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.

Abstract: RNA-level regulation provides a therapeutically actionable bridge between genomic variation and disease mechanism. Although clinical genomics has traditionally prioritised variants that alter protein-coding sequence, many pathogenic variants act by disrupting RNA processing, stability, localisation, translation, editing, or surveillance. These mechanisms determine whether a transcript is correctly spliced, degraded, translated, toxic, or therapeutically recoverable. This review examines RNA-level mechanisms in human disease with emphasis on their implications for precision therapeutics. Splicing defects, pseudoexon inclusion, untranslated-region variants, premature termination codons, stop-loss variants, RNA-binding protein dysfunction, non-coding RNA dysregulation, altered codon usage, ribosome stalling, and surveillance pathways such as nonsense-mediated decay, nonstop decay, and no-go decay all create distinct therapeutic opportunities. Importantly, treatment selection must be mechanism-matched: splice defects may require antisense-mediated correction or small-molecule splice modulation; toxic transcripts may require antisense oligonucleotides (ASOs) or siRNA-mediated silencing; haploinsufficiency may require mRNA replacement, gene replacement, or transcript rescue; premature termination codons may be considered for readthrough only when the transcript and protein context are favourable; and regulatory RNA defects require network-aware approaches. RNA sequencing, long-read transcriptomics, allele-specific expression analysis, and functional assays are therefore essential not only for diagnosis but also for therapeutic stratification. An RNA-centric framework moves variant interpretation beyond descriptive classification toward mechanism-based intervention and precision RNA medicine.

Abstract: This article describes the development of a compact and affordable variable temperature NMRinstrument designed primarily to measure dynamic molecular motions in solids and liquids. The instrument consists of Lab-Tools’ Mk4 palm-top time-domain NMR spectrometer fitted with a Peltier-cooled variable temperature probe inside a shimmed Halbach magnet. Measurement of NMR relaxation times T₁, T₂, T_1ρ are possible over the temperature range −20^◦C to 70^◦C with cooling and heating rates, and data acquisition controlled from an integrated mini-PC. The overall footprint of the instrument is roughly that of a shoe box making both in-the-field and bench-top measurements possible. Applications of this instrument include measuring the pore size distribution in porous rocks, the viscosity of oils and tars trapped in porous rock, the properties of polymers, and the viscosity of the liquid components of foods (e.g. fruits, vegetables and seeds). Results of test measurements on calibrated oils and olive oil are presented together with measurements of the molecular mobility in a sold polymer.

Abstract: Professional development in discipline-specific pedagogy remains a persistent challenge in teacher education, particularly when addressed in fully online, asynchronous settings with in-service practitioners. This article describes and theorises the design of Specific Didactics of Biology and Geology, a 14-week professional development course offered at Universidade Aberta, Portugal’s public open distance university, within the framework of the institution’s Pedagogical Model. The course targets in-service Biology and Geology teachers enrolled in the first year of a teacher professionalisation programme. Rather than reporting a completed impact evaluation, the article offers a theoretically grounded, design-oriented practitioner inquiry account of course design, early implementation reflections, and transferable design principles for asynchronous online teacher professional development. Its conceptual core is the framing of teaching as didactic decision-making, integrating reflective practice, didactic transposition, and experiential learning theory into a coherent design philosophy that places participants’ existing professional experience at the centre of learning.The article argues that subject-specific professional development can be meaningfully designed in a fully asynchronous online format, provided that the e-activity repertoire is epistemically varied, experientially anchored, and coherent with the pedagogical principles under study. Three structural features are discussed in detail: a two-tier activity design distinguishing non-assessed preparatory tasks from assessed e-activities; a deliberately diverse typology of digital activity formats, each serving a distinct cognitive and social function; and an integrative culminating task, the Professional Development and Pedagogical Action Plan, developed progressively across the final three weeks of the course.By describing the course design in sufficient detail to enable adaptation to comparable contexts, the article contributes to research-informed approaches to online teacher professional development. It highlights the pedagogical potential of asynchronous learning environments when they are designed not as simplified substitutes for face-to-face provision, but as coherent spaces for reflective, situated, and discipline-specific professional learning.

Abstract: Relation extraction is an important task for structuring information from unstructured text. However, Romanian language still lacks dedicated datasets and benchmarks for this task. To address this gap, we introduce RoRED, a Romanian relation extraction dataset built by combining two complementary data construction strategies: translating existing high-quality English resources and applying distant supervision to native Romanian Wikipedia data. We leverage a powerful open-source large language model to automatically translate English examples into Romanian. For the native subset, we align Romanian Wikipedia entities with Wikidata relations to obtain naturally occurring Romanian examples. To better reflect real-world relation extraction scenarios, we also introduce synthetic negative examples generated using existing Romanian named entity recognition models. Finally, we validate the dataset by fine-tuning and evaluating multiple baseline models. Our strongest model, LUKE-RoRED, achieves a macro-F1 score of 0.8744 on the RoRED test set, demonstrating that the dataset can support relation extraction for Romanian. Overall, RoRED provides a strong first native benchmark for Romanian relation extraction.

