind the injection and leading to an overly lean mixture and misfire. Regarding injection strategies, the open valve injection (OVI) strategy utilizes high-speed intake airflow to reduce wall adhesion and improve fuel transport efficiency compared to closed valve injection. OVI refers to the fuel injection strategy that injects fuel into the intake port during the intake valve opening phase. The open valve injection strategy (e.g., SOI −500° CA) demonstrates distinct superiority over closed valve strategies (SOI −200°/−100° CA), achieving a 75% reduction in wall film mass. The long injection duration and early phasing allow the high-speed intake airflow to carry fuel directly into the cylinder, significantly minimizing wall film accumulation and avoiding the “fuel starvation” observed in closed-valve strategies. Additionally, OVI fully utilizes methanol’s latent heat to generate an intake cooling effect, which lowers the in-cylinder temperature and helps suppress knock. Furthermore, a dual-injector strategy is proposed to balance spatial atomization and rapid fuel transport, which achieves a 66.7% increase in the fuel amount entering the cylinder compared with the original strategy. This configuration effectively resolves the fuel induction lag, achieving stable combustion starting from the first cycle. Full article

Accurate and rapid fault diagnosis is paramount to stabilizing proton exchange membrane fuel cells (PEMFC). To achieve this, this study proposes a novel fault diagnosis method that integrates a convolutional neural network (CNN), a bi-directional long short-term memory network (BiLSTM), and a waveform fault attention (WFA) mechanism. In the proposed framework, data are classified into five distinct categories utilizing a hierarchical clustering algorithm. Additionally, data augmentation techniques are implemented to bolster model performance. The introduction of amplitude attention and temporal difference attention, in conjunction with the construction of WFA, enables the accurate extraction of temporal-spatial features, significantly improving the distinguishability of fault diagnosis. Furthermore, feature contribution is evaluated using permutation feature importance (PFI) to identify key features, enhancing the interpretability of the model. Experimental findings verify that the proposed method enables high-precision fault identification, with precision values spanning 97–100% and an average stability of 98.3%, demonstrating robust performance even when the volume of original sample data is limited. This performance markedly surpasses that of extant methodologies. The comprehensive approach augments the accuracy, reliability, and interpretability of PEMFC fault diagnosis, and introduces a novel research paradigm for feature extraction, thereby possessing significant theoretical and practical application value. Full article

Introduction: Patellofemoral instability (PFI) is most prevalent in adolescents aged 10–17 years, yet disease-specific functional assessment tools validated for pediatric populations are limited. The Banff Patellofemoral Instability Instrument (BPII) 2.0 and the Norwich Patellar Instability (NPI) scores are disease-specific tools that have previously been validated in adults. The purpose of this study was to translate, culturally adapt, and validate the BPII 2.0 and NPI scores for Polish-speaking pediatric patients with PFI. Methods: The Polish versions of the BPII 2.0 and NPI were developed following Beaton’s cross-cultural adaptation guidelines. Patients aged 12–18 years with surgically treated recurrent patellofemoral joint instability completed the BPII 2.0, NPI, Anterior Knee Pain Scale (Kujala), Lysholm Knee Score, and Pedi-IKDC at a clinic visit and again 7–14 days later. The following psychometric properties were assessed: face validity, floor and ceiling effects, test–retest reliability (ICC), internal consistency (Cronbach’s α), and construct validity (Spearman Correlation Coefficients). Results: A total of 57 postoperative patients (19 males, 38 females; median age 16 years, range 12.25–18 years) participated 24–36 months after surgical stabilization. No floor or ceiling effects were observed. The test–retest reliability was excellent (ICC = 0.988 for BPII 2.0 (95% CI 0.977–0.994, p < 0.001); ICC = 0.997 for NPI (95% CI 0.995–0.998, p < 0.001)). Both instruments demonstrated excellent internal consistency (Cronbach’s α = 0.95 for BPII 2.0; α = 0.93 for NPI). The BPII 2.0 showed moderate to strong positive correlations with Lysholm (ρ = 0.69), Kujala (ρ = 0.69), and Pedi-IKDC (ρ = 0.57) and moderate negative correlation with NPI (ρ = −0.62), all of which were statistically significant (p < 0.001). Conclusion: The Polish versions of the BPII 2.0 and NPI scores demonstrated excellent reliability (ICC = 0.988 and 0.997, respectively), internal consistency (Cronbach’s α = 0.95 and 0.93, respectively), and construct validity in Polish-speaking adolescent patients with surgically treated recurrent patellofemoral instability. This is the first validation of the NPI in an exclusively pediatric population. These tools are suitable for clinical assessment and research in this specific population. Limitations include the postoperative-only cohort and absence of structural validity assessment. Full article

