Abstract: Currency instability in emerging markets has become increasingly consequential for trade flows, investment allocation, and macroeconomic management. This study examines the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using two advanced econometric frameworks: the Family GARCH (fGARCH) model and the first-order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. As one of the most heavily traded emerging-market currency pairs, the ZAR/USD serves as a barometer of South Africa’s economic health and vulnerability to external shocks. Standard GARCH specifications, however, impose symmetry constraints that fail to accommodate the long-memory effects, distributional skewness, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by deploying the fGARCH and Beta-Skew-T-EGARCH frameworks on daily ZAR/USD returns spanning 5 January 2000 to 1 October 2024. The sGARCH and fGARCH specifications were assessed across five innovation distributions, Student’s t, skewed Student’s t (SSTD), generalised error (GED), skewed generalised error (SGED), and generalised hyperbolic (GH), with model fitness evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn criterion (HQ), and Shibata criterion (SIC), selecting the specification with the lowest combined penalty. The fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming the sGARCH benchmark. Among the covariates examined (day, month, trend, oil, platinum), the trend variable is the sole statistically significant predictor (p = 0.007), exerting a positive influence on ZAR/USD volatility. The two-component Beta-Skew-T-EGARCH model, by decomposing volatility into long-run structural and short-run transient components, delivers a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (-748.464826). Seven-day-ahead forecasts confirm that the two-component model captures declining conditional volatility, whereas the one-component model sustains persistently elevated estimates.

Abstract: Carcinoid heart disease is a progressive right-sided valvulopathy caused by serotonin and other vasoactive mediators released by metastatic neuroendocrine tumours. As oncological therapies have extended survival, cardiac disease has become a leading determinant of mortality. Operative mortality has decreased to 5–6% in contemporary high-volume centres, and long-term survival appears increasingly determined by tumour biology rather than cardiac disease when surgery is appropriately timed. The principal determinant of operative outcome is preoperative right ventricular function; symptom-based referral alone is insufficient because many patients remain compensated until ventricular dysfunction is advanced. This review synthesises the evidence on surgical timing, operative strategy, prosthesis selection, perioperative endocrine management, and emerging transcatheter options. Tricuspid valve replacement is required in the majority of patients, with concomitant pulmonary valve replacement advocated where concurrent disease is present. Bioprosthetic valves are preferred. Continuous perioperative octreotide infusion has substantially reduced the incidence of carcinoid crisis. Structured multidisciplinary decision-making integrating echocardiographic surveillance, biomarker monitoring, and oncological status assessment is essential.

Abstract: Primary cutaneous anaplastic large cell lymphoma (C-ALCL) is a CD30-positive T-cell lymphoproliferative disorder that can clinically resemble various non-melanoma skin cancers, making diagnosis challenging. Although histopathology remains the di-agnostic gold standard, non-invasive imaging modalities such as dermoscopy and re-flectance confocal microscopy (RCM) are increasingly used as complementary tools to support the differential diagnosis. To date, no data on RCM features of C-ALCL have been described. Herein, we report the case of an 80-year-old man presenting with a rapidly enlarging nodule on the lateral aspect of his right eyelid, providing a detailed account of dermoscopic and RCM findings integrated with clinicopathological correlation. Dermoscopy revealed a red-orange ho-mogeneous background with white streaks, rosettes, and polymorphic vascular struc-tures, while subsequent RCM (Vivascope 3000 probe) demonstrated marked architectural disarray of the epidermis and dermoepidemal junction, with prominent epidermal in-volvement characterized by aggregates of highly reflective cells. In the absence of al-ternative diagnostic patterns, these features raised suspicion for a cutaneous lym-phoproliferative disorder, which was later confirmed by histopathological and im-munohistochemical analyses. Overall, our findings support the value of RCM as a practical tool in guiding differential diagnosis and biopsy, particularly for rapidly growing lesions located in anatomically sensitive areas.

