Abstract: Inadequate surface sanitization represents a significant risk to sterility assurance and regulatory compliance. Therefore an effective cleaning and disinfection programme is a critical component of contamination control strategies in pharmaceutical facilities manufacturing sterile medicinal products. This study aimed to standardize a carrier-based methodology for evaluating the efficacy of disinfectants against in-house environmental isolates recovered from a pharmaceutical industry facility. Nine representative strains (six bacteria and three fungi), selected based on historical environmental monitoring data (2012–2022), were characterized using matrix-assisted laser desorption/ionization - time-of-flight / mass spectrometry (MALDI-TOF MS) and molecular sequencing (16S rRNA or D2 LSU rDNA). Disinfectant efficacy was assessed on stainless-steel and low-density polyethylene surfaces using NF T 72-281:2014 with adaptations, testing alcohol 70%, sodium hypochlorite 0.5%, quaternary ammonium 0.05%, peracetic acid 0.5%, and accelerated hydrogen peroxide wipes. All agents demonstrated ≥5 log₁₀ reductions against vegetative bacteria and fungi on both surfaces. However, variable sporicidal performance was observed, particularly for one Bacillus cereus group strain (B1342/15), which showed limited reduction on stainless-steel. These findings highlight inter-strain variability and the greater tolerance of surface-associated spores. The study reinforces the importance of carrier-based testing using in-house isolates to ensure realistic validation of disinfectants and to strengthen microbiological risk management within pharmaceutical contamination control strategies.

Abstract: In the marine navigation environment, static obstacles such as shallow waters, islands, and restricted zones coexist with dynamic threats like typhoons. Rapidly planning safe, shortest routes is crucial for ensuring vessel and personnel safety while enhancing navigation efficiency. However, existing path planning algorithms face limitations when addressing dynamic threats like typhoons, struggling to achieve an effective balance between efficiency and effectiveness. To address this, this study proposes an improved Time-Dynamic Theta algorithm (TDM-Theta*) based on the Theta algorithm. By incorporating wave height as a key constraint, it comprehensively evaluates the actual impact of dynamic marine environments on routes, thereby efficiently planning safe, shortest paths that proactively avoid typhoon impacts. Through testing and analysis of eight case studies across three typical scenarios, this algorithm demonstrates high efficiency and effectiveness in planning the shortest safe routes across diverse operational environments. The research findings provide theoretical foundations and methodological support for intelligent planning of safe vessel routes.

Abstract: Despite the availability of antibiotics, pulmonary tuberculosis (TB) remains a leading infectious cause of mortality globally. Treatment failure and the emergence of drug-resistant strains are largely driven by the heterogeneous architecture of caseating granulomas and the complex biophysical mechanisms by which Mycobacterium tuberculosis (Mtb) evades host immunity. Highly lipophilic frontline drugs, such as bedaquiline and clofazimine, exhibit severe sequestration within the lipid-rich necrotic caseum, preventing them from reaching the dormant persister bacilli at the lesion's core. Furthermore, recent biophysical discoveries reveal that Mtb utilizes extracellular vesicles and specialized lipids to mechanically stiffen host macrophage membranes, thereby arresting phagosome-lysosome fusion. This review proposes an AI-optimized, "Trojan Horse" hybrid nanocarrier strategy—comprising a lipidic core, a mucoadhesive chitosan shell, mannose-targeted ligands, and pH-responsive release mechanisms—to bypass these dual barriers. By bridging lesion-centric pharmacokinetics ( , ), novel bioorthogonal diagnostic probes, and machine learning formulation designs, we present a translational roadmap aimed at achieving complete sterilization of caseous cavities.