Dynamic developmental states of follicles are regarded to be determinants of sexual maturation in fish ovaries. However, it is still a challenge to identify the critical points at which the developmental processes of different types of follicles interact and affect the ovarian development. In this study, four subtypes of the primary follicle (PF) in the ovarian folliculogenesis of zebrafish, i.e., the so-called PF-i, PF-ii, PF-iii, and PF-iv, are first identified by discontinuous NaCl-Percoll gradient centrifugation, as well as their respective morphological features. Then, for the four subtypes of PFs, stage-specific comparative analysis is employed to identify the differentially expressed genes and the differentially methylated regions, which have been validated to be significantly enriched in biological processes encompassing ribosomal biogenesis, meiotic progression, transcriptional regulation, and mitochondrial respiration. Results from transcriptional analysis further demonstrate significant changes in the expression profiles at different developmental stages from the PF-ii to the PF-iii. By molecular biology identification, it is shown that the enhancement of Notch and mTOR pathways can significantly regulate the ovarian development through the pacing effect of primary follicles. Clearly, all these uncovered results could provide a deeper understanding of the initial regulation of ovarian maturation, as well as a new multidisciplinary analytic tool to study follicle candidate regulators in the developmental process of other fish. Full article

Patellofemoral instability (PFI) is a multifactorial orthopedic condition affecting predominantly young and active individuals. Accurate diagnosis and personalized treatment planning remain challenging due to the complex interplay of anatomical and biomechanical factors. Recently, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has gained attention for its role in musculoskeletal imaging and orthopedics care. This review explores the current and potential applications of AI in diagnosis and management of PFI. A total of 11 relevant articles were identified and included in the review. Articles originated from six countries, with China having the most contributions (n = 4), followed by Finland (n = 3), and Korea, Japan, USA and Portugal with 1 each. In the results section, findings are grouped into three themes: (A) Diagnosis, (B) Outcomes and Complications and (C) Challenges, Limitations and Future Directions. The review also discussed advancements in automated image analysis, predictive modeling and outcome prediction. Overall, AI has the potential to improve consistency, efficiency, and personalization of care in patients with PFI, although still requiring technological developments for implementation in daily practice. Existing studies are limited by small datasets, methodological heterogeneity, and lack of external validation. Future research should focus on multicenter data integration, explainable AI frameworks, and clinical validation to enable translation into routine orthopedic practice. Full article

