Abstract: A single-server queueing framework with infinite queueing capacity is formulated and analyzed, where customer arrivals are governed by a Markovian arrival process and both service and repair times are characterized through phase-type distributions. The service structure is two-tier: each incoming customer undergoes a mandatory primary service under a first-come, first-served discipline; then a secondary service becomes available on an optional basis, provided only at the customer’s request after the primary service is completed. When the system empties, the server initiates a shutdown process before entering a vacation period. Upon return from vacation, the server resumes service if customers are present; otherwise, successive vacations will be taken until demand arises. Random server failures can occur during either service mode, after which the server undergoes repair before restarting. The steady-state behavior of the system is analyzed using the matrix analytical method, from which the stability conditions, stationary probability vectors, and key performance metrics are derived. A cost analysis framework is also formulated to evaluate the economic implications of system operation. To substantiate these analytical findings and illustrate the practical applicability of the proposed model, a series of numerical experiments are performed.

Abstract: Algorithms based on gradient-boosted trees are known for predictive accuracy in tabularregression, while hybrid boosting designs adding non-tree terms remain underexplored. We in-troduce Ordered Piecewise Additive Learner (OPAL), a stagewise regression model combininghinge, categorical indicator, and regression-tree terms. Stage 1 of OPAL sequentially adds hingeand categorical indicator terms to squared loss residuals; Stage 2 fits regression-tree terms toremaining residual structure. To increase expressiveness, OPAL augments data with determinis-tic numeric transformations, including products, ratios, differences, signed square roots, signedlogarithm of one plus absolute value transforms, and squared terms. OPAL can optionally fita single tree before the two-stage model and train the main component on the resulting residu-als. As regularization, inner cross-validation selects the prediction rule among the full predictor,the two-stage component alone, the optional single-tree component, the training-set mean, or aweighted blend. The rule is stored as fitted state, and the selected rule is reflected in a distilledadditive prediction equation used for term-level summaries. Across 35 regression tasks, OPALhas the best average RMSE rank against XGBoost, LightGBM, and MARS: 1.8571 versus 2.0286,2.4286, and 3.6857. A Friedman test rejects equality of ranks (χ2F = 42.977, p = 2.49 ×10−9).Log-ratio paired tests do not separate OPAL from XGBoost but favor OPAL over LightGBMand MARS. The results support the strong predictive performance of the configured OPALpipeline as a whole rather than any isolated algorithmic component; potential research direc-tions therefore include further study of OPAL’s constituent parts. A unique aspect of OPAL isits partial additive transparency: hinge and categorical terms support effect-curve summarieswhen they have nonzero weight in the distilled prediction equation, although the full predictormay include generated pairwise features and tree terms involving the same raw variables.

