Search Results (80)

The vibration monitoring of scaled models in wind tunnels is critical for aerodynamic testing and structural safety. The abrupt onset of flutter or other aeroelastic instabilities poses a significant risk, necessitating the development of real-time, model-agnostic monitoring systems. This paper proposes a novel, generalized health indicator (HI) based on an improved Fisher Discriminant Analysis (FDA) framework for vibration state classification. The core innovation lies in reformulating the FDA objective function to distinguish between stable and dangerous vibration states, rather than tracking degradation trends. To ensure cross-model applicability, a frequency-wise standardization technique is introduced, normalizing spectral amplitudes based on the statistics of a model’s stable state. Furthermore, a dual-mode entropic regularization term is incorporated into the optimization process. This term balances the dispersion of weights across frequency bands (promoting generalizability and avoiding overfitting to specific frequencies) with the concentration of weights on the most informative resonance frequencies (enhancing the sensitivity to dangerous states). The optimal frequency weights are obtained by solving a regularized generalized eigenvalue problem, and the resulting HI is the weighted sum of the standardized frequency amplitudes. The method is validated using simulated spectral data and flight data from a wind tunnel test, demonstrating a superior performance in the early detection of dangerous vibrations and the clear interpretability of critical frequency bands. Comparisons with traditional sparse measures and machine-learning methods highlight the proposed method’s advantages in trendability, robustness, and unique capability for cross-model adaptation. Full article

Artificial intelligence (AI) is profoundly transforming medicine and healthcare, evolving from analytical tools aimed at automating specific tasks to integrated components of complex socio-technical systems. This work presents a conceptual and theoretical review proposing the Model–Context–Relation (M–C–R) framework to interpret how the effectiveness of Artificial Intelligence (AI) in medicine and healthcare emerges from the dynamic alignment among algorithmic, contextual, and relational dimensions. No new patient-level data were generated or analyzed. Through a qualitative conceptual framework analysis, the study integrates theoretical, regulatory, and applicative perspectives, drawing on the Revision of the Semiological Paradigm developed by the Palermo School, as well as on major international guidelines (WHO, European AI Act, FDA). The results indicate that AI-supported processes have been reported in the literature to improve clinical accuracy and workflow efficiency when appropriately integrated, yet its value depends on contextual adaptation and human supervision rather than on algorithmic performance alone. When properly integrated, AI functions as a digital semiotic extension of clinical reasoning and may enhance the physician’s interpretative capacity without replacing it. The M–C–R framework enables understanding of how performance, ethical reliability, and organizational sustainability emerge from the alignment between the technical model, the context of use, and relational trust. In this perspective, AI is conceptualized not as a decision-maker but as an adaptive cognitive partner, fostering a reflective, transparent, and person-centered medicine. The proposed approach supports the design of sustainable and ethically responsible AI systems within a Medicine of Complexity, in which human and artificial intelligence co-evolve to strengthen knowledge, accountability, and equity in healthcare systems. Full article

Therapeutic strategies targeting “tumor-educated platelets” (TEPs) and platelet–tumor interactions by key signaling pathways (ITAM, P2Y12) may reduce metastasis and cancer. Using a TEP gene expression dataset originally created to study swarm intelligence-enhanced detection of lung cancer cells (GSE89843), we did perform extensive transcriptome analysis to integrate these data with directed protein–protein interactions and build a TEP-specific signaling network. We analyze network topology and controllability and identify critical and indispensable nodes, as well as high-weight, usually high-score nodes. We reconstruct (pharmacological) controllable subnetworks of TEP signaling, which we then explore for drugs targets. We found 111 upregulated and 108 downregulated genes compared to control platelets, enriched in pathways related to extracellular matrix interactions, cytoskeleton organization, immune signaling, and platelet activation. Ribosomal function, apoptosis, and immune signaling were among the downregulated processes, highlighting unique TEP profiles in non-small-cell lung cancer (NSCLC). Our integrative analysis of TEPs in NSCLC reveals key transcriptional and network-based alterations harmful for the cancer patient. Using four complementary strategies, we identified five high-confidence genes (Gene symbols always given throughout the paper), ITGA2B, FLNA, GRB2, FCGR2A, and APP, as central to TEP signaling. These can be targeted by FDA-approved drugs. Fostamatinib, an SYK inhibitor, emerged as the top candidate drug to disrupt ITAM-mediated platelet activation selectively; metastasis-promoting metalloprotease and cytoskeletal targets influencing adhesion were also identified. A low-dose combination therapy of fostamatinib, Aducanumab, and acetylsalicylic acid (aspirin) may control TEP effects. In conclusion, our preclinical in silico approach revealed FDA-approved drugs that allow therapeutic targeting of metastasis-promoting TEPs and target NSCLC at the same time. Full article