Background/Objectives: Ultra-endurance running (UER) presents unique hydration challenges due to prolonged duration, variable terrain, environmental extremes, and gastrointestinal limitations. Athletes often use either programmed fluid intake (PFI), which prescribes fluid volumes based on estimated sweat rate, or thirst-driven fluid intake (TDFI), which relies on internal cues. This review examines the effectiveness and limitations of each strategy in the context of UER performance and safety. Methods: A narrative review was conducted using a targeted selection of peer-reviewed studies. Both laboratory- and field-based research were included to evaluate the physiological rationale, practical feasibility, and outcomes associated with PFI and TDFI. A total of six studies (five field-based ultra-endurance and one laboratory-based endurance protocols) were included for narrative synthesis. Results: Laboratory trials support PFI for preserving plasma volume, reducing cardiovascular strain, and improving performance in prolonged exercise under controlled conditions. However, real-world ultra-endurance events often involve environmental and logistical challenges that limit the applicability of rigid hydration strategies. Field studies demonstrate that TDFI is safe and effective for many experienced athletes, with no increased incidence of exercise-associated hyponatremia or measurable performance impairment, even with moderate body mass loss. Still, TDFI may underperform in individuals with high sweat rates or impaired thirst perception. Conclusions: Neither strategy seems universally superior. A hybrid model that integrates individual sweat testing, environmental context, and responsiveness to internal cues may offer the most practical and effective hydration approach in ultra-endurance running. Continued research is needed to validate hydration strategies under field conditions and to inform personalized, performance-oriented guidelines. Full article

The aim of this study was to investigate the prognostic and predictive properties of four specific genes in triple-negative breast cancer (TNBC). We focused on ADIPOQ, GAS5, GATA4, and YAP1, which are known for their roles in key molecular pathways related to tumorigenesis, such as adipokine signaling, lncRNA regulation, transcriptional control, and Hippo signaling, but have not been sufficiently explored in the context of epigenetic regulation in breast cancer. Using the methylospecific PCR (MSP) method, we analyzed the methylation of the four genes in the tumor tissues collected from 57 TNBC patients. We evaluated their association with response to neoadjuvant treatment and clinicopathological characteristics. Additionally, we performed a bioinformatic analysis of methylation and expression data from The Cancer Genome Atlas (TCGA) TNBC cohort to explore their relationships with overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), progression-free interval (PFI), and relapse-free survival (RFS). No significant associations were observed between methylation patterns and clinicopathological characteristics in the patients. However, in silico analysis of the TNBC cohort identified ADIPOQ methylation as having the most significant associations, correlating with all five survival endpoints, including OS, DSS, DFI, PFI, and RFS. GAS5 methylation was significantly associated with OS, DSS, and RFS, and GATA4 methylation showed significant associations with PFI, whereas YAP1 methylation was significantly associated with OS and RFS. In addition, GAS5 expression was linked to DSS, DFI and RFS. This study highlights the potential prognostic significance of the epigenetic regulation of ADIPOQ in TNBC. The in silico findings shed light on the molecular pathways associated with TNBC progression and warrant further investigation to validate their role in clinical outcomes and underlying biological mechanisms. Full article

Background/Objectives: Hippocampal atrophy indicates Alzheimer’s disease (AD) progression and guides follow-up and trial enrichment. Identifying high-risk patients is crucial for optimizing care, but accessible, interpretable machine-learning models (ML) are limited. We developed an explainable ML model using clinical data and CSF erythrocyte load (CTRED) to classify adults with AD as high- or low-risk based on hippocampal volume decline. Methods: Included ADNI participants with ≥2 MRIs, baseline lumbar puncture, and vital signs within 6 months of MRI (n = 26). The outcome was the Annual Percentage Change (APC) in hippocampal volume, classified as low or high risk. Predictors were standardized; models included SVM, logistic regression, and Ridge Classifier, tuned and tested on a set (n = 6). Thresholds were based on out-of-fold predictions under a 10–90% positive rate. Explainability used PFI and SHAP for per-patient contributions. Results: All models gave identical classifications, but discrimination varied: Ridge AUC = 1.00, logistic = 0.889, and SVM = 0.667. PFI highlighted MAPres and sex as main signals; CTRED contributed, and age had a minor impact. Conclusions: The explainable ML model with clinical data and CTRED can stratify AD patients by hippocampal atrophy risk, aiding follow-up and vascular assessment planning rather than treatment decisions. Validation in larger cohorts is needed. This is the first ML study to use CSF erythrocyte load to predict hippocampal atrophy risk in AD. Full article