Abstract: This study investigates the application of Markov and Hidden Markov Models (HMMs) for detecting latent market regimes in cryptocurrency markets, with a particular focus on Bitcoin. Cryptocurrency markets are characterized by high volatility, structural breaks, and non-stationary behavior, which often limit the effectiveness of traditional linear time-series models. Hidden Markov Models provide a probabilistic framework capable of identifying unobservable market states that generate observed price dynamics. In this research, a regime-switching framework is developed to classify Bitcoin market conditions into distinct latent states characterized by different statistical properties of returns and volatility. The proposed methodology extends standard homogeneous HMMs by incorporating non-homogeneous transition probabilities and Bayesian estimation techniques to better capture dynamic market behavior. Time-varying transition probabilities allow the model to reflect evolving market conditions influenced by trading activity and external factors. Additionally, extensions addressing duration dependence and long-memory volatility are considered to improve regime persistence modeling. Empirical evaluation using Bitcoin data demonstrates that regime-aware modeling effectively captures transitions between low-volatility consolidation phases and high-volatility turbulent periods. The results suggest that incorporating regime detection significantly improves the interpretability of market dynamics and provides a valuable foundation for risk-aware trading strategies and adaptive portfolio allocation in highly volatile digital asset markets. The findings highlight the potential of Hidden Markov frameworks as a robust tool for understanding structural shifts in cryptocurrency markets and improving predictive modeling of financial time series.

Abstract: Pediatric gastrointestinal (GI) cancers are rare malignancies that differ fundamentally from their adult counterparts in molecular drivers, histology, and clinical behavior. While adult GI cancers are frequently driven by recurrent oncogenic mutations, pediatric tumors often exhibit pathway-level dysregulation involving developmental signaling networks. Among these, the RAS/MAPK pathway emerges as a central convergent axis integrating growth factor signaling, developmental programs, inflammatory cues, and post-translational regulatory mechanisms. Increasing evidence suggests that aberrant phosphorylation dynamics result from imbalanced kinase activation and phosphatase-mediated signal attenuation which contribute to sustained MAPK signaling in pediatric GI malignancies, even in the absence of canonical RAS or RAF mutations. This review synthesizes current knowledge on RAS/MAPK signaling in pediatric GI cancers, emphasizing the role of kinase–phosphatase imbalance, signal duration, and regulatory failure in shaping oncogenic outcomes. We highlight how altered phosphorylation control may influence tumor differentiation, therapeutic responsiveness, and resistance mechanisms, and discuss emerging opportunities for targeting signaling dynamics rather than single genetic lesions. This signaling-centric framework provides a biologically grounded rationale for functional biomarker-driven precision therapy in pediatric GI malignancies.

Abstract: Anti-inflammatory agents, antipyretics, and antibiotics are commonly used to manage fever and pain associated with infectious diseases in both adults and children. Despite their effectiveness, inappropriate and unnecessary prescriptions remain widespread, leading to adverse patient outcomes and, in the case of antibiotics, contributing to antimicrobial resistance. Addressing these issues requires effective stewardship programs focused on educating healthcare professionals and the public on evidence-based guidelines for optimal prescribing practices. This paper explores the five "A"s fundamental to infection management in pediatric and adult patients: appropriateness, abuse, antipyretics, anti-inflammatory agents, and antibiotics. Through a comprehensive literature review, expert perspectives, and clinical guidelines, the study evaluates the roles of anti-inflammatory agents (e.g., ibuprofen), antipyretics (e.g., paracetamol), and antibiotics in clinical practice, highlighting best practices for their use. Experts’ suggestion emphasize that antipyretics should only be administered when fever is accompanied by significant discomfort or pain, as fever itself plays a role in the immune response. Paracetamol is generally preferred as a first-line antipyretic due to its favorable safety profile, while ibuprofen should be used with caution, particularly during respiratory infections, varicella, and severe bacterial infections, due to its potential to exacerbate complications. Special consideration is also required for patients with renal or gastrointestinal comorbidities to prevent toxicity. Regarding antibiotics, prescription should be limited to clear evidence of bacterial infection to avoid unnecessary patient exposure and the development of antimicrobial resistance. Stewardship programs underscore the importance of selecting the right agent, optimizing dosing, and introducing shorter treatment regimens where supported by evidence, to improve therapeutic outcomes while minimizing resistance risks. Ultimately, this paper provides practical, evidence-based recommendations to support rational prescribing of antipyretics, anti-inflammatory drugs, and antibiotics, aiming to optimize patient outcomes, prevent unnecessary toxicity, and contribute to global efforts against antimicrobial resistance.

