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.

Abstract: Metabolism in living things is the combination of enzyme-catalyzed biochemical reactions that drive biological work in the form of energy capture and release, molecule synthesis, cell replication and other functions. It is constrained by many factors, including resources, enzyme characteristics, and temperature under the requirement that organisms persist through time. Here, the biochemical foundation for metabolism is viewed from a thermodynamic perspective that explores three different metabolic currencies: (1) entropy production, which reflects the ability to persist at or near steady state through time by the rate at which entropy of surroundings is increased relative to that inside a system, (2) reaction rate or the rate of formation of products, and (3) power, the rate at which “free” (Gibbs) energy available for doing work is generated. Rate-temperature relationships for each objective are derived from a reaction-displacement model of a metabolic reaction for near-steady-state conditions, which are presumed to be required for organisms to persist over time. Reaction rate, entropy production and Gibbs energy production are maximized at different optimal temperatures, T_opt, all at barely distinguishable near-maximum reaction rates. These theoretical predictions nevertheless provide distinct, testable hypotheses for organism response to temperature under maximizing each of the three metabolic currencies. The framework also suggests that there exists a maximum temperature for life, T_max, at which entropy generated near reaction sites by reaction activation becomes greater than that generated away from reaction sites by the dissipation of heat and products. The framework predicts shifts in Topt and T_max that differ among types of reactions, enzyme concentrations, organism element concentration and varying body size. Overall, the framework provides a greatly expanded set of hypotheses and explanations for temperature performance relationships for life, including variation in both T_opt and T_max, for growth versus locomotion and respiration, “fast” versus “slow” life histories, resource-rich versus resource-poor environments, and intra- and interspecific variation in body size.

Abstract: Tea (Camellia sinensis), a widely consumed beverage, reduces oxidative stress and has antimicrobial properties due to its phytochemicals. Ethanolic extracts from Bangladeshi green tea were analyzed for phytochemicals, antioxidants, bioactive compounds, and antibacterial activity using in vitro and in silico methods. Qualitative screening detected alkaloids, phenolics, flavonoids, and tannins, with total phenolic content (TPC) at 35.95 ± 0.24 mg GAE/g and total flavonoid content (TFC) at 34.61 ± 1.53 mg QE/g. Antioxidant tests showed strong total antioxidant capacity (301.01 ± 14.32 mg AAE/g) and DPPH radical scavenging (IC50 2.70 μg/mL vs. ascorbic acid’s 3.75 μg/mL). HPLC identified gallic acid and vanillic acid as key compounds. Agar well diffusion assays revealed dose-dependent zones of inhibition against Staphylococcus aureus (12–22 mm) and Escherichia coli (10–20 mm) at concentrations of 25–200 mg/mL. In silico docking showed gallic and vanillic acids binding to S. aureus (PBP2a, SrtA) and E. coli (AmpC β-lactamase, GyrB) targets with affinities of -4.9 to -6.0 kcal/mol, stabilized by hydrogen bonds and π-interactions. ADME profiles indicated high gastrointestinal absorption, Lipinski compliance, and bioavailability (0.56–0.85). Toxicity predictions suggested minimal risks, mainly nephrotoxicity. PASS analysis predicted antibacterial, β-lactamase, and DNA gyrase inhibition activities. These findings underscore Bangladeshi green tea as a promising source of antioxidants and antibacterials for oxidative stress and infections.

Abstract: This paper provides a rigorous examination of eight fundamental architectural deficiencies that render the Linux kernel unsuitable for deployment in safety-critical avionics. These deficiencies include inadequate temporal determinism, the absence of physical memory isolation, driver-induced contamination of global kernel state, an excessively large and unbounded Trusted Computing Base (TCB), open and nondeterministic system semantics, insufficient inter rocess fault containment, unstable kernel behavior due to continuous patching, and a highly complex toolchain that imposes prohibitive DO-330 qualification burdens. Through a technical and standards-aligned analysis, this paper demonstrates that Linux cannot satisfy the determinism, verifiability, isolation, and lifecycle stability required for airworthiness certification, making it inherently incompatible with certifiable airborne platforms.

