Abstract: BL Lac objects are active galactic nuclei notable for beamed nonthermal radiation, which is generated in one of the relativistic jets forming a small angle to our line-of-sight. The broadband spectra of BL Lacs show a two-component spectral energy distribution (SED). High-energy-peaked BL Lacs (HBLs) exhibit their lower-energy (synchrotron) peaks at UV to X-ray frequencies. Consequently, these objects are generally bright in the 0.3-10 keV bands (compared to other blazar subclasses) and allow us to carry out intense timing and spectral studies on the wide range of timescales (from years down to a few minutes). Although x-ray emission of HBLs is widely accepted to have a synchrotron origin, many problems associated with the jet particle content, their acceleration up to ultrarelativistic energies, and unstable mechanisms responsible for the extreme flux and spectral variability still remain to be solved. This review highlights the basic timing and spectral results obtained in the framework of the numerous timing and spectral studies of HBLs in the 0.3-10 keV band which is covered by the X-ray instruments operating onboard the different space missions. Moreover, the plausible physical processes ot be responsible for the observed HBL features (relativistic shocks, magnetic reconnection, turbulence etc.) are also addressed.

Abstract: This study evaluated the effect of byproduct from mining process of granite rock doses in cultures of high demand in soil fertility with annual and perennial cycles, such as soybean and Tamani perennial grass, for two years in the municipality of Paragominas-PA. Experimental design was in randomized blocks comprising five treatments and five replications. Treatments comprised doses of byproduct from granite rock of 1000, 2000, 4000 and 6000 kg ha-1 and a control treatment (without application) applied in soybean and Tamani perennial grass. Soil parameters and crops productivity were evaluated for two years. The higher doses showed positive effects on soil fertility parameters, including potassium increases. Crops productivity had low responses to application or residual effects of the byproduct from granite rock mining process from Tracuateua-PA. The byproduct from mining process of granite rock has low influence in soil fertility and yield of soybean and Tamani perennial grass.

Abstract: Acoustoelasticity describes the relationship between elastic wave velocities and the initial stress present in media. Most of the current research in this area is focused on elastic waves propagating in naturally isotropic media with initially uniaxial stress and confined the solution of acoustoelastic problem to the Cartesian coordinate system, which limits the application for the acoustoelasticity theory. In this paper, to make up for this deficiency, an acoustoelastic formulation, i.e. the equation of motion for incremental displacement with explicit dependency on initial stress or strain, is developed based on the general theory of incremental deformation. This formulation can be applied to material with any symmetricity, initial stress with any number of principle axis. Also, it can be expressed in any form of coordinate system for its tensorial nature. Furthermore, to the knowledge of authors, there is no integrated cylindrical expression for acoustoelastic theory, an expansion of the formulation in a cylindrical system for an orthotropic material is given in detail for the convenience of application, such as cylindrical waves.

Abstract: The Riemann Xi function admits the representation \( \Xi(t) = \int_0^\infty \Phi(u)\cos(tu)\,du \) where \( \Phi \) is a positive, even, integrable function. By a classical theorem of P\'olya (1927), if \( \log\Phi \) is concave on \( [0,\infty) \), then \( \Xi \) has only real zeros, which is equivalent to the Riemann Hypothesis. We prove that the dominant term of \( \Phi \) has strictly negative second logarithmic derivative for all \( u \geq 0 \), reducing the full log-concavity to a quantitative tail estimate. We verify this estimate by rigorous interval arithmetic (5000 certified subintervals on \( [0, 1/2] \) at 80-digit precision, with the complement handled analytically). The entire argument is formalised in the Lean~4 proof assistant with the Mathlib library.

