Search Results (207)

In real life, almost all systems are nonlinear in nature. The electromagnetic levitation system (EMLS) is one such system that has a wide range of applications due to its frictionless, fast, and affordable technique. Optimal control and sliding mode control (SMC) techniques are often used controllers for EMLS. However, these techniques can achieve the required levitation but lag in having perfect set-point tracking and robustness against uncertainties. To get over these drawbacks, this article proposes the design of unconstrained mismatched uncertainties, constrained mismatched uncertainties, and integral sliding mode control with mismatched uncertainties for the current-controlled-type electromagnetic levitation system (CC-EMLS). The modeled equations of CC-EMLS are transfomed in terms of the mismatched uncertainties, and the required control action is obtained with and without constraints on the control input. The quadratic performance function is suggested for the unconstrained control scheme and is solved using the Hamilton–Jacobi–Bellman (HJB) equation. The non-quadratic cost function is designed for the constrained control method, and the HJB equation is utilized to obtain the solution. Both control schemes provide robustness to the system, but deviations in the set point are observed in tracking the position of the ball when the changes in the payload occur in the system. Therefore, integral sliding mode control with robust–optimal (IOSMC) gain is proposed for the CC-EMLS to overcome the steady-state error in the other two schemes. The stability is proven using the direct method of Lyapunov stability. The essential studies based on the simulation are carried out to showcase the performance of the proposed control schemes. The integral performance indicators are compared for all three proposed control schemes to highlight the efficacy, robustness, and efficiency of the designed controllers. Full article

Interior space quality in certified office buildings is key in supporting the health and well-being of occupants. Daylight, which regulates the human circadian rhythm and affects physiological processes and productivity, is crucial. This study’s objective was to determine how a building’s architecture and selected elements of its interior such as partitions and finishing material parameters affect sunlight distribution in workspaces and its biological effectiveness, as measured using Equivalent Melanopic Lux (EML). The environment’s impact on the non-visual potential of a space was also assessed (in relation to the M/P ratio). To achieve these objectives, we used a 3D model of an office building floor to simulate natural lighting in various configurations, for a cool temperate climate using Solemma’s ALFA 2025 software. This research was conducted using simulations only, with no in situ measurements. The study assessed melanopic light intensity for specific zones and workstation groups. The impact of ceiling colors and the five colors given to partitions of different heights located between desks was also determined. The study evaluated the relationship between photopic and melanopic intensity and found that, as the height of the partitions increased, especially with cloudy skies, the importance of these planes’ colors increased. Blues had a positive effect on the space’s non-visual potential, while oranges showed significant decreases in EML relative to lux, by up to 25%. This research underscores the importance of light’s non-visual impact and the consideration of these aspects at every design stage, especially interior design, to provide a comfortable work environment and its long-term benefits. We also proposed natural light exposition optimization strategies that can support proper circadian rhythm. Full article

Identifying item–trait relationships is a core task in multidimensional item response theory (MIRT). Common empirical approaches include exploratory item factor analysis (EIFA) with rotations, the expectation maximization-based $L_1$ regularization (EML1) algorithm, and the expectation model selection (EMS) algorithm. While these methods typically assume multivariate normality of latent traits, empirical data often deviate from this assumption. This study evaluates the robustness of EIFA, EML1, and EMS, when latent traits violate normality assumptions. Using the independent generator transform, we generate latent variables under varying levels of skewness, excess kurtosis, numbers of non-normal dimensions, and inter-factor correlations. We then assess the performance of each method in terms of the F1-score for identifying item–trait relationships and mean squared error (MSE) of parameter estimations. The results indicate that non-normality leads to a reduction in F1-score and an increase in MSE generally. For F1-score, EMS performs best with small samples (e.g., $N=500$), whereas EIFA with rotations yields the highest F1-score in larger samples. In terms of estimation accuracy, EMS and EML1 generally yield lower MSEs than EIFA. The effects of non-normality are also demonstrated by applying these methods to a real data set from the Depression, Anxiety, and Stress Scale. Full article