Abstract: Changes in land use and land cover (LULC) are among the leading contributors to global envi-ronmental transformation. Analyzing these dynamics is essential for understanding historical land utilization patterns and identifying the key drivers behind such shifts. This research focuses on LULC changes in the Kunar region of eastern Afghanistan. To classify the LULC types, the study area was divided into nine major classes using the Support Vector Machine (SVM) algorithm, based on Landsat 07 Enhanced Thematic Mapper Plus (ETM+) data for 2004 and Landsat 8 Oper-ational Land Imager (OLI) data for 2014 and 2024. Past and present changes were evaluated using ArcGIS 10.8, while future scenarios for 2034 and 2044 were simulated using the Land Change Modeler (LCM) embedded in the TerrSet platform, combined with the Cellular Automata–Markov Chain (CA-MC) model with 90% kappa agreement validation value. From 2004 to 2024, grassland expanded significantly from 68.93% (3,406 km2) to 73.94% (3,654 km2). Built-up areas grew from 0.59% (29.10 km2) in 2014 to 1.02% (50.39 km2) in 2024. Conversely, dense forest cover declined from 27.50% (1,358.90 km2) to 22.96% (1,134.75 km2), a decrease of 224.15 km2. Barren land, after a temporary increase, also showed a net decline. Projections for 2034 and 2044 suggest a further reduction in forested areas to 1,077 km2, while grasslands and urbanized zones are ex-pected to increase to 3690 km2 and 60.63 km2, respectively. These trends emphasize a swift transi-tion in land use patterns, primarily driven by the conversion of forested and barren landscapes into settlements and grasslands. The findings underline the urgent need for implementing sus-tainable land management strategies to curb environmental degradation and ensure balanced land resource utilization in the future.

Abstract: Efficient irrigation management is critical for increasing water production and providing high-quality planting material in fruit tree nurseries. This study looked at how four different irrigation depths (0, 10, 20, and 30 mm each irrigation event) affected graft establishment, nursery survival rate, total water consumption, and irrigation water productivity in peach (Prunus persica (L.) Batsch). Field studies were carried out in a commercial nursery in northeastern Romania over two consecutive growth seasons, with two cultivars ('Redhaven' and 'Cresthaven') and four fertilization levels in a factorial design. Irrigation considerably increased graft take and the number of marketable nursery trees compared to rainfed circumstances. Moderate irrigation (20 mm per irrigation event) resulted in the highest nursery survival rate and water efficiency. Higher irrigation inputs increased total water use, but reduced irrigation water productivity. Regression analysis revealed nonlinear connections between water consumption and nursery performance, implying that productivity advantages drop with increasing irrigation levels. The findings suggest that moderate watering can boost nursery yield while conserving water. These findings offer practical recommendations for irrigation management in commercial peach nursery production systems.

Abstract: Visual field testing is critical in diagnosing and managing glaucoma, which affects over 80 million people worldwide, as well as other ocular diseases and stroke. Visual field test perimeters detect blind spots (scotomas) and map the boundaries of a patient’s visual field. Standard perimetry machines are accurate, but prohibitively expensive and inaccessible in low-resource settings, while traditionally accessible forms of perimetry are less reliable. Virtual reality (VR)- based perimetry systems offer a potential low-cost, accessible alternative. This study reviews the development, clinical potential, and accuracy of VR perimetry. Findings showed that VR-based perimetry systems were comparable to, and in some cases even more effective than, standard perimetry tests, but these results were not universal, suggesting that greater standardization of testing protocols is necessary.

Abstract: The main aim of this study was to investigate the association between areas of work life and engagement in the development of burnout syndrome in self-employed and sub-ordinate psychologists. Using a cross-sectional approach with quantitative and qualitative elements, three scales validated for the Brazilian population were applied: Burnout Assessment Tool (BAT), Areas of Worklife Survey (AWS) and Utrecht Work Engagement Scale (UWES). 180 psychology professionals took part, with a predominance of females (88.3%) and a majority aged between 24 and 29. The results revealed a strong negative correlation between the BAT domains and aspects assessed by the UWES, confirming the inverse association between engagement and burnout. In addition, the positive associa-tion between areas of work life and engagement was confirmed. However, there was no confirmation of the negative association between areas of work life and burnout, and no evidence was found that these areas act as mediators in the relation between engagement and burnout. Thus, although the areas of work life have been shown to influence en-gagement and burnout independently, their role as mediators in this relationship has not been confirmed. Although some hypotheses were confirmed and significant associations were found, the research also encountered unexpected results and limitations that deserve to be considered in future investigations such as this one.