Abstract: Background/Objectives: Although pharmaceutical formulations on the market are more varied than in previous decades, developing tablets remains a continuing interest due to increased patient compliance. Simultaneously, the development of multifaceted excipients has remained a requirement of the pharmaceutical industry. This study aimed to develop a granular co-processed excipient for tablets and to evaluate it using the SeDeM expert system. Methods: Six granule formulations (obtained via wet layering granulation) were developed using different binder concentrations, fillers, and core types. The binders’ concentration (AquaPolish® STA) varied on three levels: 10%, 15%, and 20%. Three formulations used microcrystalline cellulose as the filler, while the remaining three replaced it with lactose (six formulations coded E1-E6). The granules obtained were evaluated using the SeDeM expert system for all 12 characteristic parameters, encompassing six incidence factors. The unloaded granules were compressed to yield uncoated tablets, which were verified for dimensional parameters, mechanical properties, and disintegration ability in accordance with the in-force European Pharmacopoeia requirements. Results: The binder concentration influenced particle size, with a 20% AquaPolish® STA concentration yielding large granules. It has been observed that the type of core used to prepare the granules played an important role in establishing mechanical strength; thus, the formulation in which Cellets® was used exhibited lower resistance than those in which sugar was used. During the SeDeM evaluation, it was observed that two formulations (E4 and E5) exhibited good results in terms of the parameter index (PI), parameter profile index (PPI), and Good Compressibility Index (GCI). The recorded disintegration times were less than 15 minutes for all the tablets obtained from the formulated granules. Conclusions: For granule development, binder concentration had the greatest influence on particle size, mechanical strength, and lubricity; also, the type of core used played an important role in tablet mechanical strength. With the help of the SeDeM expert system, the excipients most suitable for developing uncoated tab-lets were highlighted.

Abstract: The digital age has fundamentally dissociated the creation of fundamental intellectual frameworks, such as novel theories, methodologies, and paradigms, from their widespread application and economic value realization. The fundamental reason why the creators of such meta-intellectual labor often receive disproportionate returns to the enormous long-term social and commercial value created by their work is that we cannot accurately measure, attribute, and automatically trade the value contained therein. In this paper, we propose a new integrated framework for automated valuation and liquidation of knowledge contribution based on the principle of fusion intelligence. This problem is formalized as a Knowledge Contribution Valuation and Liquidation (KCVS) system, with the dual formalization mechanism as its operational core, and the nine steps of intellectual integration as the maturity model of value creation. It shows how AI systems themselves, especially large language models, can be repositioned as impartial measuring instruments, automated traders, and transparent governance within this framework. Through the analysis of real cases of DeepSeek and Qianwen in scientific research and commercial applications, it is clarified that their underlying architectures have instantiated dual formal mechanisms, thus providing empirical support for the theoretical basis of the system proposed in this paper. This is followed by a blueprint consisting of three pillars: (1) an AI-driven knowledge contribution index for dynamic, multi-dimensional impact measurement; (2) a decentralized micropayment and clearing layer based on smart contracts; and (3) a transparent governance protocol for auditability using distributed ledgers. A simulated economic model is used to assess the feasibility of the framework and demonstrate its potential in building a sustainable, equitable, and self-optimizing ecosystem for foundational intellectual labor. This paper provides a theoretical and practical roadmap for aligning the incentives of knowledge creators with the structure of AI-driven economies, ensuring that future innovation is both dynamic and fair.

Abstract: Globally, the burden of breast cancer remains high as it is the most prevalent malignancy among women and a major contributor to cancer mortality, with therapeutic success often limited by drug resistance, treatment toxicity, and tumor heterogeneity. Cold atmospheric plasma (CAP), a partially ionized gas enriched in reactive oxygen and nitrogen species (RONS) electromagnetic waves and ultraviolet radiation, has emerged as a selective antitumor therapy, inducing cancer-specific cytotoxicity while sparing normal tissue. Here, we review the mechanisms of CAP action against breast cancer, including RONS-mediated oxidative stress, mitochondrial disruption, apoptosis, immunogenic cell death, and suppression of metastatic and angiogenic pathways. Notably, This approach selectively targets therapy-resistant breast cancer stem cells and sensitizes the highly aggressive forms, particularly triple-negative breast cancer (TNBC). Its synergy with drug therapy, radiotherapy, immunotherapy and surgery further broadens therapeutic potential. Advances in delivery platforms, such as plasma-activated media, nanoparticles, and hydrogels, address CAPs instability and enhance tumor penetration. Despite promising preclinical results, clinical translation faces barriers such as the short half-life of RONS, device standardization, and unresolved immunomodulatory effects. Overcoming these challenges through interdisciplinary collaboration and optimized protocols may unlock the potential of CAP for precision oncology.