Cosmic demographics—the statistical study of populations of astrophysical objects—has long relied on tools from multivariate statistics for analyzing data comprising fixed-length vectors of properties of objects, as might be compiled in a tabular astronomical catalog (say, with sky coordinates, and brightness measurements in a fixed number of spectral passbands). But beginning with the emergence of automated digital sky surveys, ca. 2000, astronomers began producing large collections of data with more complex structures: light curves (brightness time series) and spectra (brightness vs. wavelength). These comprise what statisticians call functional data—measurements of populations of functions. Upcoming automated sky surveys will soon provide astronomers with a flood of functional data. New methods are needed to accurately and optimally analyze large ensembles of light curves and spectra, accumulating information both along individual measured functions and across a population of such functions. Functional data analysis (FDA) provides tools for statistical modeling of functional data. Astronomical data presents several challenges for FDA methodology, e.g., sparse, irregular, and asynchronous sampling, and heteroscedastic measurement error. Bayesian FDA uses hierarchical Bayesian models for function populations, and is well suited to addressing these challenges. We provide an overview of astronomical functional data and some key Bayesian FDA modeling approaches, including functional mixed effects models, and stochastic process models. We briefly describe a Bayesian FDA framework combining FDA and machine learning methods to build low-dimensional parametric models for galaxy spectra. Full article

Additive manufacturing via selective laser sintering (SLS) enables the rapid production of geometrically complex polyamide 12 (PA12) components. However, conventional pointwise analysis techniques often overlook the full depth of continuous experimental datasets, thus limiting the interpretation of structure–function relationships that are essential to high-performance design. This study employs functional data analysis (FDA) to elucidate the microstructural, chemical, thermal, and mechanical behaviours of SLS-fabricated PA12, focusing on the effects of build orientation (horizontal, transverse, vertical) and wall thickness (2.0–3.0 mm). The samples were produced via a commercial SLS platform and characterised via X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and tensile testing. The FDA was applied to raw, normalised, and first derivative datasets via Python’s Scikit-FDA package, increasing the sensitivity to latent material variations. The findings demonstrate that the build orientation has a marked influence on the crystallinity and mechanical performance: horizontal builds yield narrower gamma-phase XRD peaks, greater structural order, and enhanced tensile properties, whereas vertical builds exhibit broader peak dispersion and greater thermal sensitivity. The wall thickness effects were minor, with only isolated flux-related anomalies. The FTIR spectra confirmed the consistent chemical stability across all the conditions. The FDA successfully identified subtle transitions and anisotropies that eluded traditional methods, underscoring its methodological strength for advanced polymer characterisation. These insights offer practical guidance for refining SLS process parameters and improving predictive design strategies in polymer-based additive manufacturing. Full article

Camelina (Camelina sativa) is an oilseed crop of increasing importance, valued not only for its adaptability to diverse environmental conditions and potential for sustainable agriculture but also for its economic advantages, including low input requirements and suitability for biofuel production and niche markets. This study examines the relationship between camelina yield and climatic variables—specifically temperature and precipitation—based on a ten-year field experiment conducted in Poland. To capture the temporal dynamics of weather conditions, Functional Data Analysis (FDA) was applied to daily temperature and precipitation data. The analysis revealed that yield variability was strongly influenced by the length of the vegetative period and specific weather patterns in April and July. Higher yields were recorded in years characterized by moderate spring temperatures, elevated temperatures in July, and evenly distributed rainfall during the early generative growth stages. The Maximal Information Coefficient ($\mathrm{M}\mathrm{I}\mathrm{C}$) confirmed the relevance of these variables, with the duration of the vegetative phase showing the strongest correlation with yield. Cluster analysis further distinguished high- and low-yield years based on functional weather profiles. The FDA-based approach provided clear, interpretable insights into climate–yield interactions and demonstrated greater effectiveness than traditional regression models in capturing complex, time-dependent relationships. These findings enhance our understanding of camelina’s response to climatic variability and support the development of predictive tools for resilient, climate-smart crop management. Full article