Abstract: The Riemann Hypothesis, one of the most celebrated open problems in mathematics, addresses the location of the non-trivial zeros of the Riemann zeta function and their profound connection to the distribution of prime numbers. Since Riemann’s original formulation in 1859, countless approaches have attempted to establish its truth, often by examining the asymptotic behavior of arithmetic functions such as Chebyshev’s function θ(x). In this work, we introduce a new criterion that links the hypothesis to the comparative growth of θ(x) and primorial numbers. By analyzing this relationship, we demonstrate that the Riemann Hypothesis follows from intrinsic properties of θ(x) when measured against the structure of primorials. This perspective highlights a striking equivalence between the distribution of primes and the analytic behavior of ζ(s), reinforcing the deep interplay between multiplicative number theory and analytic inequalities. Beyond its implications for the hypothesis itself, the result offers a fresh framework for understanding how prime distribution governs the analytic landscape of the zeta function, thereby providing new insight into one of mathematics’ most enduring mysteries.

Abstract: Remote sensing (RS) air pollution and greenness investigations provide new information about the risk factors and physiologic mechanisms that change stroke subtype occurrence and risk. Five electronic literature databases (PubMed, Scopus, Embase, Web of Science, Google Scholar) were searched to identify 99 unique published research studies that used RS to evaluate the contribution of ambient air pollution and greenness to four stroke subtypes (cerebrovascular, stroke, ischemic, hemorrhagic). Agreement between ambient aerosol optical depth (AOD)-PM2.5 concentration level values and ambient PM2.5 air monitor measurements was 75.1% (95% CI=72.0-%-78.2%). Record analyses demonstrated the significant contribution of higher ambient AOD-air pollution concentration level values to increased stroke subtype occurrence and risk, but significant protection was rendered by increased RS greenness spatial coverage to decreased stroke subtype occurrence and risk. Environmental risk factors were significantly associated with each stroke subtype. There were significant differences between the eight risk factors and stroke subtype combinations. Single and multiple physiologic mechanisms of immune, inflammation, oxidative stress, and other sources were uniquely associated with individual and multiple stroke subtypes. Literature review findings were used to develop a descriptive-explanatory stroke subtype physiologic mechanism model. The model can assist in the development and implementation of population-based stroke subtype intervention programs.

Abstract: The rapid advancement of artificial intelligence (AI) presents unprecedented challenges for labor market forecasting, requiring fundamental methodological innovations that move beyond traditional extrapolation techniques. This policy paper proposes comprehensive enhancements to the U.S. Bureau of Labor Statistics (BLS) employment projection systems to better capture and forecast AI's impact on employment structures, job roles, and workforce skill requirements. Drawing on recent empirical research and the bureau's existing methodological frameworks, we present an integrated architectural framework that combines task-based exposure modeling, real-time data analytics, causal inference methods, and enhanced gross flows estimation. Our recommendations address critical gaps in current BLS methodologies identified through systematic literature review and analysis of emerging AI adoption patterns, including the distinction between automation and augmentation effects, the nonlinear dynamics of AI adoption, and differential impacts across worker demographics. We propose a dynamic Occupational AI Exposure Score (OAIES) framework that leverages large language models and occupational task data, alongside enhanced data collection strategies and modernized estimation techniques. The architectural framework, illustrated through five interconnected diagrams, demonstrates how these methodological innovations integrate into a coherent system for measuring labor market transformation. These enhancements would enable more accurate projections of job displacement, skill evolution, and employment transformation across industries and geographic regions, supporting evidence-based policymaking for workforce development in an AI-driven economy. The paper concludes with a phased implementation strategy and validation protocol to ensure methodological rigor and operational feasibility.

Abstract: Organizations increasingly confront persistent tensions that require leaders to pursue competing demands simultaneously. Although prior research highlights paradox mindset as an orientation toward embracing tensions, less is known about the capabil-ity-based microfoundations that enable leaders to enact paradoxical leadership behaviors in practice. Addressing this gap, this study develops a cognitive–emotional capability framework that focuses on two developable resources: integrative complexity (IC)—a cognitive ca-pacity for differentiating and integrating competing demands—and emotion regulation (ER)—an affective capacity for sustaining engagement under tension. Using survey data from 264 Japanese managers, we examine the independent and joint effects of IC and ER strategies on paradoxical leader behaviors (PLB). Results show that IC and cognitive reappraisal are positively associated with PLB. Pol-ynomial regression and response surface analyses further reveal that PLB increases as IC and cognitive reappraisal rise together. However, when the two capabilities are im-balanced, PLB tends to be higher in profiles where IC exceeds reappraisal than in the opposite configuration. These findings suggest an asymmetric form of complementarity in which integrative complexity functions as a foundational capability while reappraisal provides supportive leverage. Overall, the study shifts attention from trait-like mindsets to trainable lead-ership capabilities and clarifies how cognitive–emotional capability configurations enable the enactment of paradoxical leadership.