Abstract: Background/Objectives: Diagnosing periprosthetic joint infection (PJI) after megaprosthetic reconstruction may be difficult due to altered inflammatory responses, extensive prior surgery, and the limited performance of conventional criteria such as the 2018 ICM score. Synovial calprotectin is a rapid neutrophil-derived biomarker that may improve diagnostic accuracy in this challenging setting. The primary aim of this study was to evaluate the diagnostic performance of synovial calprotectin in detecting periprosthetic infection in patients treated with tumor megaprostheses; secondary aims included comparison with ICM classification, assessment in infection classification-inconclusive cases, and exploratory performance in patients with low CRP. Methods: This prospective study included 20 consecutive megaprosthesis patients evaluated for suspected PJI at ATTIKON University Hospital, Athens, with a minimum follow-up of 1 year after biomarker testing. Synovial calprotectin was measured using a lateral-flow assay (positive ≥ 50 mg/L) and compared with a predefined infection reference standard. ICM final status (0 = aseptic, 1 = inconclusive, 2 = infected) was recorded for all cases. Other synovial biomarkers (α-defensin, leukocyte esterase, synovial D-dimer) were not routinely available. The cohort had a mean age of 52.9 ± 22.5 years, 70% were male, and reconstructions involved the knee (80%), hip (15%), and humerus (5%). Preoperative cultures were positive in 40%, systemic WBC was elevated in 55%, and the median time from last surgery to testing was 1.0 years (IQR 0.46–2.0). Among infected cases, the most common microorganisms were coagulase-negative staphylococci (61.5%) and Staphylococcus aureus (23.1%), with 30.8% demonstrating polymicrobial infection. Results: Thirteen of 20 patients (65%) were classified as infected. Using the ≥ 50 mg/L threshold, synovial calprotectin demonstrated high diagnostic accuracy, and no false positives, yielding a sensitivity of 92.3%, specificity of 100%, PPV of 100%, NPV of 87.5%, LR+ = ∞, and LR− = 0.08. The AUC for continuous values was 1.00. Agreement with the Parvizi final classification was substantial (κ = 0.76), with no directional discordance (McNemar p = 1.00). Among the three ICM-inconclusive cases, calprotectin correctly reclassified two (66.7%). In patients with low CRP (< 10 mg/L), a clinically difficult subgroup, calprotectin maintained strong performance (sensitivity 75%, specificity 100%, NPV 85.7%). Conclusion: Synovial calprotectin demonstrated excellent diagnostic accuracy for PJI in megaprosthesis patients, with high sensitivity, perfect specificity, and substantial agreement with the 2018 ICM criteria. It successfully clarified most ICM-inconclusive cases and remained reliable even in patients with low CRP. These findings support calprotectin as a valuable adjunctive biomarker in the complex diagnostic environment of megaprosthetic reconstruction and justify further validation in larger cohorts.

Abstract: Structural reanalysis involves repeated evaluation of structural responses under iterative design changes. It is a major computational burden in structural optimization, sensitivity analysis, and health monitoring. The three-layer architecture, which comprises the stiffness, displacement, and force layers, is motivated by the governing structural mechanics relationship F=K·U, which establishes stiffness and displacement as natural intermediate quantities for predicting internal forces. This physics-informed hierarchy reduces dependence on large training datasets while preserving predictive accuracy across all response quantities. The framework predicts member-level stiffness statistics, nodal displacements, and internal forces through three sequential layers: stiffness, displacement, and force. Each layer enriches the feature set of the layer above. Sensitivity-based secondary inputs are derived analytically from closed-form expressions relating cross-sectional dimensions to stiffness and displacement changes. This embeds structural mechanics knowledge directly into the feature engineering process without additional analyses. Member stiffness matrices are recovered as submatrices of the global stiffness matrix, encoding inter-member structural context into each member’s representation. The framework is implemented on a six-floor, three-bay reinforced concrete frame of 42 members. Training uses 1,890 data points from 45 finite element iterations. The Random Forest model achieves R² scores of 0.99, 0.98, and 0.91 for axial force, shear force, and bending moment respectively on unseen validation data. Once trained, the framework predicts any number of design iterations in a single inference pass. This substantially reduces the computational cost of reanalysis-based workflows. The proposed framework offers a scalable, interpretable, and physics-consistent alternative to both classical reanalysis methods and purely data-driven surrogate models, with direct applicability to structural size optimization and structural health monitoring workflows.

Abstract: Rice chalkiness is a key constraint on high-quality rice breeding, and unbalanced sucrose transport and starch metabolism are its primary causes. To clarify the molecular mechanism by which OsSUT2 regulates rice grain chalkiness formation, the rice cultivar TP309 was used as material, and ossut2 homozygous mutants were generated via CRISPR/Cas9. Systematic studies were performed using gene overexpression complementation, phenotypic identification, cytological observation, transcriptome sequencing and haplotype analysis. Results showed that loss of OsSUT2 function significantly increased grain chalkiness, deteriorated agronomic traits, induced carbon assimilate accumulation in leaves, blocked sugar transport and starch synthesis in grains, and destroyed starch fine structure; normal phenotype was fully restored by OsSUT2 overexpression. OsSUT2 was expressed in both source and sink organs, with the most obvious inhibition detected in panicles. Mutation of OsSUT2 disordered sucrose and starch metabolic pathways. Three main haplotypes of OsSUT2 were identified in natural populations, with significant indica–japonica differentiation. OsSUT2 is confirmed as a key regulator of rice chalkiness, providing gene resources and theoretical support for rice quality improvement.