Objectives. The present study aimed to quantify changes in spinal kinematics during Nordic walking compared to regular walking (RW) for 60 s on a training path among physically fit young males (n = 20, aged 19–22 years). Methods. Two walking speeds were analyzed: 3 km/h and 5 km/h. The experimental setup was designed to assess spinal angular rotations using five kinematic parameters: upper spine, lower spine, thoracic region, lumbar region, and pelvis. Results. The data were acquired from 9 compact inertial sensors and the following motion analysis is carried out using 3D MioMotion IMU sensor’s analysis system. The differences in the obtained cyclic biomechanical parameters were detected using functional data analysis (FDA) statistical tests. Conclusions. The key finding of the study is that Nordic walking significantly alters the angular kinematic pattern of spinal movement as it revealed significant differences in all five measured parameters when compared to normal walking. Notably, the most pronounced changes were observed in the upper spine and pelvis motion. Additionally, Nordic walking increased stance phase duration and velocity: (i) significantly increased the duration of the stance phase in all three planes of motion; (ii) significantly increased the velocity during the stance phase across all three planes. These reported findings highlight the biomechanical, preventive, therapeutic, and rehabilitative potential of Nordic walking. Full article

Background: Despite clinical and pathological risk tools, predicting outcomes in non-muscle-invasive bladder cancer (NMIBC), particularly high-grade (HG) cases, remains challenging due to its unpredictable recurrence and progression. There is an urgent need for molecular biomarkers to enhance risk stratification and guide treatment. Methods: We assessed the prognostic potential of eight miRNAs (miR-9, miR-143, miR-182, miR-205, miR-27a, miR-369, let-7c, and let-7g) in a cohort of ninety patients with primary bladder cancer. Expression data were retrieved from our previously published studies. Kaplan–Meier’s and Cox’s regression analyses were used to evaluate the associations with overall survival (OS), metastasis-free survival (MFS), and clinical outcomes. Principal component analysis (PCA) was performed to identify informative miRNA combinations. Target gene prediction, pathway enrichment (DAVID), and drug–gene interaction mapping (DGIdb) were conducted in silico. Results: A high expression of let-7g and miR-9 was significantly associated with better OS in HG NMIBC and MIBC, respectively (p = 0.013 and p = 0.000). MiR-9 downregulation correlated with metastasis in MIBC (p = 0.018). Among all combinations, miR-205 and miR-27a best predicted intermediate-risk NMIBC progression and recurrence (r² = 0.982, p = 0.000). A functional analysis revealed that these miRNAs regulate key cancer-related pathways (MAPK, mTOR, and p53) through genes such as TP53, PTEN, and CDKN1A. Drug interaction mapping identified nine target genes (e.g., DAPK1, ATR, and MTR) associated with eight FDA-approved bladder cancer therapies, including cisplatin and gemcitabine. Conclusions: Let-7g, miR-9, miR-143, miR-182, and miR-205 emerged as promising biomarkers for outcome prediction in NMIBC. Their integration into liquid biopsy platforms could support non-invasive monitoring and personalized treatment strategies. These findings warrant validation in larger, prospective studies and through functional assays. Full article

We propose a novel deep learning framework for counterfactual inference on the COMPAS dataset, utilizing a multi-task CNN-LSTM architecture. The model jointly predicts multiple outcome types: (i) count outcomes with zero inflation, modeled using zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), and negative binomial (NB) distributions; (ii) time-to-event outcomes, modeled via the Cox proportional hazards model. To effectively leverage the structure in high-dimensional tabular data, we integrate functional data analysis (FDA) techniques by transforming covariates into smooth functional representations using B-spline basis expansions. Specifically, we construct a pseudo-temporal index over predictor variables and fit basis expansions to each subject’s feature vector, yielding a low-dimensional set of coefficients that preserve smooth variation while reducing noise. This functional representation enables the CNN-LSTM model to capture both local and global temporal patterns in the data, including treatment-covariate interactions. Our approach estimates both population-average and individual-level treatment effects (ATE and CATE) for each outcome and evaluates predictive performance using metrics such as Poisson deviance, root mean squared error (RMSE), and the concordance index (C-index). Statistical inference on treatment effects is supported via bootstrap-based confidence intervals and hypothesis testing. Overall, this comprehensive framework facilitates flexible modeling of heterogeneous treatment effects in structured, high-dimensional data, advancing causal inference methodologies in criminal justice and related domains. Full article