Abstract: Background: Pneumococcal conjugate vaccines (PCVs) prevent severe disease in children, but high costs limit access. PNEUMOSIL®, a 10-valent PCV prequalified by World Health Organization (WHO) in 2019, offers a cost-effective alternative. This study assessed its safety and immunogenicity in Vietnamese children aged 6 weeks–24 months. Methods: An open-label, single-arm study enrolled 304 children in three age groups: 6 weeks–6 months (n=151), >6–12 months (n=76), and >12–24 months (n=77). Participants received two or three doses. Safety was evaluated through immediate reactions, adverse events (AEs), serious adverse events (SAEs), and withdrawals. Immunogenicity was measured 28 days after the final dose using serotype-specific IgG geometric mean concentrations (GMCs), opsonophagocytic activity (OPA) titers, and seroresponse rates. The trial was approved by the IRB of the National Ethics Council (code: No. 75/CN-HĐĐĐ on date June 4th, 2021) and was registered with ClinicalTrials.gov, NCT05140720. Results: Of 304 enrolled participants, 294 (96.7%) completed follow-up. No immediate adverse events or serious adverse events occurred. Unsolicited adverse events were reported in 17%, mainly respiratory, while serious adverse events occurred in 4%. Mild local/systemic reactions (e.g., injection site pain, crying) resolved without sequelae. Immunogenicity was strong, with GMCs 1.8–9.11 µg/mL, GMTs 277.8–22,342, and >90% achieving seroresponse for all 10 serotypes. Conclusions: PNEUMOSIL® demonstrated favorable safety and robust immunogenicity, supporting its inclusion in national immunization programs as an affordable option for pneumococcal disease prevention.

Abstract: Translational neuroscience relies on both in vitro slice recordings and in vivo record-ings. Their spontaneous population dynamics are observed under decisively different conditions, and across independent experiments there is typically no clear neuron-to-neuron correspondence. Here we formulate a time-resolved, bidirectional transfer task be-tween in vitro and in vivo multineuronal spike trains and provide a standardized evalua-tion procedure for generation across markedly different recording preparations. We train an autoregressive Transformer on 1-ms binned, 128-unit binary sequences and introduce Dice loss to directly optimize spike-event overlap under extreme class imbalance, compar-ing it with Binary Focal Cross-Entropy (γ = 2.0). Across 12 mouse datasets (6 in vitro HD-MEA sessions and 6 in vivo Neuropixels sessions), the method achieves strong within-domain performance and remains above chance for cross-domain generation (ROC-AUC 0.70±0.09 for in vitro→in vivo; 0.80±0.10 for in vivo→in vitro). Because spike events are ra-re, we report Precision–Recall curves and PR-AUC alongside ROC-AUC to reflect minori-ty-event quality. To our knowledge, this is the first demonstration of bidirectional, time-resolved generation between unpaired in vitro and in vivo population spike trains without assuming cell correspondence, and the framework can be adapted to other sparse neural event data and related event-based datasets when domain-specific validation criteria are defined.

Abstract: The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m×n), where m denotes the number of alternatives and n the number of criteria, becomes prohibitive when decision matrices have several million rows. To overcome this limitation, we propose GPU-TOPSIS, a fully vectorized and parallel reformulation of TOPSIS based on tensor execution on graphics processing units (GPUs), whose main contributions are: (i) a formally correct reformulation of TOPSIS as a GPU tensor pipeline preserving mathematical fidelity to the original method; (ii) a two-pass fragment-processing algorithm guaranteeing exact mathematical equivalence with monolithic TOPSIS, while reducing the memory footprint from O(m×n) to O(mₜ×n); (iii) Three independent implementations on CuPy, PyTorch, and TensorFlow ensure the framework's portability and genericity. Experimental evaluations on real data from the Amazon Products 2023 dataset, using matrices of up to 200 million alternatives (via the 2-pass formulation), demonstrate speedups of up to 4.75× compared to the reference CPU implementation (NumPy). A perturbation sensitivity analysis of the criteria weights and cross-backend consistency tests confirms that GPU acceleration fully preserves robustness and decision reliability, making GPU-TOPSIS a practical, open, and reproducible solution for large-scale multi-criteria decision making in Big Data environments.