Abstract: Traditional credit scoring models reduce decisions to static classification, ignoring dynamic risk evolution and long-term profit. This paper integrates the Hamilton-Jacobi-Bellman (HJB) equation with deep reinforcement learning, reformulating credit risk as a discrete-time stochastic optimal control problem. Theoretically, we establish equivalence between discrete Markov decision processes and the HJB equation, prove existence and uniqueness of the optimal value function, derive the closed-form Riccati solution under linear-quadratic assumptions, and show neural network value iteration is an effective numerical scheme with separable errors. Empirically, using LendingClub data (2016–2018), the HJB-based PPO model significantly outperforms all static baseline models considered (e.g., logistic regression, random forest, XGBoost) in average profit (1.5167) and total profit (786,700.4682). Ablation experiments replacing the policy network with linear mapping reduce profit by 34.7%, confirming the necessity of nonlinear approximation. Theoretical validation gives a mean squared error of 0.0006 between the neural value function and Riccati solution. This work provides a rigorous mathematical foundation for reinforcement learning in financial risk control and a path from static classification to dynamic optimization in credit scoring.

Abstract: In the face of economic disparities, housing as a fundamental right highlights differences and social stratification. From the perspective of complexity, factors such as location, distance from development hubs, and designs that standardize needs accentuate weaknesses in its conception. The new realities of living in housing after the pandemic lead us to rethink new design approaches where housing and work can be combined. This research analyzes the case of the Ciudad Alegría Social Housing Program, located in the city of Loja, Ecuador. The diagnostic method determined that 24% of the homes have commercial projections as a survival strategy. While these spatial patterns reduce the levels of habitability in the homes, they also produce benefits such as proximity between home and work, savings in transportation costs, interaction with neighbors, and mixed uses. These facts reflect gaps in the architectural design process, which fails to consider both service providers and users in decision-making in the design of VIS programs, as well as the need for this phenomenon to be elevated to public policy.

Abstract: Currently (2025), deep learning is the most important and popular methodology in artificial intelligence (AI) and artificial neural network (ANN) is the foundation of deep learning. The main drawback of ANN is the boom problem of a huge number of parametric weights when ANN in deep learning establishes a large number of hidden layers. The boom problem can be alleviated by high-performance computer but will be serious in case of high-dimension input data like image. The excellent solution for image processing within context of deep learning is that large parametric weight vector is reduced into much smaller window encoded by a so-called filtering kernel which is often 3x3 matrix or 5x5 matrix which is convoluted over entire image data. ANN with support of such filtering kernel is called convolutional neural network (CNN). Many researches prove that CNN is feasible and effective in image processing. The hidden cause of the effectiveness of CNN is that the visionary structure of an image is aggregated in such a way that filtering kernel is ideal to extract image features. However, it is not asserted that matrix-based filtering kernel is appropriate to other high-dimension data that is not image. Another solution of the boom problem is that large parametric weight vector is organized as matrix that is the same structure of 2-dimension data like image, which leads to a so-called matrix neural network (MNN) whose parameters are weighted matrices. Computation cost of MNN is decreased significantly in comparison with ANN but it is necessary to test the effectiveness of MNN with respect to CNN. This is the main hypothesis “whether MNN is the alternative of CNN” which is tested in this research, hinted by the research title. Moreover, transformer which is the new trend (2025) in AI and deep learning, which aims to improve/replace traditional ANN by self-supervised learning, in which attention is the significant mechanism of self-supervised learning. Anyhow, attention which is the cornerstone of transformer is the representation of internal structure/relationship inside high-dimension data like image. Therefore, the implicit deep meanings of attention and filtering kernel are similar, which represents feature of data, which does not go beyond parametric weights too. In general, the research has two goals: 1) explaining and implementing ANN, CNN, and transformer (attention) and 2) applying analysis of variance (ANOVA) into evaluating the effectiveness of ANN, CNN, and transformer (attention) within context of image classification. The ultimate result is that it is not asserted that MNN is the alternative of CNN but MNN can be an optional choice for implementing ANN in context of image processing instead of focusing on the unique CNN solution. Moreover, the incorporation of MNN and attention in implementing transformer produces a compromising solution of high performance and computational cost.