Abstract: Background/Objectives: Rhanterium epapposum Oliv. a medicinally valuable plant traditionally used by indigenous communities to treat skin and gastrointestinal ailments and as a natural insecticide, was investigated for its phytochemical composition and diverse biological activities (antioxidant, antimicrobial, anti-inflammatory), and enzyme inhibitory (α-amylase and lipoxygenase), supported by advanced in silico analyses. Methods: Methanolic and aqueous extracts were analyzed for total phenolic, flavonoid, and tannin contents, with their metabolite profiles characterized via LC-ESI-MS/MS. Both extracts were comprehensively assessed for antioxidant, antimicrobial, anti-inflammatory, and enzyme inhibitory (α-amylase and 5-lipoxygenase) activities. Furthermore, in silico framework was applied to elucidate the binding efficiency and inhibitory potential of key bioactive compounds against selected pharmacological targets. Results: Many bioactive compounds, mainly chlorogenic and syringic acids were identified in both extracts. The aqueous extract showed higher TTC (69.61 ± 0.212 mg TAE/g extract) and TFC (23.81 ± 0.163 mg QE/g extract), while the methanolic extract was richer in phenolics and exhibited overall stronger scavenging antioxidant activity with IC₅₀ of 14.9 ± 4.7 µg/mL (DPPH) and 35.0 ± 0.67 µg/mL (ABTS), respectively. both extracts exhibited remarkable antimicrobial activity towards ESKAPE and Candida spp. strains. Furthermore, the aqueous extract demonstrated greater, dose-dependent oedema inhibition, peaking at 100 mg/kg, and stronger α-amylase (IC₅₀ = 188 μg/mL) and lipoxygenase (IC₅₀ = 49 μg/mL) inhibition than the methanolic extract (IC₅₀ = 247 μg/mL and 188 μg/mL, respectively), though both were less potent than standard drugs. Molecular docking, MD simulations, and DFT analyses assessed phytocompounds against multiple targets. Chlorogenic acid exhibited multi-target activity, forming hydrogen bonds with Ser49, Thr121, Leu5, Ala7, Asp27 (3FYV), Asp120, Asp86, Asp218 (3Q70), Ser602, Arg415, Asn382, Arg483, Ile461, Ser508 (7Q6S), Gly249, Asp212, Tyr2 (1B2Y), and Gly249, Leu211, Asp212 (1N8Q). MD confirmed complex stability, while DFT (6-31G**) highlighted favorable electronic reactivity and stability. Conclusions: Overall, the aqueous and methanolic extracts showed complementary bioactivities, emphasizing their potential as natural therapeutic agents and viable candidates for developing new pharmacological formulations.

Abstract: One of the most common ways to improve the properties of aluminum casting alloys is through their modification. This study investigates the influence of titanium modification on the mechanical properties of Al-Si casting alloys. In this research, the Al-Si alloy which is widely used in the foundry industry, was selected as an object. The samples were liquefied in an induction furnace, and the liquid alloy was poured into sand-clay molds. The casting temperature was 750 °C. The titanium element was added to the liquid Al-Si foundry alloy in special packaging in the form of a powder from 0.1% to 0.3% of the charge and in the form of Al-10Ti master alloy from 0.1% to 0.2%. Then, samples were machined to prepare further investigations. During the research, mechanical properties including hardness and wear resistance analysis were conducted. Moreover, X-ray diffraction and microstructural analysis of the Ti modified Al-Si samples were carried out. Experimental results showed that the addition of titanium improved the mechanical properties of the samples. That is, the highest hardness was obtained at 0.1 wt.% Ti modified Al–10Ti master alloy, while titanium powder resulted in a more gradual increase in hardness. According to the wear resistance evaluations, addition of titanium within the range of 0.1–0.2 wt.% content was performed an optimal result. After, microstructural analysis, it is found that titanium promoted grain refinement and improved structural homogeneity, especially it is added in the form of Al-10Ti master alloy. The introduction of titanium into the aluminum alloy led to the formation of the Al₃Ti intermetallic compound, which contributed to the improvement of mechanical properties. These results demonstrate that the modification of Al–Si alloys with titanium can be reliably used to predict and improve mechanical properties based on comprehensive experimental analysis.

Abstract: Effective dementia care is hindered by fragmented perspectives among patients, caregivers, and clinicians, leading to delayed or suboptimal interventions. We introduce a novel Ambient Assisted Living (AAL) framework that reconceptualizes the home as an active caregiving partner, aiming at safe aging in place. Our key contribution is a privacy-preserving, affordable, holistic system with continuous real-world validation, designed for unobtrusive, zero-interface operation. The system integrates novel low-cost hardware solutions, including a remote audio announcement device, a bidirectional intercom, and a “Zero-Interface Mirror” enabling non-recorded, real-time video co-presence between patients and guardians. A local edge-based AI architecture processes multi-modal inputs without persistent data storage, ensuring privacy by design. The system combines real-time acoustic event detection (via Audio Spectrogram Transformer), automatic speech recognition and diarization, dual-stage fall detection, and extraction of acoustic biomarkers from natural conversations, which are selectively uploaded -depending on Dementia stage- to a clinician-facing dashboard for longitudinal monitoring. A 10-month in-home deployment demonstrates robust detection of clinically relevant patterns revealed through heatmaps, including nocturnal wandering, counting delusional incidents, behavioral disturbances, quantifying silence and TV usage, respiratory irregularities, and nutritional adherence. The proposed framework establishes a scalable, privacy-first paradigm for continuous dementia monitoring, enabling earlier and more informed clinical interventions to push back hospitalizations and decompress elderly care facilities.