Anaplastic lymphoma kinase (ALK), a member of the receptor tyrosine kinase family, plays a central oncogenic role in the initiation and progression of diverse malignancies. Aberrant ALK activation generally results from structural alterations or dysregulated expression, leading to persistent activation of downstream signaling pathways that drive tumor cell proliferation, survival, and metastasis. ALK gene abnormalities predominantly encompass fusions, point mutations, and amplifications, with EML4-ALK-positive non–small cell lung cancer representing a canonical example. The advent of ALK-targeted inhibitors has constituted a major therapeutic milestone for ALK-positive tumors. From first-generation Crizotinib to third-generation Lorlatinib, successive agents have been refined for target selectivity, central nervous system penetration, and coverage of resistance-associated mutations, substantially improving patient survival and intracranial disease control. Nonetheless, the emergence of acquired resistance remains an overarching challenge, mediated by secondary kinase domain mutations, activation of bypass signaling pathways, and tumor phenotypic transformation. This review presents an integrative synthesis of ALK-targeted therapeutic developments, elucidates underlying resistance mechanisms, and surveys emerging strategies, providing a comprehensive perspective on current advances and future directions in precision management of ALK-driven malignancies. Full article

Background/Objectives: Lung cancer remains among the most frequently diagnosed malignancies in Romania, with a high mortality rate. Beyond EGFR mutations, clinically relevant genetic alterations in non-small cell lung cancer (NSCLC) include fusions involving ALK, ROS1, RET, and NTRK1/2/3. This study aimed to determine the prevalence of these mutations in a Romanian cohort and evaluate their associations with clinicopathological features. Methods: DNA and RNA were simultaneously extracted from formalin-fixed, paraffin-embedded (FFPE) tissue sections using the Genexus Purification System (ThermoFisher Scientific). Concentrations were quantified fluorometrically, and gene fusions were analyzed with Ion Torrent NGS (Ion GeneStudio S5) with the Oncomine Focus Assay (ThermoFisher Scientific). Library preparation was automated with the Ion Chef System, and data interpretation was conducted using Ion Reporter. Results: Among 721 newly diagnosed NSCLC patients, 28 (3.88%) harbored gene fusions. Adenocarcinoma prevailed among fusion-positive cases (85.7%). The subgroup included 15 males and 13 females, with a mean age of 63.25 years (range 43–83). ALK fusions were most frequent (1.66% of the cohort; 42.86% of positives), predominantly EML4::ALK. ROS1 fusions were detected in five patients (0.7%), most frequently CD74::ROS1. RET fusions occurred in 1.11%. Rare fusions included one ETV6::NTRK3, one PTPRZ1::MET, and one FGFR3::TACC3 co-occurring with EGFR L858R. Conclusions: Gene fusions were present in a minority of NSCLC cases, with ALK, ROS1, and RET being the most clinically relevant. These alterations were mutually exclusive with common drivers such as EGFR or KRAS. Detection of rare fusions highlights the therapeutic potential of comprehensive NGS profiling in Romanian NSCLC patients. Full article

With the rapid deployment of artificial intelligence (AI) data centers, demand for optical modules surges—alongside faster upgrades and stricter low-power requirements. However, traditional optical driver integrated circuits (ICs) rely on device-specific customization, which lengthens driver design cycles, delays module deployment, and raises costs, becoming a bottleneck for optical module evolution. To address these issues, this work proposes a unified optical transmitter electronic integrated circuit (EIC) design approach featuring synergistic driver-laser/modulator co-design and a versatile output driver (VOD). The VOD can be configured into three output impedance states (open-drain, differential 50-$\Omega$, or differential 100-$\Omega$), enabling it to drive various optical devices like distributed feedback lasers (DFBs), vertical-cavity surface-emitting lasers (VCSELs), electro-absorption modulated lasers (EMLs), and Mach-Zehnder modulators (MZMs) with a single design, minimizing device-specific customization. Meanwhile, its power consumption is also adjustable to maximize the power efficiency. The proposed design approach demonstrates the potential to address the critical interoperability, cost, and power challenges faced by AI data centers, providing a scalable template for next-generation coherent and 4-level pulse amplitude modulation systems and facilitating rapid deployment. Full article

This single case study examines the implementation of a co-designed fifth-grade science unit enhanced by using Virtual Reality (VR) and integrating translingual and transmodal scaffolding strategies to support students’ participation and quality of talk during scientific sensemaking. The co-designed science unit covered physical and chemical changes as part of the fifth-grade science curriculum. The research involves a fifth-grade science teacher and her class of 22 students comprising multilingual learners (ML) and English monolingual learners (EML). This study examines the learning experience of 3 student pairs grouped as ML-ML, EML-ML and EML-EML. Using content analysis, we analyzed 911 min of video data on the six students’ learning in this unit. The results indicate that when the teacher used translingual and transmodal scaffolding strategies introduced during the co-design process, equal participation across MLs and EMLs was observed. The VR pair programming worked well for student pairs in increasing active participation regardless of the pairing, although active participation did not necessarily lead to high quality science talk. Findings of this study provide implications and recommendations for leveraging the scaffolding from teachers, materials, and VR pair programing activity to support the equal participation and quality of talk among all learners during scientific sensemaking. Full article