Project delays remain a persistent challenge in the construction industry, having significant financial implications and contributing to disputes between project participants. Forensic Delay Analysis (FDA) has emerged as a specialised function that identifies the root causes of such delays, quantifies their duration, and assigns responsibility to the appropriate parties. While FDA is a widely practised process, it has yet to fully exploit the potential of emerging technologies. This study explores the integration of both existing and emerging technologies for enhancing FDA processes. A Design Science Research (DSR) approach is adopted, with data collection methods that involve the use of the literature, archival materials, case studies and survey methods. The research demonstrates how the use of technologies, such as database management systems (DBMSs), building information modelling (BIM), artificial intelligence (AI) and games engines, can improve the analytical efficiency, data management, and presentation of findings through a case study. The study showcases the transformative potential of these interventions in streamlining FDA processes, ultimately leading to more accurate and efficient resolution of construction disputes. The proposed process is exemplified by the development of a prototype: the Forensic Information Modelling Visualiser (FIMViz). The FIMViz is a practical tool that has received positive evaluation by FDA experts. The prototype and the enhanced FDA process model that underpins it demonstrate significant advancement in FDA practices, promoting improved decision-making and collaboration between project participants. Further development is needed, but the results could ultimately streamline the FDA process and minimise the uncertainties in FDA outcomes, thus reducing the incidence of costly disputes to the wider economic benefit of the industry generally. Full article

Adrenocortical carcinoma (ACC) is an aggressive cancer with a poor prognosis. Mitotane, the only FDA-approved treatment for ACC, targets adrenocortical cells and reduces cortisol levels. Although it remains the cornerstone of systemic therapy, its overall impact on long-term outcomes is still a matter of ongoing clinical debate. Drug repurposing is a cost-effective way to identify new therapies, and defactinib, currently in clinical trials as part of combination therapies for various solid tumours, may enhance ACC treatment. We aimed to assess its efficacy in combination with mitotane. We tested the combination of mitotane and defactinib in H295R, SW13, and mitotane-sensitive and -resistant HAC15 cells, using functional assays, transcriptomic profiling, 2D and 3D cultures, bioprinted tissues, and xenografts. We assessed drug interactions with NMR and toxicity in vivo, as mitotane and defactinib have never been previously administered together. Genomic data from 228 human ACC and 158 normal adrenal samples were also analysed. Transcriptomic analysis revealed dysregulation of focal adhesion along with mitotane-related pathways. Focal adhesion kinase (FAK) signalling was enhanced in ACC compared to normal adrenal glands, with PTK2 (encoding FAK) upregulated in 44% of tumour samples due to copy number alterations. High FAK signature scores correlated with worse survival outcomes. FAK inhibition by defactinib, both alone and in combination with mitotane, showed effective anti-tumour activity in vitro. No toxicity or drug—drug interactions were observed in vivo. Combination treatment significantly reduced tumour volume and the number of macrometastases compared to those in the mitotane and control groups, with defactinib-treated tumours showing increased necrosis in xenografts. Defactinib combined with conventionally used mitotane shows promise as a novel combination therapy for ACC and warrants further investigation. Full article

This observational pilot study investigated neurocardiovascular responses to an active stand test using continuous physiological monitoring and functional data analysis (FDA) in older women. A sample of 25 community-dwelling female adults aged 59–78 years (mean age: 70.3 years) participated. Participants were dichotomized into comparison groups based on five factors: age (<70 vs. ≥70 years); the presence of initial orthostatic hypotension (IOH, yes/no); body mass index (BMI < 25 vs. ≥25 kg/m²); antihypertensive medication use (yes/no); and physical frailty status assessed by the Survey of Health, Ageing and Retirement in Europe—Frailty Instrument (SHARE-FI score < −0.5 vs. ≥−0.5). Each participant completed an active stand test during which six physiological signals were continuously recorded: systolic (sBP) and diastolic (dBP) blood pressure and heart rate (HR) via digital artery photoplethysmography and left frontal oxygenated hemoglobin (O₂Hb), deoxygenated hemoglobin (HHb), and tissue saturation index (TSI) via near-infrared spectroscopy (NIRS). The signal analysis focused on a standardized 200 s window spanning 50 s before to 150 s after the stand, with all signals resampled and synchronized at 5 Hz. FDA was used to statistically compare the full time series between groups for each signal. Group-level differences revealed that younger participants (<70 years) exhibited significantly higher HR in multiple periods following the stand (~10 s, ~30 s, ~90 s, and ~140 s post-stand) compared to their older counterparts. Participants with IOH demonstrated significantly lower sBP at ~10 s, ~80 s, and ~130 s post-stand and lower dBP at ~10 s post-stand. Among participants classified as overweight/obese (BMI ≥ 25 kg/m²), significantly lower levels of HHb were observed at ~10 s, ~30–50 s, and ~60 s post-stand, while O₂Hb levels were reduced at ~50 s, ~60 s, ~70–110 s, ~130 s, and ~140 s post-stand. No statistically significant group-level differences were observed based on antihypertensive medication use or frailty status. These findings demonstrate the utility of FDA in detecting subtle, time-dependent physiological variations during orthostatic challenge and underscore the value of continuous neurocardiovascular monitoring in assessing orthostatic tolerance in aging populations. Full article