Abstract: Water deficit is one of the main limiting factors in agricultural production, affecting the growth and photosynthetic performance of plants. The aim of this study was to evaluate the effects of foliar applications of melatonin, A. nodosum and L. calcareum on black pepper, ‘Bragantina’, subjected to three cycles of water deficit, on physiological per-formance, carbohydrate allocation and vegetative growth. The results showed that the applied solutions contributed to the recovery of damage caused by water stress. The first cycle of water deficit affected the electron transfer quantum yield, resulting in the re-duction of φP0 and PIABS. However, the damage to the photosynthetic apparatus was recovered in subsequent cycles. Water stress reduced chlorophyll indices, possibly as a strategy to minimize photo-oxidative damage. Plants treated with melatonin and L. calcareum maintained a stable leaf expansion rate and showed greater water recovery, standing out in the second and third cycles of water deficit.

Abstract: Male infertility is estimated to affect over 30 million men around the world, with a prevalence of 2.5% - 12%. Plants, including Aframomum melegueta, are valued for their nutritive and medicinal properties, particularly on the reproductive system, due to bioactive compounds. This study examined the effects of Aframomum melegueta aqueous seed extract (AMASE) on male reproductive parameters Twenty-five adult male Sprague-Dawley rats (150-190 g) were randomly assigned to five groups (n=5). AMASE was orally administered at doses of 115, 230, 345, and 460 mg/kg daily for 21 days alongside a naïve control group. Blood and reproductive organs were collected post-treatment for hormonal, histological, and sperm quality assessments. Data were analyzed using ANOVA with Dunnett’s post hoc test comparing the various treatments to the naïve control group. Treatment with 345 mg/kg AMASE significantly increased prostate gland weight (p=0.038). Sperm concentration significantly increased in all treated groups, with 345 and 460 mg/kg showing the most pronounced effects (p < 0.0001), while sperm morphology improved significantly at 345 mg/kg (p = 0.0013). Testosterone levels exhibited a biphasic response, with a significant increase at 115 mg/kg (p = 0.0009) and a decline at 460 mg/kg. Histological analysis confirmed preserved testicular structure at 115 and 230 mg/kg, enhanced spermatogenesis at 345 mg/kg, and alterations at 460 mg/kg. Although AMASE exhibited selective activity improving sperm concentration and morphology without affecting motility or viability, it demonstrated promising potential as a fertility-enhancing agent.

Abstract: Mitral valve regurgitation is the second most common valvular heart disease in Europe, and an estimated 10% of individuals older than 75 years have severe mitral regurgitation. Mitral valve repair is the preferred strategy to treat mitral regurgitation and is associated with better outcomes than mitral valve replacement. Despite the proven efficacy of surgical repair, available data in functional aetiologies reported a not negligible rate of echocardiographically detected severe mitral regurgitation within ten years of the index procedure, in some cases resulting in redo interventions. Data on the optimal management of patients with failed mitral repair remain limited. The aim of this review is to present the available approaches for treating failed mitral valve repair and to describe criteria for selecting the most appropriate strategy on the basis of the underlying mechanism of repair failure, with respect to possible surgical re-repair and novel transcatheter edge-to-edge repair techniques in presence of favourable mitral valve anatomies.