This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO₂/m³ reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies. Full article

Levitation techniques reduce the available heterogeneous nucleation sites and provide stable access to deeply undercooled melts. However, some samples have repeatably demonstrated that, in the presence of strong stirring, solidification may be induced at moderate, sub-critical undercoolings. Dynamic nucleation is a mechanism by which solidification may be induced through flow effects within a sub-critically undercooled melt. In this mechanism, collapsing cavities within the melt produce very high-pressure shocks, which shift the local melting temperature. In these regions of locally shifted melt temperatures, thermodynamic conditions enable nuclei to grow and trigger solidification of the full sample. By deepening the local undercooling, dynamic nucleation enables solidification to occur in conditions where classical nucleation does not. Dynamic nucleation has been observed in several zirconium and zirconium-based samples in the Electromagnetic Levitator onboard the International Space Station (ISS-EML). The experiments presented here address conditions in which a zirconium sample alloyed with 2.5 atomic percent niobium spontaneously solidifies during electromagnetic levitation experiments with strong melt stirring. In these experimental conditions, classical nucleation predicts the sample to remain liquid. This solidification behavior is consistent with the solidification behavior observed in prior experiments on pure zirconium. Full article

This paper reinforces the previously proposed moving linear (ML) model approach for time series analysis by introducing theoretically grounded enhancements. The ML model flexibly decomposes a time series into constrained and remaining components, enabling the extraction of trends and fluctuations with minimal structural assumptions. Building on this framework, we present two key improvements. First, we develop a theoretically justified evaluation criterion that facilitates coherent estimation of model parameters, particularly the width of the time interval. Second, we enhance the extended ML (EML) model by introducing a new outlier detection and estimation method that identifies both the number and locations of outliers by maximizing the reduction in AIC. Unlike the earlier version, the reinforced EML model simultaneously estimates outlier effects and improves model fit within a unified, likelihood-based framework. Empirical applications to economic time series illustrate the method’s superior ability to detect meaningful anomalies and produce stable, interpretable decompositions. These contributions offer a generalizable and theoretically supported approach to modeling nonstationary time series with structural disturbances. Full article

Background: Comprehensive molecular profiling is essential for precision oncology in non-small cell lung cancer (NSCLC). However, genomic data from Eastern European populations, including Romania, remain limited. Methods: We analyzed 398 consecutive NSCLC cases tested at the PATHOS Molecular Pathology Laboratory (Cluj-Napoca, Romania) between April 2024 and February 2025 using the Ion Torrent™ Genexus™ System and the Oncomine™ Dx Target Test, which evaluates SNVs/indels in 46 genes, fusions in 23 genes, and CNVs in 19 genes from FFPE samples. Results: The cohort was predominantly male (66%) with a median age of 67 years. Adenocarcinoma represented 70% of cases with known histology. Genomic profiling revealed a high frequency of actionable alterations. KRAS mutations were the most common (29.1%), with p.G12C detected in 10.3% of all the cases. EGFR mutations were present in 14.3% of patients, mostly exon 19 deletions and L858R substitutions. BRAF alterations (5.3%) included both V600E and non-V600E variants. RET alterations were detected as eight missense mutations, two canonical fusions (KIF5B–RET, CCDC6–RET), one amplification, and three transcript imbalances. EML4-ALK fusions (1.77%), ERBB2 mutations/amplifications (3.0%), and FGFR1/FGFR3 amplifications were also observed. Conclusions: This study provides the first large-scale molecular snapshot of NSCLC in Romania. While the overall genomic profiles align with Western populations, the higher frequency of KRAS p.G12C and FGFR amplifications highlights the value of region-specific data to support targeted therapies in Eastern Europe. Full article