Abstract: This study investigates the effectiveness of truncating the EfficientNet-B0 architecture for the computer-aided diagnosis of tuberculosis (TB) on chest radiograph (CXR) images. A series of truncated EfficientNet-B0 models are proposed, systematically removing blocks to reduce model complexity while maintaining diagnostic accuracy. The B0(-3) model, which eliminates three blocks, emerges as a highly efficient configuration, achieving 100% internal test accuracy on the Kaggle dataset and demonstrating robust generalization to an external Mendeley dataset. Bootstrap analysis reveals that the B0(-3) model achieves a mean accuracy of 97.38% (95% CI: 96.94%–97.84%) on the external dataset, with performance statistically overlapping that of the complete B0(-0) model (97.24%, 95% CI: 96.78%–97.69%). Despite this overlap, the B0(-3) model uses 13 times fewer parameters, making it a more efficient alternative without sacrificing accuracy. These results highlight the potential of model truncation to improve efficiency while maintaining performance, positioning B0(-3) as a promising candidate for real-world TB detection.

Abstract: Ultrastrong magnetic fields, ranging from 100~T to 1,000~T, are generated exclusively by destructive pulsed magnets. While various generation methods exist, this review focuses on the Single-Turn Coil (STC) and Electromagnetic Flux Compression (EMFC) techniques, which provide optimal environments for high-precision measurements in materials science. First, we present recent technological breakthroughs in the EMFC method that have successfully achieved fields exceeding 1,000~T. We then describe specialized measurement infrastructures for magneto-optics, magnetization, and magneto-transport, highlighting the development of miniaturized all-plastic cryostats and custom sample holders designed for the dual extremes of cryogenic temperatures and megagauss fields. Representative physical phenomena revealed through these techniques are discussed, including quantum phase transitions in frustrated magnets, Aharonov--Bohm effects in carbon nanotubes, and semiconductor-to-metal transitions in strongly correlated systems. Furthermore, we address emerging measurement platforms such as magnetostriction, specific heat, and ultrasound velocity. Throughout this review, we emphasize the instrumentation and experimental refinements that ensure reliable data acquisition in the ultrastrong pulsed field regime.

Abstract: Digital Twin (DT) systems are revolutionizing modern industry by enabling real-time monitoring, simulation, and predictive control of physical assets. However, their widespread adoption in critical domains is contingent upon the trust and security they inspire. This paper presents a comprehensive survey of trust and security in DT systems, synthesizing recent advancements to bridge interdisciplinary gaps. We propose a novel taxonomy that categorizes trust into behavioral and non-behavioral dimensions and aligns these with the architectural layers of a DT. The survey meticulously analyzes the evolving threat landscape, detailing DT-specific vulnerabilities and their implications across diverse application domains. Furthermore, we explore current defense mechanisms, architectural models for secure data distribution, and privacy-preserving techniques such as federated learning and differential privacy. The paper also investigates trust-building strategies, including certification, explainable AI, and stakeholder-centric design. Finally, we identify critical open challenges and outline promising future research directions, including the need for unified trust metrics, lightweight security for edge DTs, and resilient, adaptive autonomy. This survey serves as a foundational reference for researchers and practitioners aiming to develop intelligent, connected, and inherently trustworthy digital twin ecosystems.

Abstract: For long-term and continuous monitoring of ECG signals, textile electrodes may be an option. In this study, woven stainless steel and silver copper-plated polyester fabrics were used to create electroconductive textile-based electrodes that were simultaneously tested against commercial Ag/AgCl electrodes. The ECG signals were recorded using the static and dynamic BIOPAC MP360 ECG data capture module (BIOPAC Systems, Inc., Goleta, CA, USA). Using the algorithmic features retrieved for each electrode, sensor characterization involved ECG monitoring, surveys on the comfortability of human participants, and wash ability effect evaluation. Under both static and dynamic conditions, the obtained ECG signal waveform was observable for each electrode. Based on the signal shape, HR, and R-R interval, the ECG signals recorded while the participants were running on a treadmill machine were evaluated and compared. The findings showed that signals acquired using all electrodes had visible P, QRS, and T waves but that under both static and dynamic conditions, silver copper-plated polyester textile electrodes had a greater R-peak amplitude (1.28 mV) than did standard Ag/AgCl electrodes and stainless-steel textile electrodes. The signals were distorted slightly during running, which could have been caused by shaky skin-electrode contact.