Abstract: To address the problems of strong noise, high asynchrony, pronounced subjectivity in risk labels, and insufficient model stability under extreme market conditions in multi-source risk signals within trading environments, a low-noise investment risk prediction method based on multimodal sensing signals and self-supervised representation learning is proposed. Market quotations, order books, terminal interactions, network transmission, device status, and news sentiment are uniformly modeled as risk perception signals. A temporal masking-based risk structure modeling module, a risk-oriented contrastive learning representation constraint mechanism, and a risk representation and downstream prediction task alignment strategy are designed to learn stable, transferable, and interpretable risk features. Experimental results show that the proposed method achieves the best performance in investment risk prediction, with mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE) reaching 0.0164, 0.0851, and 0.1281, respectively, outperforming baseline models including generalized autoregressive conditional heteroskedast (GARCH), multi-layer perceptron (MLP), long short term memory (LSTM), temporal convolutional networks (TCN), and Transformer. The IC, RankIC, and AUC reach 0.496, 0.462, and 0.817, respectively, indicating stronger risk ranking capability and improved discrimination between high-risk and low-risk states. At the classification recognition level, the proposed method also demonstrates superior accuracy, precision, recall, and F1-score, indicating that potential high-risk assets can be identified more accurately. Ablation experiments verify the effectiveness of multimodal fusion, temporal masking, self-supervised contrastive constraints, and task alignment modules. Robustness experiments further show that lower prediction errors and higher AUC can still be maintained in high-volatility and extreme-shock markets, demonstrating strong noise resistance, stability, and practical application potential in complex sensing scenarios.

Abstract: The pressing need to minimize cement-related CO₂ emissions attracted the search for agricultural waste ashes as supplementary cementitious materials (SCMs). Agro-waste ashes, rich in silica and alumina, are promising cementitious materials. However, prior researchers reported inconsistent chemical compositions and physical properties due to differences in calcination methods. This systematical review proofed the impact of calcination temperature on the chemical composition, physical properties and pozzolanic performance of agricultural waste ashes. Systematic Literature Review (SLR) following PRISMA protocols, with published articles retrieved from Scopus, Web of Science, Wiley Online Library, and Google Scholar (2014–2025) was used for the study. Using keywords, 524 published articles were first identified; after screening and eminence evaluations, 50 articles met the inclusion criteria. Data was obtained mainly on calcination methods, chemical compositions, physial properties and compliance with standards such as GS 1118 (2016) EN 197-1 (2011) and SANS 50197-1. Comparative analysis disclosed constant deficiencies in CaO (0.91–25.80%) and extreme SiO₂ (40–63%) and Al₂O₃ (10–42%) contents, particularly with open-air burning. The findings emphasized 600–700°C for 90–120 minutes as the best manufacturing window for standard-conformity of ashes derived from agro-waste materials. This review highlighted the importance of controlled calcination, identified research gaps, and provided evidence-based principles for manufacturing SCMs in construction.

Abstract: In this review, the occurrence in the environment and human surrounding, as well as toxic action of perfluorooctanoic acid (PFOA) and its selected short-chain analogs – perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) has been described. These substances belong to a group of polyfluoroalkyl substances (PFASs) widely represented in various compartments of the environment, including air, water and soil. They are also present in dust, drinking water and food, which are main sources of the exposure of humans to these compounds. Due to physico-chemical properties PFASs are strongly resistant to degradation in the biosphere and therefore, effectively accumulate in biota, including humans. PFOA has been produced from decades, but due to its toxicity it has been successively replacing by PFASs of shorter chain, including PFHxA and PFBA, which presence in the environment, as well as risk of human exposure and toxicity has been poorly investigated. It has been proven that PFOA reveals hepatotoxic and endocrine-disrupting activities, as well as exhibits prooxidative, immunotoxic, epigenetic and carcinogenic potential. Hitherto conducted researches have shown that PFHxA and PFBA are less toxic than PFOA, nevertheless additional extensive studies should be conducted in order to determine environmental and toxicological status of these compounds.