Abstract: Background and Aims: This study aimed to systematically search for molecular biomarkers that contribute to the risk of coronary artery disease (CAD). Methods and Results: A SNP-based multiomics data analysis plan was used to identify biomarkers contributing to the risk of CAD through a two-step discovery and validation design. By integrating CAD GWAS data with epigenome, transcriptome, and proteome quantitative trait loci (QTLs) from blood, 44 CpG sites, 37 transcripts, and 27 protein biomarkers were identified contributing to the risk of CAD. The identified biomarkers shared interactions and were enriched in lipid metabolism-related processes. The PCSK9 protein was under the regulatory impact of the APOC1, GZMA, and GRN proteins. The impact of SMARCA4 and PSRC1 transcripts on CAD were mediated through lipids, whereas the influence of the FES transcript on the risk of CAD was attributed to blood pressure. Finally, while 53% of the transcripts identified through the discovery stage were validated, this ratio was 20% for the protein biomarkers and 24% for the CpG sites. Conclusions: This study identified biomarkers contributing to the risk of CAD through a two-step discovery and validation analyses; furthermore, it provided insights into the paths by which several biomarkers influence the risk of CAD and underlined the efficiency of transcriptome platforms in identifying biomarkers.

Abstract: The rapid expansion of sports broadcasting and digital media platforms has increased the demand for intelligent systems capable of automatically identifying important sports events for real-time analytics and highlight generation. Manual annotation of sports videos requires significant time and effort and may introduce human errors during analysis. This paper presents a real-time sports action recognition framework using a hybrid CNN–Transformer architecture for detecting critical events in football and cricket videos. The proposed system processes live or recorded video streams through frame extraction, normalization, and spatial feature learning using the MobileNetV2 network. Temporal relationships between consecutive frames are modeled using a Transformer encoder to improve action understanding. The framework classifies events such as pass and goal in football, and four, six, and wicket in cricket. Motion-based filtering and confidence thresholding reduce non-action frames and improve prediction reliability. Detected events are recorded with timestamps and displayed using broadcast-style overlays to support automated highlight generation. Experimental evaluation demonstrates high recognition accuracy and efficient real-time performance on low-cost hardware platforms. The framework provides an effective solution for sports analytics, media automation, and intelligent decision-support systems.

Abstract: Starting from the assumption that, in contemporary debate, sustainability should increasingly be interpreted within a sociological paradigm, this contribution aims to analyse the soft competences required for the education of a citizen capable of developing an ethical and inclusive orientation, understood as a civic prerequisite for processes of sociocultural integration. From this perspective, sustainability is not considered solely as a set of environmental practices or public policies, but rather as a cultural and normative dispositive that structures habitus, representations and models of action. The progressive acquisition of knowledge, values and practices oriented towards sustainability, both at the individual and collective levels, makes it possible to frame this phenomenon as a constitutive dimension of processes of modernisation and sociocultural development. Such processes are frequently supported and accelerated by technological innovation, which acts as an enabling but not determining factor. Moving beyond deterministic interpretations of a technological, political or economic nature, the analysis adopts a culturalist perspective that emphasises the social construction of a sustainable identity, namely an identity that assumes sustainability as a regulative principle of everyday action and as a lifestyle for the contemporary citizen. This trajectory implies the active and inclusive involvement of agencies of socialisation, first and foremost the school institution, called upon to promote sustainability as a foundational value of social inclusion and community cohesion. Within this framework, the second part of the contribution explores sustainability education through the implementation of a design protocol for digital education within STEAM disciplines, placing particular emphasis on methodologies and tools such as coding and educational robotics, understood as pedagogical tools for the development of critical, collaborative and socially responsible competences.