Traditional oncological therapies have contributed to reducing the global cancer burden; however, they have not achieved complete eradication, nor have they effectively prevented relapses, minimized toxicity, or preserved immune function. Recent advances, particularly the introduction of immune checkpoint inhibitors (ICIs) and CAR-T cell therapies, have markedly improved clinical outcomes and overall survival in certain cancer subtypes. Nevertheless, response rates remain suboptimal, and adverse immunological events are frequent. This review starts by highlighting the FDA-approved ICIs currently utilized in cancer immunotherapy, emphasizing those that have demonstrated clinical efficacy in recent years. The true focus of our analysis is on the latest clinical applications of near-infrared photoimmunotherapy (NIR-PIT). This emerging modality is evaluated in patients with head and neck cancers (HNC), particularly in cases that are unresectable, locally advanced, or recurrent. Finally, the review explores the current landscape and prospects of NIR-PIT, considering its potential to enhance therapeutic efficacy and extend relapse-free survival. Photoimmunotherapy is a promising, molecularly targeted option for patients with limited prognosis, offering new hope where conventional therapies fail. By synthesizing recent clinical trial data, this work highlights how NIR-PIT could bridge the translational gap between preclinical research and clinical practice. The integration of advanced technologies and interdisciplinary collaboration among researchers, clinicians, and technologists will be critical in optimizing NIR-PIT, improving its accuracy, efficacy, and safety, and ultimately advancing standards of cancer care and patient survival. Full article

In this work, Functional Data Analysis (FDA) is used to detect behavioral patterns in the Bolsa de Valores de Colombia (BVC) in reaction to the global crises caused by COVID-19 and the war in Ukraine. The oil price fluctuation curve is considered a covariate. The FDA’s distinctive ability is to represent stock values as smooth curves that evolve over time and provide new insights into the dynamics of the BVC. The methodology makes use of functional multivariate techniques applied to the smoothed curves of the closing prices of the main stocks of the BVC. The results show that the correlations of the oil curve with the average market curve change from almost null or low in the global period to extremely significant in time windows immediately after the beginnings of COVID-19 and the war in Ukraine, respectively. On the other hand, the velocity curves, which are used to evaluate the stock market volatility, show a pattern of synchronization of companies in the crisis periods. Furthermore, in these crisis periods, the companies in BVC showed a high synchronization with the Brent crude oil price. In conclusion, this work shows the usefulness of the FDA as a complement to time series analysis in the study of stock markets. The results of this research could be of interest to academic researchers, financial analysts, or institutions. Full article

Background/Objectives: Rhabdomyolysis, a dangerous breakdown of skeletal muscle, has been reported as an adverse event in those prescribed a statin therapy for the treatment of hypercholesterolemia. Statin drugs are some of the most prescribed treatments for elevated cholesterol levels. The purpose of this comparative study was to determine the association between the statin drugs used and the risk of rhabdomyolysis using the FDA Adverse Event Reporting System (FAERS) and transcriptomic data. Methods: A disproportionality analysis was performed to compare the risk of rhabdomyolysis between the reference statin drug (simvastatin) and the treatment group, with patient age assessed as a possible confounder. In addition, association rule mining was utilized to both identify other adverse events that frequently presented with rhabdomyolysis and identify possible drug-drug interactions (DDIs). Finally, public transcriptomic data were explored to identify the possible genetic underpinnings highlighting these differences in rhabdomyolysis risk across statins. Results: Rhabdomyolysis is a commonly reported adverse event for patients treated with statins, particularly those prescribed simvastatin. Simvastatin was associated with a more than 2-fold increased likelihood of rhabdomyolysis compared to other statins. Men were twice as likely to report rhabdomyolysis than women regardless of statin treatment, with the highest risk observed for pravastatin (ROR = 2.30, p < 0.001) and atorvastatin (ROR = 2.03, p < 0.0001). Several possible DDIs were identified, including furosemide/Lasix, allopurinol clopidogrel/Plavix, and pantoprazole, which may elevate rhabdomyolysis risk through impaired muscle function and delayed statin metabolism. Finally, nine myopathic genes were identified as possible regulators of statin-induced rhabdomyolysis, including DYSF, DES, PLEC, CAPN3, SCN4A, TNNT1, SDHA, MYH7, and PYGM in primary human muscle cells. Conclusions: Simvastatin was associated with the highest risk of rhabdomyolysis. The risk of rhabdomyolysis was more pronounced in men than women. Several possible DDIs were identified including furosemide/Lasix, allopurinol clopidogrel/Plavix, and pantoprazole. Full article