Abstract: The Aircraft Landing Problem (ALP) poses significant challenges for traditional Monte Carlo Tree Search (MCTS) due to its vast search space and reliance on inefficient random simulations. To overcome these limitations, this paper proposes a novel Transformer-Augmented Monte Carlo Tree Search (TMCTS) algorithm. Our approach integrates a reinforcement learning framework that incorporates key operational constraints, including wake turbulence separation and time windows, and employs a cost function aimed at minimizing both delay time and fuel consumption. A core innovation is the replacement of the conventional random simulation phase in MCTS with a Transformer-based value predictor. This leverages the Transformer’s superior capability in sequence modeling and capturing global dependencies among flights, thereby dramatically accelerating search convergence. Specifically, we design a two-head Transformer network (comprising policy and value heads) to provide informed prior knowledge, which effectively guides the selection and expansion steps of the MCTS tree. The model is trained within an Actor-Critic framework, utilizing behavior cloning for pre-training followed by reinforcement learning for fine-tuning. Experimental evaluations on the standard OR-Library benchmark demonstrate that our TMCTS method significantly reduces scheduling deviation compared to state-of-the-art baselines (including DPALO+GA, DPALO+PSO, and DALP). Moreover, it achieves a 90.6% reduction in computation time relative to the DALP method, highlighting its superior efficiency and practical applicability for real-time scheduling.

Abstract: This study successfully developed a novel tumor-associated macrophages (TAMs)-targeting nanoplatform-sialic acid-disulfide bond-camptothecin (SA-SS-CPT) nanowires. This system significantly improved the solubility and bioavailability of camptothecin (CPT) and achieved active targeted drug delivery by utilizing sialic acid as a targeting ligand to specifically recognize the highly expressed Siglec-E receptor on TAMs. Upon internalization into TAMs, the disulfide bond in the SA-SS-CPT nanowires was cleaved in response to intracellular glutathione (GSH), leading to the controlled re-lease of CPT. SA-SS-CPT induced DNA damage in TAMs, thereby activating the cGAS-STING signaling pathway, promoting the polarization of TAMs toward the M1 phenotype, enhancing pro-inflammatory and anti-tumor immune responses, and effec-tively inhibiting tumor immune escape. Furthermore, the SA-SS-CPT nanowires syner-gistically enhanced the efficacy of PD-L1 blockade immunotherapy, collectively remod-eling the tumor immune microenvironment and ultimately facilitating significant tumor clearance.

Abstract: This paper introduces Bayesian R-LayerNorm, a normalization layer that extends the previously proposed R-LayerNorm with uncertainty quantification. Building upon R-LayerNorm, we draw connections to statistical field theory, renormalization group methods, and infor-mation geometry to motivate the design. The method incorporates uncertainty estimation through a stable ψ-function, enabling adaptive noise suppression based on local entropy esti-mates. We provide theoretical analysis of numerical stability, gradient stability, and training convergence under standard assumptions. A key practical contribution is the integration of uncertainty quantification directly into the normalization operation, providing confidence estimates for each normalized activation without additional cost. The method adapts to local noise, varying normalization strength spatially based on estimated noise levels. The implementation is simple, adding only two learnable parameters per layer, and serves as a drop-in replacement for existing normalization layers. Due to computational constraints (Kaggle P100 GPU, limited epochs), we evaluate Bayesian R-LayerNorm on CIFAR-10-C using 50 training epochs and 3 random seeds. Under these limitations, it achieves average accuracy gains of +0.49% over standard LayerNorm across four common corruptions, with the largest improvement of +0.74% on shot noise. While these gains are modest, they are consistent across seeds. The method requires mini-mal computational overhead ( 10%) and we provide complete open-source implementation. We further show that the learned λ parameters offer interpretability, revealing which layers adapt most strongly to different corruptions. The framework suggests promising directions for trustworthy normalization in safety-critical applications where uncertainty matters alongside accuracy.