Abstract: Improving provitamin‑A cassava requires a clear understanding of how key agronomic and nutritional traits respond to seasonal variation and how these traits interact within a multivariate structure. The objectives were to evaluate agronomic and nutritional traits across seasons, apply multivariate analyses to uncover trait domains, and investigate inter‑trait relationships to guide breeding strategies. Forty‑two provitamin‑A cassava accessions were evaluated using a split‑plot randomized complete block design, with genotype as the main‑plot factor and harvest time as the subplot factor, replicated across seasons. Eight traits were measured, with emphasis on DM, FYLD, and TC. Multivariate analyses provided deeper insight into trait structure. Principal component analysis and factor analysis identified distinct trait structures: PCA revealed four trait domains, while factor analysis uncovered three latent trait groupings–Root Productivity (ML2), Vegetative and Compositional Diversity (ML3), and Harvest Efficiency (ML1). Traits such as RTWT and HI exhibited near‑zero uniqueness, reinforcing their roles as anchor indicators of productivity and efficiency, while TC displayed exceptionally high uniqueness, confirming its independence from yield domains and its regulation by distinct metabolic pathways. Biplots highlighted genotype dispersion and trait loadings, hierarchical clustering grouped accessions by combined agronomic and nutritional performance, and a chord diagram confirmed a tightly linked yield complex alongside a separate nutritional domain. Variance component estimates revealed contrasting levels of genetic and environmental influence. DM showed moderate genotypic variance and no significant seasonal effect, underscoring its stability and reliability for processing quality. FYLD exhibited low genotypic variance, high residual variance, and a highly significant season effect, confirming strong environmental sensitivity. TC displayed high genotypic variance and a significant seasonal effect, suggesting that carotenoid accumulation is both genetically controlled and environmentally modulated. The integration of mixed‑model variance partitioning with multivariate and network‑based analyses revealed clear differences in trait stability, genetic control, and inter‑trait relationships. These findings identify DM as a robust trait for selection, FYLD as highly environment‑dependent, and TC as a promising target for biofortification with manageable environmental sensitivity. The results provide a comprehensive model for index‑based selection strategies, guiding breeders toward “stacked” ideotypes that combine high yield potential, stable dry matter content, enhanced carotenoid accumulation, and efficient resource allocation to meet industrial, nutritional, and food security needs across cassava‑growing regions.

Abstract: The mathematization of science is undergoing a structural transformation driven by the rise of computation and data-intensive methods. While classical mathematization relied on explicitly defined laws and formal structures, contemporary scientific practice increasingly encounters mathematical objects that arise as outcomes of dynamical and algorithmic processes. This paper introduces the notion of computationally emergent structures to describe entities generated and stabilized through the interaction of parameterized models, optimization dynamics, and data. We develop a minimal formal framework in which such structures are characterized as asymptotic outcomes of learning dynamics and show that, in over parameterized regimes, they are selected by implicit variational principles not specified a priori. This framework provides a unified account of implicit regularization, kernel regimes, and stability phenomena in modern learning systems. These results show that contemporary learning systems operate according to implicit variational principles in which geometry, dynamics, and data jointly determine effective mathematical structure. They thereby identify a shift from representation to dynamical emergence, extending the scope of mathematization toward a theory of structure formation grounded in computation.

Abstract: Generative Artificial Intelligence (GenAI) is driving a fundamental paradigm shift in architectural design, transitioning from deterministic drafting to algorithmic curation. While the Architecture, Engineering, and Construction (AEC) sector rapidly adopts these tools, academic curricula face a critical "Techno-Instructional Void." This gap risks inducing a "Zero Order Thinking State" (ZOTS)—a cognitive passivity rooted in Cognitive Load Theory, where students uncritically accept unbuildable machine hallu-cinations. Developed through comprehensive preliminary consultations with academ-ic colleagues and longitudinal studio observations, this study introduces the "Twin Houses" methodology and the "Technical Sealing" protocol. By enforcing "Cognitive Friction," the framework compels students to validate probabilistic GenAI outputs against deterministic physical laws (e.g., Blondel's Formula 2R + T = 63 cm) and safety norms. Crucially, Building Information Modeling (BIM) acts as an automated Proof-Assistant, utilizing visual programming APIs (Revit Dynamo, Allplan Python-Parts) and IFC 4.3 data schemas for rigorous Rule-Based Checking (RBC). To confirm cross-border transferability and optimize the time-costs of curriculum integration via an asynchronous AI-TPACK module, the framework is currently undergoing verifica-tion interviews with a bilateral expert panel (n=8) from Germany and Türkiye. Ulti-mately, this framework provides a structured pedagogical approach, equipping in-structors to guide students in transforming machine hallucinations into legally builda-ble, tectonic realities. Sample videos showcasing student works are available in the Supplementary Materials.