Background: Medicines affect the environment throughout their lifecycle, from production and distribution to use and disposal. They contribute to the pollution of air, water, and soil, impacting ecosystems and human health. Recognizing these risks, regulatory bodies and organizations have highlighted pharmaceutical pollution as a global concern, emphasizing the need for environmental risk assessments and sustainable practices. Methods: This study reviewed the essential medicines lists (EMLs) from 158 countries and examined the available ecotoxicological data. Medicines with high bioaccumulation, persistence, and toxicity were identified and cross-referenced with their inclusion in EMLs. Additionally, we analyzed the presence of alternative medicines with similar therapeutic effects but potentially lower environmental risks. Results: Five medicines—ciprofloxacin, ethinylestradiol, levonorgestrel, ibuprofen, and sertraline—were selected as illustrative examples due to their high environmental persistence and toxicity. All were listed in the 2023 WHO model list, with ciprofloxacin appearing in 94.3% of national EMLs. Conclusions: This study underscores the limited availability of ecotoxicological data, which hinders environmental risk assessment for medicines. EMLs could serve as a tool to enhance the awareness and data mobilization of pharmaceutical pollution. Incorporating environmental criteria into EMLs could support more sustainable medicine selection and regulatory practices. Full article

Ovarian tissue is critical for goose reproduction. This study aimed to investigate gene regulation by DNA methylation in relation to the reproductive traits of geese. We performed whole-genome bisulfite sequencing (WGBS) on ovarian tissues from Sichuan white geese (high-laying-rate group: HLRG, ♀ = 3; low-laying-rate group: LLRG, ♀ = 3) during the laying period. The results showed a higher level of hypermethylated differentially methylated regions (DMRs) in the HLRG, indicating a higher overall methylation level compared to the LLRG. In total, we identified 2831 DMRs and 733 differentially methylated genes (DMGs), including 363 genes with upregulated methylation. These DMGs were significantly enriched in pathways related to microtubule function (GO:0005874; GO:0000226), GnRH secretion, thyroid hormone signaling, ECM-receptor interaction, and PI3K–Akt signaling. Integration with RNA-seq data identified eight overlapping genes between DMGs and differentially expressed genes (DEGs), with five genes (CUL9, MEGF6, EML6, SYNE2, AK1BA) exhibiting a correlation between hypomethylation and high expression. EML6, in particular, emerged as a promising candidate, potentially regulating follicle growth and development in Sichuan white geese. Future studies should focus on further verifying the role of the EML6 gene. In conclusion, this study provides important insights into the regulatory mechanisms of DNA methylation influencing reproductive traits in geese, offering novel candidate markers for future goose breeding programs. Full article

With the rapid growth of data center demand driven by AI, high-speed optical modules (such as 800G and 1.6T) have become critical components. Traditional 800G modules face issues such as complex processes and large sizes due to the separate packaging of EML chips, AlN substrates, and capacitors. This study proposes a high-speed EML module based on silicon integration, where resistors, capacitors, and AuSn soldering areas are integrated onto the silicon substrate, enabling the bonding of the EML chip, reducing packaging costs, and enhancing scalability. Key achievements include: the development of a 100G EML chip; the fabrication of a high-speed silicon integrated carrier; successful Chip-on-Carrier (COC) packaging and testing, with a laser output power of 10 mW, extinction ratio of 10 dB, and bandwidth greater than 40 GHz; and reliability verified through 500 h of aging tests. This study provides an expandable solution for next-generation high-speed optical interconnects. Full article

Background/Objectives: This study presents a novel double-weighted Bayesian Ensemble Machine Learning (DW-EML) model aimed at improving the classification and prediction of metabolomics data. This discipline, which involves the comprehensive analysis of metabolites in a biological system, provides valuable insights into complex biological processes and disease states. As metabolomics assumes an increasingly prominent role in the diagnosis of human diseases and in precision medicine, there is a pressing need for more robust artificial intelligence tools that can offer enhanced reliability and accuracy in medical applications. The proposed DW-EML model addresses this by integrating multiple classifiers within a double-weighted voting scheme, which assigns weights based on the cross-validation accuracy and classification confidence, ensuring a more reliable prediction framework. Methods: The model was applied to publicly available datasets derived from studies on critical illness in children, chronic typhoid carriage, and early detection of ovarian cancer. Results: The results demonstrate that the DW-EML approach outperformed methods traditionally used in metabolomics, such as the Partial Least Squares Discriminant Analysis in terms of accuracy and predictive power. Conclusions: The DW-EML model is a promising tool for metabolomic data analysis, offering enhanced robustness and reliability for diagnostic and prognostic applications and potentially contributing to the advancement of personalized and precision medicine. Full article