Abstract: Background: Intraoperative radiotherapy with low-energy X-rays (IOXRT) is an increasingly utilized modality during breast conserving therapy (BCT). However, the molecular mechanisms by which it affects the postoperative microenvironment remain to be fully elucidated. Surgical wound fluid (WF) has been demonstrated to modulate cancer cell behavior; however, its metabolomic composition has not been previously characterized in the context of breast cancer. The objective of this study was to evaluate metabolic alterations in postoperative WF and to determine whether IOXRT induces distinct metabolic signatures compared with mastectomy (AMP).Methods: Postoperative WF was collected from 54 breast cancer patients (38 BCT IOXRT; 16 AMP) at two time points: day 1 (A) and day 5 (B) after surgery. The samples were then subjected to analysis using ¹H NMR spectroscopy, encompassing NOESY, CPMG, and JRES techniques. A total of 114 spectral signals were quantified, and 42 metabolites were identified. Multivariate analyses (PCA, PLS DA, OPLS DA) and Wilcoxon signed rank tests were applied to assess temporal and intergroup differences.Results: A clear metabolic separation between time points A and B was observed in both treatment groups. However, statistical analysis revealed no significant differences between BCT IOXRT and AMP. In BCT IOXRT, on the fifth day, WF exhibited a decline in branched chain amino acids, asparagine, lysine, methionine, and glutamate, concomitant with an increase in lactate and pyruvate. AMP-specific alterations encompassed a decrease in 2-oxoglutarate and hypoxanthine on the first day, along with an increase in glucose and creatinine on the fifth day. A decline in ketone bodies (3-hydroxybutyrate, acetoacetate, acetone) was observed in both groups.Conclusions: Postoperative WF demonstrates dynamic metabolic changes reflecting early wound healing processes and treatment-related effects. IOXRT has been found to be associated with enhanced glycolytic signatures and reduced amino acid levels, suggesting altered metabolic activity in the irradiated tumor bed. The metabolomic profiling of WF has the potential to offer a novel source of biomarkers, which could facilitate the assessment of treatment response and tumor microenvironment characteristics.

Abstract: Contemporary large language models (LLMs) are radically stateless: at every inference step they recompute the entire context, retain no persistent state, and perform no local weight adaptation. This simplicity enables massive scaling but also imposes fundamental limits on stability, speed, and energy efficiency. Each generation step collapses a rich internal state into a single token, causing cumulative drift and extreme computational redundancy. I formulate the AI Theorem: no purely computational system that generates output iteratively and without an external source of negative entropy can maintain stable information for an unlimited number of steps. This represents an analogue of Shannon’s Data Processing Inequality for computational cognition and defines a theoretical boundary for all computable architectures. Building on this limit, I outline Dynamic‑State AI, an architecture with persistent state, local updates, and dynamic weights. It respects the AI Theorem while approaching its limit asymptotically, reducing drift and energy use. This paper proposes a conceptual limit and an architectural framework rather than empirical results.

Abstract: We investigate the mechanisms by which natural systems encode data across multi-dimensional spaces. Integrating principles from information theory, probability, and geometry, we propose that certain Lie Groups govern these encoding processes. We first demonstrate that evenly distributed information becomes computationally unsolvable in higher dimensions. If we do not notice the "curse of dimensionality," it is because nature likely uses geometric positional notation at a rudimentary level. By extending the definition of representational cost to m dimensions using Benford’s Law, we identify a cost minimum at powers of Euler’s number (е^m). We introduce the "Lie Squad" (B3, F4, G2, A2, A1, and E6), a set of six compact simple Lie groups whose irreducible representations coincide with this ideal cost when m matches the group’s algebraic rank. These irreps facilitate a fundamental, rank-invariant number system based on balanced ternary, uniquely encoding integers as the difference of two natural numbers in bijective notation. Finally, we examine the Weyl orders of the Lie Squad members to show that Weyl divisors yield a logarithmic scale consistent with Benford’s Law and the universal number system proposed.