Abstract: This project checks methods in wind power forecasting by comparing Gregorian calendar based on seasonal alignments with the vedic lunisolar calendar parallely. Rather than using timestamps like most forecasting methods, this project seeks to determine whether periodic cycles based on nature’s cosmos could reveal correlational patterns of wind activity surges and enhance accuracy. This study exploits the SOLETE dataset from SYSLAB, Denmark, which consists of 15 months of power generation alongside weather data. The dataset underwent processing with the CleanTS tool (an R package) and it was transformed into Gregorian and Vedic time frameworks. Within both time frameworks, the forecast approaches a hybrid forecasting model integrating “Variational Mode Decomposition (VMD) with Gaussian Process Regression (GPR)” was designed and assessed [11][12 ]. The Vedic forecasting approach is slightly better as it gives RMSE of 2.5519 and MAE of 2.0763, while the Gregorian forecasting approach gives RMSE of 2.6123 and MAE of 2.1424. The MAE correlation analysis over months revealed differing patterns within the two forecasting approaches with vedic giving better correlation than gregorian. This suggests that the Vedic calendar forecasting approach is better than the gregorian calendar system, which is based on natural cycles and is lunisolar, it is more accurate in capturing the chaotic signal of wind patterns than the arbitrary gregorian forecasting approach. This project helps in research, questioning the standard time representation in forecasting models which uses the gregorian timestamps and gives idea that if we put natural cycles through alternative calendar systems will it enhance the accuracy of energy predictions, potentially updating grid integration and operational planning.

Abstract: Infective endocarditis (IE) is a severe pathology with recent changes in its epidemiological profile, characterised by older patients with more comorbidities. The objective of this study is to describe the clinical and microbiological characteristics, as well as potential relations with mortality, of patients with IE in a tertiary academic centre. Material and Methods: Descriptive, retrospective, and observational study of patients over 18 years of age with a confirmed diagnosis of IE, conducted between 2021 and 2023 at the Dr Hernán Henríquez Aravena Hospital in Temuco, Chile. Biodemographic variables, risk factors, microbiology, echocardiographic findings, and complications were analysed using descriptive statistics and logistic regression models. Results: 119 patients were included (average age 60 years; 65.5% male; 28.5% rural). The most frequent risk factors were arterial hypertension (55%) and diabetes mellitus (29%). 18% were on haemodialysis (HD). Microbiological isolation was achieved in 78.1% of cases, with Streptococcus gallolyticus the most frequent isolate (16.8%), followed by Staphylococcus aureus (15.1%) and coagulase-negative Staphylococcus (15.1%). Complications were present in 69% of cases, mainly emboli (43%) and septic shock (23%)—59.6% required surgery. Global mortality was 44.5%, with a decreasing annual trend (from 58% in 2021 to 33% in 2023). Independent predictors of mortality were chronic renal failure on HD (OR 5.76; p = 0.001), heart failure (OR 3.13; p = 0.025), and septic shock (OR 3.31; p = 0.016). Conclusions: IE in this centre presents an aggressive profile and a high burden of comorbidities. The prevalence of S. gallolyticus stands out, possibly associated with high regional rurality. Mortality remains high, although it is improving.

Abstract: Quantum-like models of cognition account for order effects, conjunction and disjunction fallacies, and contextuality in human decision data using the Hilbert-space formalism without claiming literal quantum processes in the brain. Two decades of theoretical development have produced a mature mathematical apparatus, but its empirical foundation rests almost entirely on human-subject paradigms that are subject to linguistic priming confounds, demand characteristics, and replication concerns. This paper proposes that engineered brain-organoid preparations on multielectrode arrays—specifically the Cortical Labs CL1 and DishBrain-class systems—constitute the first substrate on which the structural commitments of quantum-like cognition can be tested without these confounds. I specify four operational signatures (sequential-stimulation order effects, Contextuality-by-Default cyclic-system inequalities, response replicability under non-invasive measurement, and interference effects in combined stimulation), and characterize, for each, the formal observable, the discriminating prediction against classical adaptive-learning baselines, and the substrate-level constraints imposed by current commercial wetware. The paper is offered as a theoretical specification, not an experimental protocol, and is calibrated for falsifiability rather than confirmation: a positive result on any signature would constrain classical models of organoid learning without confirming quantum-like dynamics; a fully negative result would narrow—though not conclusively delimit—the empirical scope of the quantum-like cognition program, with one natural reading being that these signatures depend more strongly on linguistic, pragmatic, or task-structured features of human-subject paradigms than on generic neural substrate dynamics.