Abstract: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder driven by complex interactions between protein aggregation, oxidative stress, neuroinflammation, and cellular dysfunction. Among plant-derived compounds, curcumin has emerged as one of the most extensively studied polyphenols due to its broad spectrum of biological activities. This review provides a critical synthesis of mechanistic, preclinical, and clinical evidence on curcumin in AD. Experimental studies consistently demonstrate that curcumin modulates key pathogenic processes, including neuroinflammatory signaling, oxidative stress, and amyloid-β aggregation, with more limited evidence for effects on tau pathology. While in vitro studies offer detailed mechanistic insights, in vivo models provide more integrated evidence, including improvements in cognitive performance and reductions in pathological markers. Despite this strong preclinical foundation, clinical evidence remains limited and inconsistent. Randomized controlled trials have not demonstrated clear therapeutic efficacy, with outcomes strongly influenced by formulation, bioavailability, and study design. Poor solubility, rapid metabolism, and limited brain exposure remain key translational barriers. In response, increasing attention has been directed toward formulation strategies and structurally related compounds. Emerging curcuminoids, such as bisdemethoxycurcumin (BDMC), are discussed as potential next-generation candidates. Preliminary evidence suggests that BDMC may modulate oxidative stress, autophagy, astrocyte senescence, and amyloid-related processes, although data remain largely preclinical. Overall, curcumin represents a mechanistically rich and preclinically promising multi-target compound, but with unresolved translational limitations. Future research should prioritize pharmacokinetic optimization, formulation-dependent validation, and exploration of novel curcuminoid strategies to bridge the gap between experimental findings and clinical application in AD.

Abstract: Bismuth tungstate (Bi2WO6) is a typical bismuth-based visible-light-responsive semiconductor photocatalyst that has attracted significant attention in the fields of environment remediation and energy conversion. In this paper, to address the issues of high photogenerated carrier recombination rate and limited visible-light response range of Bi2WO6, various modification strategies are highlighted, including morphology control, element doping, heterojunction construction, carbon material compositing, and coupling with functional materials such as MOFs, COFs, or conductive polymers. Furthermore, the structure-activity relationships are discussed. On this basis, the latest application progress of Bi2WO6-based photocatalysts in fields such as pollutant degradation, antibacterial activity, and energy conversion and storage is summarized. Finally, prospects are put forward regarding the existing shortcomings and future development directions in the application of Bi2WO6-based photocatalysts, aiming to provide a systematic theoretical reference for the design and application of high-performance Bi2WO6-based photocatalysts.

Abstract: A systematic experimental investigation was conducted on the motion response (RAO) and mooring performance of a novel disk-shaped buoy (geometric scale 1:10) subjected to combined wind, wave, and current actions. A hybrid experimental strategy was employed, integrating a large-scale wave flume (for long-period waves and currents) with a harbor basin (for short-period waves and wind), aiming to mitigate the scale effects inherent in Froude-scaled models, particularly with regard to drag force measurements. The test matrix included free decay in calm water, RAOs under regular waves, motion and mooring line tension under irregular waves, and measurements of wind and current drag coefficients. Key results indicate a natural roll period of approximately 3.0 s with a notably high dimensionless damping ratio (ζ ≈ 0.14–0.15), which is conducive to rapid motion attenuation. A pronounced resonance peak in the roll RAO (26.6°/m) was observed near the 3 s period. Under an extreme sea state (Hₛ = 13.8 m, Tₚ = 16.1 s), the maximum roll angle and dynamic mooring line tension reached 21.30° and 61.56 kN, respectively, the latter being about 3.0 times the static pretension. The mean wind drag coefficient and current drag coefficient were determined as 0.76 and 0.44. This research provides a validated dataset and critical insights for the design, mooring system optimization, and operational safety assessment of such disk-shaped buoys. The effectiveness of the hybrid testing approach is confirmed, and the favorable damping characteristic of this buoy form is highlighted.