Abstract: This paper investigates the transition of euro area inflation dynamics from a stable regime to persistent cyclical behavior through the lens of nonlinear macroeconomic theory. We develop and estimate a nonlinear New Keynesian Phillips Curve (NKPC) model augmented with endogenous monetary policy feedback and regime-dependent dynamics. The analysis shows that increasing Phillips curve convexity, rising inflation persistence, and variations in policy responsiveness can push the system through a Hopf bifurcation, leading to the emergence of endogenous limit cycles. Empirical results based on euro area data (2000–2025) confirm significant nonlinearities, structural breaks around major crisis episodes, and a narrowing stability margin over time. Robustness checks—including alternative inflation measures, estimation methods, subsample tests, and country-group heterogeneity—support the central findings. Welfare comparisons across alternative policy rules indicate that nonlinear dynamics alter the ranking of monetary strategies and that aggressive policy responses are not universally stabilizing. The results provide a unified explanation for the euro area’s regime transitions and offer concrete guidance for central bank design in environments characterized by structural nonlinearities and shock amplification.

Abstract:

Background. Quaternary phosphonium salts (QPSs) are extensively researched since represent new promising weapons to counteract critical superbugs, regardless their robust pattern of resistance. Methods. Here, dynamic light scattering analysis was carried out on QPSs 1, 3 and 4 recently reported and already found active against cancer cells, and phosphine 2 unveiling particles of 700-800 nm for 2, 3 and 4 and positive Zeta-potential (ζ-p ) for all (+4.2-+38.1 mV). 1, 3 and 4 plus 2, were microbiologically evaluated, assessing minimum inhibitory concentration values (MICs) (1-4), time-killing curves (1), and anti-biofilm capacity (1). Results. MICs on a total of 23 Gram-positive and Gram-negative clinically isolated superbugs, evidenced that, poorly soluble 2, 3 and 4 exhibited not reproducible MICs, while 1 provided interesting MICs, which made it worthy of further investigations. In fact, 1 was active against clinically relevant multidrug-resistant (MDR) Gram-positive species and not active against MDR Gram-negative species including Pseudomonas aeruginosa. Specifically, MICs = 16-32 µg/mL and 16-64 µg/mL were determined against methicillin-resistant Staphylococcus aureus (MRSA) and S. epidermidis (MRSE) respectively. MICs = 32-64 µg/mL were observed against teicoplanin- and vancomycin-resistant (VRE) Enterococcus faecalis and E. faecium and no activity against P. aeruginosa (> 128 µg/mL). Notably, time-kill experiments established that 1 was bactericidal against MRSA, while strongly inhibited (up to 100%) the formation of biofilm produced by the strongest biofilm-producers S. epidermidis and S. aureus isolates of our collection, at MICs and 2.5 × MIC concentrations, depending on isolates considered. Interestingly, if used against Staphylococci, and mainly MRSA, 1 was softly haemolytic. It was no cytotoxic against not tumorigenic human keratinocytes (HaCaT) and murine embryonic fibroblasts (3T3) in all cases. Structure-activity relationships have been studied, leading to outcomes which could be of great help for designing optimized new QPSs. Conclusions. Findings of this study overturn previous antimicrobial reports on compound 1, suggesting it as a new excellent weapon to counteract bacterial resistance and biofilm production by MRSA and MRSE superbugs, as well as thinkable for future in vivo experiments and clinical development.

Abstract: Understanding pathological processes remains challenging because clinical descriptions primarily rely on phenotypic observations, while the underlying dynamical mechanisms that generate and stabilize disease states often remain implicit. This article introduces forms dynamics as an applied physics framework aimed at interpreting pathology as the dynamical evolution of structured configurations sustained by continuous exchanges of energy, matter and information with the environment. The approach integrates concepts from non-equilibrium thermodynamics, complex systems modelling and Gestalt-inspired structural reasoning. Within this perspective, pathological systems are represented through physically meaningful variables and fluxes whose interactions can be expressed through coupled balance equations or equivalent graphical schematizations. Empirical data, including clinical observations, diagnostic measurements and network-based analyses of biological interactions, inform the identification of relevant variables and pathways. Model calibration constrains parameters using physiological ranges, characteristic timescales and observed trajectories, while validation relies on the consistency of the resulting dynamical regimes with clinical phenotypes and responses to perturbations. Within this framework, physiological conditions correspond to stable attractors in the system’s dynamical landscape, whereas pathological states emerge from altered coupling between variables and fluxes, leading to alternative stable or metastable regimes. By providing a physically grounded representation of pathological dynamics, forms dynamics offers a unifying modelling strategy for complex diseases and may support translational research, physics-informed digital twins and more interpretable computational tools for clinical decision support.