Abstract: Background: Disorders of the gut–brain interaction (DGBIs) constitute a group of func-tional conditions widely described in adults; however, some of these have not been in-cluded in pediatric Rome Criteria, despite the fact that they may manifest during childhood. Early identification of these conditions is relevant due to their clini-cal/psychosocial impact, as well as their effect on quality of life. The aim was to determine the prevalence and associated factors of some DGBIs described in adults according to the Rome IV Criteria in pediatric population. Methods: An observation-al/prospective/cross-sectional study was conducted in toddlers, school-aged children, and adolescents from three Colombian cities. The adapted Questionnaire for Pediatric Gas-trointestinal Symptoms Rome IV (QPGS-IV) using adult criteria was applied, along with quality-of-life scales and PROMIS for anxiety/depression. Descriptive uni/bivariate analyses were performed, as well as a multivariate logistic regression model. Results: 789 participants were included (13.7±2.8 years old). The prevalence of DGBIs described in adults according to QPGS-IV was 5.8%, proctalgia fugax being the most frequent. In the bivariate analysis, race, school/social absenteeism, depressive traits, and impaired quality of life were significantly associated. In the multivariate model depressive traits (OR=4,08; 95%CI=1,82-9,12; p=0,001), school (OR=2,51; 95%CI=1,06-5,98; p=0,036), and social ab-senteeism (OR=4,04; 95%CI=1,70-9,62; p=0,002) were the factors independently associated. Conclusions: These adult’s DGBIs according to the QPGS-IV can occur in pediatric pop-ulations and are closely related to psychoemotional and functional factors. They are mainly associated with depression and school/social absenteeism, supporting the need for a biopsychosocial approach and a revision of pediatric diagnostic criteria.

Abstract: Background/Objectives: Rates of postoperative recurrence of varicose veins range from 7% to 62%. We analyzed factors associated with recurrent varicose veins (RVVs) of the lower extremities after interventional treatment. Methods: We enrolled 99 patients (114 lower extremities) with RVVs admitted from January 2018 to June 2025 (71 women, 76 limbs; 28 men, 31 limbs; average age (SD) 63.9 (9.8) years). Duplex ultrasound scanning, magnetic resonance venography, or computed tomography venography were performed. The presence of a residual great saphenous vein, primary deep venous valve insufficiency, incompetent perforating veins, and iliac vein stenosis were recorded. We analyzed the charts of patients who underwent operations for recurrent varicose veins for associated factors. Results: We recorded residual trunk of the great saphenous vein in 55 limbs (48.3%), deep venous valve insufficiency in 47 (41.2%), incompetent perforating veins in 7 (6.1%), anterior accessory saphenous vein insufficiency in 2 (1.8%), and small saphenous vein insufficiency in 2 (1.8%). Iliofemoral venography indicated 31 limbs (27.2%; left lower, 24; right lower, 7) with a nonthrombotic iliac vein compression lesion. After identifying associated factors, retreatments—high ligation and stripping of the great saphenous vein or iliac vein stent implantation or small saphenous vein dissection ligation and stripping—were performed. The venous clinical severity score was 7 (interquartile range, 5–10) on admission and 3 (interquartile range, 2–4) 3 months after discharge.Conclusions: RVVs are associated with multiple factors, including inadequate initial surgical techniques, nonthrombotic iliac vein compression lesions, deep venous valve insufficiency, small saphenous vein insufficiency, and incompetent perforating veins. Performing detailed imaging before retreatment is essential to identify factors associated with RVVs and prevent recurrence.

Abstract: Numerous metrics, like as visitor numbers, tourism net profit, and hotel occupancy rates, are included in the dataset, which covers 77 provinces. A baseline-based concept of shock-recovery is introduced to measure the impact and the different recovery paths in different regions. Recurrent neural networks incorporate engineered elements that capture seasonality, trend dynamics, shock strength, volatility, and recovery tim-ing. Importantly, latent spatial heterogeneity and cross-regional dependencies are learned inside a single architecture by integrating province-level spatiotemporal em-beddings. To jointly forecast tourism demand and net profit, models called Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are created. Using a time-preserving evaluation technique, model performance is assessed against statisti-cal time-series baselines and XGBoost. In early 2020, the results show a structural break that exceeded the 95% decline, along with significantly unequal recovery pat-terns. By roughly 22-28% in RMSE and 14-16% in MAPE, the suggested deep learning models surpass baselines, exhibiting superior ability to capture spatial heterogeneity and nonlinear recovery dynamics.