Abstract: Delayed discharge represents a persistent challenge in healthcare systems, contributing to inefficiencies in hospital bed utilization, increased costs, and reduced patient flow. In small and centralized healthcare systems, these effects may be further amplified due to limited post-acute care capacity and restricted patient redistribution. This study quantified the prevalence of inappropriate hospital days as a proxy for delayed discharge and examined their relationship with patient demographics, medical specialty, and associated costs in an acute general hospital. A quantitative retrospective analysis of 220 medical records was conducted using a modified Appropriateness Evaluation Protocol (AEP). Descriptive statistics and non-parametric tests were applied to identify significant associations between inappropriate hospital days and selected variables. The results showed that approximately 50% of inpatient days were inappropriate, with most delays attributed to waiting for long-term care and rehabilitation placement. Age and medical specialty were significantly associated with delayed discharge, while no consistent relationship was observed with gender. Cost analysis indicated a substantial financial burden, with annual estimates exceeding €2.5 million for the units studied. These findings suggest that delayed discharge is driven by a combination of external capacity constraints and internal operational inefficiencies. The study highlights the need to strengthen post-acute care provision, improve discharge coordination processes, and enhance system integration to optimize hospital efficiency and patient flow.

Abstract: This paper explores the role of dimensional analysis as the fundamental grammar that decides which physical expressions can be meaningfully compared before any dynamics is established. We develop this grammar inside the Finite Ring Cosmology framework. In this setting, spatial and temporal dimensions arise as frame readings of a finite symmetry space of admissible reference frames, while conventional units such as metres and seconds enter as observer-assigned modular domains. The shell structure itself is invariant under changes of observer frame and unit convention, although its numerical values change with the chosen scale. The resulting unit-domain algebra reproduces the familiar rules of dimensional analysis: quantities can be added only within the same domain, products and ratios move between domains, and physical invariants are precisely the expressions with neutral total domain. The construction gives a finite-domain reading of the constants that connect mechanics, quantum phase, and gravitation. The speed of light appears as a finite, observer-invariant upper boundary relating spatial and temporal scale assignments: its numerical value changes with units, but the boundary does not. The Planck relation forces energy to share the temporal recurrence domain: h converts frequency measured relative to the chosen time scale into frequency measured relative to the complete phase cycle, not into a separate modular unit domain. The mass domain is derived from energy and speed, and Newton’s constant is identified as the conversion domain for gravitational geometry.

Abstract: During HIV infection, the immune system initiates an immune response to control the viremia. In this in vitro study, we investigated the immunomodulatory and anti-HIV potential of a traditional medicine formulation, Product Nkabinde (PN). A freeze-dried extract of PN was used to determine cytotoxicity in MT4 cells. Non-cytotoxic doses were used to evaluate the immunomodulatory and anti-HIV effects of PN using neutralization, prophylactic and treatment approaches. Post-treatment, cytokine quantification and p24 detection were performed. In the neutralization strategy, PN restored IL-1α (p = 0.0389) and IL-10 (p = 0.0443) to levels of uninfected cells. It also reduced HIV-induced elevations in IL-8 (p = 0.035), IP-10 (p = 0.0886), and MCP-1 (p = 0.0733) compared to HIV-infected cells. In the prophylactic approach, HIV infection upregulated IL-1α (p = 0.676), which was suppressed by PN. In the treatment strategy, PN reversed the elevated levels of IL-1α (p = 0.049). IL-10 was fully restored by PN to levels of uninfected cells. In all strategies, PN induced a significant and dose-dependent decrease in HIV replication. These findings demonstrate that PN exerts potent immunomodulatory effects all strategies used by reversing HIV-induced cytokine dysregulation, thereby inducing significant anti-HIV effects.