Abstract: The Neotropical genus Inga (Fabaceae) is a dominant component of tropical forests and plays important ecological and functional roles; however, its diversity patterns and environmental controls across Andean landscapes remain poorly documented under increasing deforestation pressure. This study quantified the diversity, distribution, and environmental determinants of Inga species in the Imbabura Province, northern Ecuador, by integrating field surveys along five elevational transects, herbarium records, and Geographic Information Systems (GIS)-based analyses of climatic and edaphic variables. We recorded 17 species, nearly tripling previous regional findings. Species richness and occurrence were strongly structured by altitude, temperature, and soil properties. Ten species showed narrow altitudinal range and limited thermal tolerance (<2 °C), indicating habitat specialization, whereas I. densiflora and I. insignis exhibited broader niche breadths and generalist behavior. Edaphically, most species were associated with sandy loam soils, particularly Mollisols and Inceptisols. These results indicate that environmental gradients and soil conditions act as primary filters shaping Inga assemblages in heterogeneous montane landscapes. The high level of specialization observed suggests elevated vulnerability to land-use change and highlights the need for habitat-specific conservation strategies in Andean forests.

Abstract: Glacial lake outburst floods (GLOFs) are high-impact hazards in mountain regions, yet many events remain poorly documented because field access is limited and lake evolution can occur on sub-weekly time scales. Here we used high spatiotemporal resolution PlanetScope imagery (3 m) to quantify the seasonal evolution and abrupt drainage of a moraine-dammed glacial lake in August 2025 in northern Pakistan. Historical lake dynamics were reconstructed using PlanetScope (2016-2024) imagery and multi-decadal Landsat observations (1992-2018). Climatic conditions were evaluated using ERA5-Land temperature data, and seasonal snow dynamics were characterized using MODIS and PlanetScope-based snow cover analyses. Multi-decadal satellite imagery indicates that lake formation in this catchment was historically intermittent, with no evidence of abrupt drainage before 2025, highlighting the anomalous nature of the event. PlanetScope observations show steady lake expansion throughout summer 2025, reaching a maximum area of 0.052 km² prior to the GLOF on August 22. Pre- and post-event imagery reveals no discernible landslide or impact trigger. Instead, the observations are most consistent with a failure mechanism driven by meltwater-driven lake growth and overtopping or erosion of the moraine dam. The 2025 summer season (June to September) was characterized by exceptionally warm conditions and unprecedented early snow depletion relative to the 2000-2024 baseline, suggesting a strong climatic and cryospheric contribution to the outburst. These results demonstrate the value of integrating dense time series of satellite observations and climatic data for capturing glacial-lake life cycles and diagnosing likely controls on outburst initiation. The study highlights the critical role of high-frequency satellite remote sensing for improving GLOF monitoring and early-warning capabilities in data-scarce mountain environments.

Abstract: We quantize the exterior region of a Schwarzschild-AdS black hole using our model of quantum gravity. The resulting hyperbolic equation is solved by products of temporal eigenfunctions w_i, the eigenvalues of which all have multiplicity one, and spatial eigendistributions v_ij having the same eigenvalues but with multiplicities 1 ≤ m_i, where the m_i could in principle be arbitrarily large. Regarding only the exterior region, there was no guidance how to determine the values of the m_i. However, considering also the quantization of the interior region, where the same question did not arise since the m_i could be chosen by maximizing the value, it seemed logical to choose the same values, too, in the exterior case. Since the eigenvalues in the interior are the same because the temporal Hamiltonian is the same in both cases, this choice defined a unitary equivalence between the respective Hilbert spaces and the respective Hamiltonians. Hence, there is no information paradox on a quantum level.