Abstract: We derive a quantum conjugacy between spacetime diffusivity and inertial mass from relativistic information-transport kinematics. Two Lorentz-invariant laws—(i)~an invariant-time gauge for timelike segments, \( ds=c\,dt \), and (ii)~diffusive evolution \( d\epsilon/ds=c- \)yield a first-order action whose canonical quantization gives \( [\hat\epsilon,\hat m]=i\hbar \) and the emergent Cosmological Uncertainty Principle~(CUP), \( \Delta\epsilon\,\Delta m\ge\hbar/2 \). Independence across coherence cells of size \( \ell_{\rm coh} \) amplifies the bound to \( \Delta\epsilon\,\Delta m\ge(\hbar/2)\,N_{\rm eff} \) with \( N_{\rm eff}=D/\ell_{\rm coh} \), extending quantum uncertainty to cosmic baselines. A single area-diffusion parameter provides an operational unification of Planck and Hubble times across \( {\sim}\,61 \) orders of magnitude. Applied to black-hole horizons, the CUP reproduces Hawking's temperature exactly. for de~Sitter space, a naive 1/H correlation window overshoots by a factor \( \pi \), while KMS/Unruh calibration (\( \tau=\pi/H \)) recovers the standard Gibbons–Hawking result \( T_{\rm dS}=\hbar H/(2\pi k_B) \). Unlike generalised or extended uncertainty principles that deform the position--momentum commutator, the CUP introduces a new conjugate pair (\( \epsilon,m \)) while leaving the Heisenberg sector intact. These results position CUP as an emergent, testable quantum--informational constraint on cosmological observables rather than an added axiom.

Abstract: Salinity stress is one of the major obstacle worldwide for the glycophytic crop production, including rice. It alters the cellular metabolism, causing significant crop destruction resulting in substantial reductions in yield. Through genetic engineering, the oxidative stress can be decreased while increasing the photosynthetic capability by using C3 transgenic plants that produce the C4 enzymes like phosphoenolpyruvate carboxykinase (PEPCK) at a high level. In this research, we evaluate the efficiency of transgenic rice plants (Oryza sativa L. cv. IR64) over-expressing PEPCK genes to act against salinity stress as well as increasing its photosynthetic efficiency. The T1 transgenics showed increased levels of several biochemical factors, including ascorbate peroxidase (APX), malondialdehyde (MDA), glutathione reductase (GR) and guaiacol peroxidase (GPX) activities suggesting the existence of an effective antioxidant defense mechanism that helps the plants to deal with oxidative damage driven by salt stress. The photosynthetic parameters like chlorophyll contents, net photosynthetic rate, intercellular CO2 content and stomatal conductance were elevated in transgenic plants when compared with the control plants (null seggregant). It also exhibited higher agronomic characteristics than the control plant. Our findings add a conclusive evidence of PEPCK gene’s potential role in regulating salt stress response and tolerance of rice plants.

Abstract: Deep learning classifiers deployed in scientific applications often encounter inputs that violate physical laws (e.g., due to sensor failure or corruption). Standard methods cannot detect such violations and may produce confident but wrong predictions. We propose UA-PBR, a framework that combines a physics-informed autoencoder (to detect physics violations) with a Bayesian CNN (to quantify predictive uncertainty). Inputs are rejected if either the PDE residual exceeds a threshold or the predictive entropy is too high. As a proof-of-concept, we evaluate UA-PBR on a synthetic Darcy flow dataset (32 × 32 grid) under severe computational constraints (Google Colab, 10 seeds). Despite these limitations, UA-PBR reduces classification risk by over 90% on heavily corrupted samples while accepting 89.7% of clean inputs with 99.99% accuracy on accepted samples. Ablation studies confirm that both components contribute synergistically. These preliminary results on a synthetic benchmark illustrate the potential of physics-aware rejection and motivate further investigation with larger-scale experiments. Code is available at: https://github.com/UA-PBR/UA-PBR.

Abstract: Pupillometry is a reliable method of pain control. There are experimental conditions under which standard pupillometry equipment cannot be used. Studying effects of different pulse forms used for transcutaneous spinal cord stimulation (tSCS) is one such task. The aim was to create a system for recording pupil diameter based on a web camera because it can be synchronized with external equipments, which allows the diameter to be recorded simultaneously with other physiological signals. A markerless system for recording and analyzing pupil diameter using deep neural networks was developed based on a commercially available web camera. The accuracy of this system was compared with the accuracy of measurements using manual analysis with the ImageJ. The system was tested in a study of the dependence of tolerance to tSCS on the shape of stimulating pulses, which involved the participation of volunteers (n=12). The results of the developed pupillometry were compared with the pain rating scale traditionally used in such studies. The developed system is accurate in determining the pupil diameter, comparable to human accuracy. The pupillometry results reproduced those obtained using a subjective pain scale. The developed method was found to be a reliable method for recording pain in electrophysiological studies.