Abstract: Background/Objectives: Lung cancer survival depends on early detection; however, in the Philippines, high radiologist workloads and the anatomical complexity of chest X-rays (CXRs) contribute to missed pulmonary nodules and false-negative diagnoses. This study aims to develop an enhanced deep learning model to improve nodule classification and localization sensitivity. Methods: We propose RNNet-MST, an extension of ResNet-50 that incorporates Multi-Scale Transformer blocks for global context modeling and a custom spatial attention mechanism for attention-based weak localization of disease-relevant regions. The model was trained and evaluated on the NODE21 chest X-ray dataset and compared with a baseline ResNet-50 using classification metrics, with attention maps used for weak localization analysis. Results: RNNet-MST demonstrated consistent improvements over the baseline ResNet-50 across evaluated metrics. Mean Nodule Recall improved from 88.02% ± 1.92% to 91.55% ± 1.41%, reducing false negatives. Mean Test Precision reached 90.46% ± 0.99%, and mean Nodule F1-Score improved to 90.99% ± 0.39%. On the isolated small-nodule subset, RNNet-MST achieved a 12.3% improvement in sensitivity over the baseline. Conclusions: The integration of multi-scale transformer features improved classification sensitivity, while the attention mechanism provided weak localization cues that aligned more closely with annotated nodule regions than the baseline. RNNet-MST shows potential as a diagnostic support tool, warranting further validation on larger and more diverse clinical datasets to reduce perceptual errors and facilitate early lung cancer detection in resource-constrained settings.

Abstract: Drug recall is a critical regulatory mechanism implemented to protect public health by removing defective, unsafe, or non-compliant pharmaceutical products from the market. Despite stringent regulatory approval processes, issues related to manufacturing defects, contamination, labeling errors, stability failures, and post-marketing safety concerns may lead to drug recalls. Regulatory authorities across the world, including the Central Drugs Standard Control Organization (CDSCO), the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMA), and other national agencies, have developed structured recall guidelines and rapid alert systems to ensure timely withdrawal of defective products. Drug recalls are typically classified based on the level of health risk and may be executed at different levels of the distribution chain, including wholesale, retail, and consumer levels. Effective recall management involves risk assessment, recall communication, product traceability, documentation, and recall effectiveness checks. Pharmacovigilance systems also play an important role in identifying adverse drug reactions and quality defects that may lead to product recalls. This review article provides a comprehensive overview of drug recall systems, including causes of recalls, regulatory frameworks in India and other countries, recall classification, recall procedures, rapid alert systems, and global recall trends. The article also discusses challenges in recall implementation and provides recommendations to strengthen drug recall systems and regulatory coordination worldwide.

Abstract: This paper presents a comprehensive comparative analysis of recent advances in smart bone prosthetics. The emphasis is made on the integration of embedded sensors, adaptive control systems, and wireless monitoring into metallic, carbon-based and bioceramic materials. The evaluation of essential characteristics of mechanical strength, durability, and biocompatibility is combined with its integration of smart functionality. The key mechanical properties, such as tensile strength, Young’s modulus, and fatigue life, are reviewed to assess how each material supports long-term prosthetic performance. Concurrently, biocompatibility factors, tissue integration and inflammatory response are examined to ensure safe and effective clinical application. The integrative approach can help clinicians and biomedical engineers to fine-tune the selection of the optimal material-smart system and provide individually tailored combinations to specific patient needs and surgical-operative contexts.

Abstract: The experimental determination of the relationships between the stress distribution zone in the soil layer and the parameters of tillage working bodies is a labor-intensive process. Therefore, preliminary mathematical modeling of this process is recommended to mi-nimize the total number of experiments. The research was conducted using the principles of classical mechanics and soil mechanics. Using an equation proposed by J. Boussinesq,a graphical-analytical method was developed to evaluate the stress state in the soil layer induced by a dihedral wedge. This method incorporates both the geometric parameters of the dihedral wedge and the physico-mechanical properties of the soil. A direct pro-portional relationshipwas established between the length of the dihedral wedge and the total area of the deformed soil mass. Specifically, increasing the length of the dihedral wedge by 83% (from 0.05 to 0.30 m) resulted in an 80% increase in the area of the de-formed soil mass (from 0.02 to 0.10 m²). The proposed graphical–analytical method can be employed in the design of tillage implements.