Abstract: Corporate governance reforms in emerging and frontier markets frequently assume that strengthening board oversight, audit committees, and ownership monitoring will improve audit quality and enhance firm value. Yet, in weak institutional environments, these mechanisms often function symbolically rather than substantively. This study rethinks the governance–audit–value nexus by integrating Agency Theory, Institutional Theory, and the concept of symbolic governance to explain why governance may appear structurally robust while failing to constrain managerial discretion. Using panel data from Ghanaian listed firms between 2015 and 2023, the analysis shows that audit committee independence and board independence are negatively associated with both audit quality and firm value, indicating that formal independence without expertise, authority, or enforcement capacity does not translate into meaningful oversight. By contrast, institutional and managerial ownership positively influence both outcomes, suggesting that incentive alignment and informed monitoring can substitute for weak formal governance. Foreign ownership improves firm value but does not consistently enhance audit quality, while macroeconomic conditions such as inflation and GDP growth further shape firm performance. The study advances the literature by reconceptualising governance effectiveness in weak institutional environments, demonstrating that governance mechanisms may exist in form without functioning in substance. The findings underscore the need for governance reforms that prioritise enforcement capacity, board expertise, and audit committee competence rather than structural compliance alone.

Abstract: This paper introduces a novel theoretical framework for classifying Autonomous Mobile Robots (AMRs) into three hierarchical layers: Perception, Cognition, and Operation. Unlike prior hardware-centric taxonomies, our approach, grounded in a structured review of seminal works, foundational methodologies, and state-of-the-art advances, explicitly integrates locomotion mechanisms (wheeled, legged), application domains (industrial, agricultural), and autonomy levels with navigation strategies. The framework unifies terrestrial navigation techniques into a cohesive taxonomy, clarifying modular boundaries and interdependencies. Serving as both a conceptual guide and educational tool, it empowers researchers to evaluate trade-offs in sensor configurations, decision-making algorithms, and trajectory execution under real-world constraints. A comparative analysis positions this framework against established navigation architectures, highlighting its role as a high-level reference design for modular implementations. By bridging theoretical principles with system optimization, the framework enhances interoperability across robotic platforms. Ultimately, this work delivers a practical design atlas, structuring the end-to-end pipeline of autonomous navigation to guide researchers and practitioners in selecting algorithms suited to their specific robotic platforms and mission requirements.

Abstract: Objective: To identify the clinical effectiveness of laser photobiomodulation in the treatment of orofacial paresthesia in a public university clinic in Northeast Brazil. Methods: A retrospective, observational study was conducted using secondary data from 125 patients treated at the Biophotonics Center of the Faculty of Dentistry of the Federal University of Bahia from 2003 to 2019 with a confirmed diagnosis of orofacial paresthesia. Data collection included the sociodemographic profile, clinical and dental history, and the patients' main complaint. The therapeutic protocol employed an infrared diode laser (λ 700–808 nm), in continuous mode, with applications every 48 hours, totaling 12 individualized sessions. Nerve sensitivity was measured in each session using the Visual Analogue Scale (VAS), with pain reduction and movement considered as response variables. The data were analyzed, and the study was approved by the ethics committee of FOUFBA (protocol 60327516500005024). Results: Tooth extraction was identified as the main etiological factor of paresthesia (60%), with an average of three treatment cycles per patient. At the end of the intervention, 67% of individuals achieved satisfactory results, with significant clinical improvement or total remission of symptoms. Cases resulting from orthognathic surgery required higher cyclic doses, although no statistically significant difference was found between dosage and clinical outcome. Laser photobiomodulation proved to be an effective and viable therapeutic modality for sensory recovery and management of orofacial paresthesias.