Abstract: Agentic AI systems plan over time, call tools, write and retrieve memory, and coordinate across modules and services. Some of the most consequential failures in such systems are structural before they are behavioral: hidden coordination, trace-mediated lock-in, seam bottlenecks, and the silent erosion of meaningful override. This position paper argues that agentic AI systems should be evaluated and governed for \textbf{structural governability}, not only output alignment. By structural governability, we mean whether consequential coordination remains observable, attributable, interruptible, and steerable at the seams between components before irreversible commitments occur. Output-only evaluation does not capture this property. We propose an evidence ladder for structural risk in place of any single master metric: architecture-time priors over system structure, runtime coupling signals on telemetry graphs, and deeper state-regime diagnostics for high-stakes cases. We then sketch a research agenda for benchmarks that stress structure rather than terminal task success, reporting standards that disclose control geometry, and seam-level interventions including approval gates, permission freezes, trace decay, rollback, and subsystem isolation. The wider stake is cognitive integrity. Once agentic systems mediate what users retrieve, remember, delegate, and act upon, alignment depends on preserving the conditions under which users and operators can still understand, contest, redirect, and refuse those processes.

Abstract: Isavuconazole is increasingly being used for the treatment of invasive fungal disease (IFD), although real-world pediatric data remain limited. We retrospectively reviewed patients aged ≤18 years who received isavuconazole for fungal infection at two tertiary centers in Chile between 2021 and 2025. Twenty patients were included, with a median age of 13.7 years (IQR, 9.4–15.5); 14 (70%) had an underlying malignancy, and 6 (30%) were allogeneic hematopoietic cell transplant recipients. Isavuconazole was used for treatment in 16 patients and for prophylaxis in 4, predominantly as second-line therapy due to prior antifungal intolerance, inadequate response, drug–drug interactions, or QT prolongation. Fifteen patients had IFD, with Mucorales (n=3) being the most frequently identified pathogens in proven cases and pulmonary involvement predominating in probable IFD. A succesful response at 90 days was achieved in 60% (6/10) of evaluable cases. No breakthrough fungal infections occurred during treatment or prophylaxis. Hepatic enzyme elevations were observed in four patients. Isavuconazole was associated with favorable outcomes and an acceptable safety profile, supporting its use as an alternative antifungal in pediatric patients.

Abstract: This study examines the relationships between innovation, green management implementation, and their effects on business sustainability and value creation in Indonesian healthcare companies. Innovation is measured using Value Added Intellectual Capital (VAIC) efficiency, while green management implementation is proxied by Environmental, Social, and Governance (ESG) scores. Business sustainability is operationalized through carbon disclosure practices, and value creation is measured via Market Value Added (MVA). Using panel data from 30 listed firms (2019–2023) and applying fixed and random effects regression models showed that green management implementation significantly and positively influences carbon disclosure, supporting the role of ESG practices in enhancing sustainability transparency. However, innovation capacity demonstrates no significant direct effects on either carbon disclosure or market value creation, challenging conventional assumptions about intellectual capital's impact on sustainability and value performance. Component-level analysis improves explanatory power, showing that structural capital efficiency has a marginally negative effect on carbon disclosure, while social ESG scores emerge as the strongest driver of transparency. However, neither carbon disclosure nor ESG performance translates into immediate value creation, with MVA explained more by firm-specific characteristics and profitability (ROA). These findings extend intellectual capital and transparency theories by showing how social performance channels shape disclosure practices.