Abstract: Detecting distributional drift is central to reliable inference in evolving systems, yet existing approaches treat it as a static discrepancy measured by fixed divergence functionals. We introduce an information-geometric approach grounded in the Kerimov–Alekberli (KA) framework, where drift is modeled as a non-equilibrium trajectory on a curved statistical manifold. Three contributions are presented. First, we establish a formal impossibility result: no fixed divergence can achieve uniform optimality under non-stationary dynamics (Theorem 3.1). Second, we connect drift detection to entropy production, linking statistical inference with physical irreversibility (Theorem 4.1). Third, we introduce an asymmetric entropy operator A(θ) into the KA drift equation—a directional term that amplifies early entropy signals through a skew-symmetric perturbation of the gradient flow. Validated by 50 Monte Carlo runs on non-stationary Gaussian processes, adaptive divergence achieves mean detection at step 113 ± 11 vs 117 ± 13 for fixed KL (reference onset at step 100), demonstrating no-regret behaviour: the method is never significantly worse than the best fixed metric, while outperforming scalar baselines (ADWIN, Page-Hinkley) by wide margins (p < 10⁻⁴). These results motivate the principle of adaptive divergence, whereby the notion of distance between distributions must itself evolve in response to system dynamics.

Abstract: The increasing presence of microplastics (MPs) in freshwater ecosystems poses significant threats to aquatic biota; yet, species-level information on the presence of MPs in Indian riverine ecosystems is scarce. This study assessed 220 fish samples from twelve species and various trophic levels for MP ingestion, organ-level accumulation, polymer type, and ecological risk at four locations along the River Yamuna in India. MPs were detected in all the studied species and organs, indicating their widespread distribution across various ecological habitats and trophic levels. A total of 1,678 MPs were quantified, which were significantly higher in fish from urban Delhi compared to upstream regions. The gastrointestinal tract had the highest MP concentrations (751), followed by gills (605) and muscle tissues (322), thus confirming ingestion as the primary route of MP uptake and their subsequent translocation into internal organs. Fibers dominated the MP community (>78%), with transparent (44%) and blue (19.5%) particles being the most abundant. ATR-FTIR analysis revealed the presence of ten different polymers, with polyethylene (≈24%) and polypropylene (≈21%) contributing to approximately 45% of MPs. Significant organ-level correlations (r/ρ = 0.635-0.958) and spatial variability (Kruskal-Wallis, H = 11.03, p = 0.011) indicated coordinated MP accumulation influenced by urban pollution. The Polymer Hazard Index analysis revealed a high PHI value (Category IV), mainly contributed by the widespread distribution of highly toxic polymers such as polycarbonate and polyimide.

Abstract: Traditional European craft practices face dual pressures: the erosion of tacit knowledge held by aging practitioners, and the risk of cultural homogenization through uninformed digital adoption. This paper presents a comparative analysis of a structured design pilot conducted across five Representative Craft Instances (RCIs): glassblowing, tapestry, marble/silversmithing, porcelain, and woodcarving within the Horizon Europe CRAEFT project. Drawing on co-creative workshops, motion capture pipelines, physically based rendering (PBR), interactive simulation, and additive manufacturing, we analyze how context-specific digital tools performed as mediators rather than modernizers across heterogeneous craft domains. Cross-domain analysis reveals that digital tools achieve cultural legitimacy only when introduced through co-creative, practitioner-led cycles; that gesture and tacit knowledge are transferable via structured computational pipelines; and that methodological portability, not workflow replication, is the appropriate model for cross-context scaling. Implications are discussed for sustainable heritage policy, design education, and the development of craft-sensitive digital infrastructure in Europe.