Abstract: Patients with cardiovascular diseases often require cardiac surgery with cardiopulmonary bypass (CPB), which triggers inflammation and increases the risk of postoperative atrial fibrillation (POAF). This study assessed the predictive value of inflammatory biomarkers and clinical and surgical variables for POAF in patients undergoing coronary artery by-pass grafting (CABG; n = 36), valve surgery (n = 40), or combined CABG and valve surgery (n = 13), all of whom utilized CPB. Levels of IL-6, IL-8, IL-10, and C-reactive protein (CRP) were measured preoperatively, at 24 and 48 hours postoperatively, and at discharge. Sta-tistical analyses included t-tests, Mann–Whitney U tests, correlation analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis. Sixteen of 89 patients (18%) developed POAF between 48 and 72 hours after surgery. The Society of Thoracic Surgeons (STS) score and hemoglobin at 24 hours were significantly different (p < 0.05) between the POAF and non-POAF groups. At 24 hours, POAF patients had significantly higher IL-6, IL-8, and IL-10 levels (p < 0.02); IL-6 remained elevated at 48 hours (p < 0.05), while CRP declined at discharge (p = 0.05). A multivariable model including STS score, IL-6 at 24 hours, and postoperative magnesium yielded an AUC of 0.82, with an optimism-corrected AUC of 0.77 after internal bootstrap validation. Integrating inflammatory and clinical variables produced a robust predictive model.

Abstract: Garden pruning waste from Cynodon sp. is a lignocellulosic resource with high lignin content, which limits anaerobic digestion efficiency. While white-rot fungi can delignify biomass through solid-state fermentation (SSF), their efficacy depends on balancing lignin removal with preservation of fermentable carbohydrates. This study evaluated the effect of SSF times (8, 21, and 36 days) with Trametes hirsuta on enzymatic activity and subsequent biogas production. Laccase activity increased progressively, reaching 983.84 U/L at 36 days; whereas manganese and versatile peroxidases peaked at 21 days. Fungal-pretreated samples exhibited lower methane yields, a maximum of 225.32 NmL/gVS at 8 days, compared to untreated biomass (381.66 NmL/gVS). Total lignin content apparently increased across treatments, suggesting pseudo-lignin formation during autoclave sterilization, while glucose and xylose decreased. Biological pretreatment affected methane production by reducing sugar availability, potentially forming inhibitory furanic compounds and antimicrobial metabolites, thereby negating the benefits of enzymatic delignification. These results underscore the complexity of optimizing fungal pretreatment and highlight the need to balance fermentation time to preserve carbohydrates while modifying lignin structure.

Abstract: Market-based environmental policies are typically evaluated in terms of their deterrent effects on individual behavior, yet this perspective offers only a partial explanation of how such instruments operate in practice. This study argues that market-based regulation can also function as a legitimacy-generating governance mechanism that shapes environmental action through socio-emotional pathways. Focusing on Turkey’s plastic bag charge introduced in 2019, the study examines whether price-based regulation operates solely through cost sensitivity or also through perceived policy legitimacy and emotional environmental engagement. Drawing on ecological modernization theory and regulatory governance literature, the study employs survey data from 515 participants in Turkey to test a mediation model linking perceived policy legitimacy, emotional environmental engagement, and environmental action. The findings show that perceived policy legitimacy significantly enhances emotional environmental engagement, which in turn predicts both individual and collective environmental action. These results indicate that policy effectiveness extends beyond economic deterrence and depends on the capacity of policies to generate emotional engagement among citizens. The study contributes by demonstrating the dual governance role of market-based instruments and by integrating affective mechanisms into environmental governance analysis.

Abstract:

Background: Early marriage among girls under 19 years remains a significant public health and social concern in the Sundarbans of West Bengal, India. Despite legal restrictions, the practice continues due to socio-cultural norms, economic constraints, and gender inequality. Objectives: To assess the sociodemographic characteristics of girls married before the age of 19 years and to explore their opinions regarding early marriage. Methods: A mixed-methods study employing a convergent parallel design was conducted among 20 girls married before the age of 19 years. Quantitative sociodemographic data were analyzed descriptively, while qualitative insights were generated through two Focus Group Discussions (FGDs) and analyzed using thematic analysis. The findings were integrated using a joint analysis approach to examine convergence, divergence, and complementarity across data strands. Results: Quantitative findings: Most participants were aged 16–18 years (80%), with 90% living with their husbands. A majority were housewives (60%), while others were engaged in daily work or farming. Half had secondary education (50%), while 15% had no formal education. Most participants had no children (65%). Qualitative findings: Early marriage was socially accepted and influenced by family pressure and limited autonomy. Although participants preferred marriage after valued education, early marriage often resulted in school discontinuation. Girls reported a lack of readiness for marital responsibilities and economic dependency. However, many expressed a desire to delay marriage and continue education. Conclusion: Early marriage persists due to entrenched socio-cultural and economic factors despite awareness of its adverse effects. Strengthening education, empowerment, and community awareness is essential to delay the age of marriage.