Abstract: Background: Sickle cell disease (SCD) is the most common inherited blood disorder globally, affecting approximately 300,000 newborns annually. Cardiac remodeling, resulting from chronic anemia, vascular obstruction, and endothelial dysfunction, substantially contributes to morbidity and mortality in SCD. Apprehending these patterns is important for guiding clinical management and enhancing outcomes in pediatric patients. However, a comprehensive summary of cardiac remodeling patterns in pediatric and adolescent SCD, and their associations with genotype and clinical severity, is lacking. Methods: We conducted a systematic review following PRISMA 2020 guidelines, searching five databases for studies published from January 1978 to December 2024. Of 1,131 records, 37 studies met the inclusion criteria: 31 focused exclusively on children (Group A), and 6 included both children and adults (Group B). We analyzed cardiac remodeling patterns, genotype-specific findings, associations with disease severity, and imaging modalities. The 37 studies included approximately 4,847 patients from 18 countries, covering varied populations and imaging techniques. Left ventricular (LV) dilation was the most frequent finding (89.2%), followed by diastolic dysfunction (48.6%), pulmonary hypertension or elevated tricuspid regurgitant jet velocity (TRV; 37.8%), myocardial fibrosis (8.1%), and arrhythmia (2.7%). The HbSS genotype was associated with the most severe cardiac changes. Markers of disease severity, such as elevated lactate dehydrogenase (LDH), frequent acute chest syndrome (ACS), and increased hospitalizations, were strongly correlated with more pronounced cardiac remodeling. Variability in study design and imaging modalities underscores the requirement for standardized assessment protocols to enhance comparability and clinical translation. Cardiac remodeling in SCD starts early, even in infancy, and progresses with age. Recognizing this early can prompt healthcare professionals to prioritize rapid interventions and point out the importance of early monitoring and management.

Abstract: Early identification of corrosion-prone conditions remains a major maintenance challenge in closed, hard-to-access structural zones. This paper presents a multi-sensor data fusion approach for early warning of corrosion-prone conditions in selected closed zones of a medical rescue aircraft, as part of a structural health monitoring framework. The study combines sensor selection, installation in restricted-access compartments, and analysis of in-service data collected during helicopter operation. The workflow includes data acquisition, preprocessing, feature extraction, fused interpretation of multi-channel data, and assignment of warning levels linked to maintenance actions. Environmental, conductance, and electrochemical channels provide a first-stage early-warning layer that indicates persistent conditions favorable to long-term corrosion development, rather than direct proof of existing damage. Persistent warning states are intended to trigger staged follow-up diagnostics: PZT sensing localizes suspect subregions, while eddy-current sensing verifies and monitors the growth of local metallic degradation. Field inspection evidence of corrosion in hidden zones supports the practical relevance of this approach. Although demonstrated on an aircraft, the methodology is transferable to other closed or poorly accessible structural zones, including civil engineering applications.

Abstract: Mobile edge computing (MEC) enables computation-intensive and latency-sensitive tasks to be offloaded from mobile devices to nearby edge servers. Most existing MEC task offloading studies formulate offloading as a selection problem over a fixed or fully available set of candidate servers, which is restrictive in heterogeneous MEC scenarios with task-node eligibility constraints. Under such constraints, a task can be processed by an edge server only when task attributes, service requirements, link conditions, and node states jointly satisfy the corresponding eligibility conditions. The feasible action set therefore varies over time, while offloading decisions are further coupled with local queueing competition and long-term load evolution. To address this problem, this paper proposes RoSCo, a load-aware task offloading method with scheduling and constraint coordination for eligibility-constrained MEC systems. RoSCo constructs a dynamic feasible action set, applies eligibility-aware action masking to exclude infeasible offloading actions, introduces priority-driven local coordination to characterize service competition among heterogeneous tasks, and designs a load-responsive reward to guide congestion mitigation and load balancing. The offloading policy is learned using a dueling double deep Q-network (D3QN). Simulation results show that RoSCo reduces task drop rate and edge-node load imbalance while maintaining competitive task completion delay and energy consumption, especially under high-load and sparse-eligibility conditions.