Abstract: We performed a retrospective analysis of the data from a total of 241 patients (n=241). A total of 161 (66.8%) patients received the peripherally-inserted central catheter (PICC) for long-term intravenous access, 172 (71.4%) had no past history of receiving catheters and 142 (58.9%) received the PICC on the right side. Target veins include basilic vein (42.7% [103/241]), brachial vein (41.9% [101/241]) and cephalic vein (15.4% [37/241]). There were a total of five cases (2.1%) of the PICC-related infection. Of these, one case (0.4%) was the PICC-related bloodstream infection; Candida parapsilosis was identified from both the PICC tip and blood samples. A total of 224 patients (92.9%) had optimal positions of the PICC tip. Male sex (OR 0.183; 95% CI 0.050-0.675, p=0.011), the length of a catheter (OR 0.794; 95% CI 0.657-0.960, p=0.017) and right side (OR 4.711; 95% CI 1.227-18.091, p=0.024) were significant risk factors of non-optimal positions of the catheter. Time-to-events are estimated at 56.02±1.37 days (95% CI 53.33-58.71). Here, we describe our single-center, retrospective experience with bedside ultrasound (US)-guided PICCs in elderly ICU patients in a small-volume center.

Abstract: Invasive candidiasis is a severe opportunistic infection whose incidence may be influenced by major disruptive events. The COVID-19 pandemic substantially altered hospital dynamics in Colombia. This study aimed to evaluate temporal trends, seasonality, and potential changes in the incidence of invasive candidiasis between 2019 and 2024. We conducted an observational time-series study using confirmed cases of invasive candidiasis from medium- and high-complexity hospitals in three major Colombian cities. Cases were aggregated quarterly. An interrupted time-series (ITS) analysis was performed. A total of 1,294 cases were analyzed. An increasing trend was observed until mid-2022, followed by a decline during 2023. Seasonal decomposition revealed persistent seasonality with recurrent peaks in the second and fourth quarters. The ITS analysis did not demonstrate statistically significant changes in level or slope after the interruption (p > 0.05), although clinically relevant fluctuations were observed. No significant differences in temporal trends were identified across Candida species. Invasive candidiasis in Colombia exhibited a complex temporal evolution during and after the COVID-19 pandemic characterized by sustained seasonality and an increase followed by a decline. Although the ITS analysis did not identify statistically significant post-pandemic changes, the findings support the use of time-series models as valuable tools for epidemiological surveillance and trend monitoring.

Abstract: Influenza sentinel surveillance in Libya was formally established in 2022 by the Libyan National Center for Disease Control (NCDC), initially comprising a single sentinel site in Tripoli. By the end of 2025, the network had expanded to 15 sites across five cities nationwide. Between 2022 and 2024, a total of 1,864 nasopharyngeal specimens were collected from patients presenting with influenza-like illness and tested using the GeneXpert for influenza A virus, influenza B virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV). Influenza A virus was detected in 21.1% (393/1,864) of samples and influenza B virus in 5.4% (100/1,864). SARS-CoV-2 and RSV were identified in 11.6% (216/1,864) and 4.1% (77/1,864) of specimens, respectively. A subset of 29 influenza A–positive samples was randomly selected for confirmatory testing and further molecular characterization. Real-time RT-PCR subtyping identified 13 A(H1N1)pdm09 and five A(H3N2) viruses. Whole-genome sequencing was successfully performed for 13 isolates, followed by phylogenetic analysis. Genetic characterization revealed that all A(H1N1)pdm09 viruses belonged to clade 6B.1A.5a.2a (5a.2a), while A(H3N2) viruses clustered within clade 3C.2a1b.2a.2a.3a.1 (2a.3a.1) based on hemagglutinin gene mutations. No neuraminidase mutations associated with antiviral resistance were detected. This study represents the first molecular and phylogenetic characterization of circulating human influenza viruses in Libya, with sequence data submitted to the Global Initiative on Sharing All Influenza Data (GISAID). These findings establish baseline genetic data for influenza viruses in Libya and support the strengthening of national respiratory virus surveillance.

Abstract: Large language models (LLMs) have demonstrated logical reasoning abilities, but their inferences remain non-traceable and lack formal guarantees. We introduce eXa-LM, a controlled natural language (CNL) interface between LLMs and first-order logic solvers. Based on a Controlled Natural Language, our approach aims to create an explicit, verifiable, and interpretable bridge between text and formal logic. It relies on three main components: (1) a reformulation prompt that constrains the LLM to produce a set of facts and rules in CNL, (2) the semantic analyzer eXaSem translating this CNL into a Prolog program made of extended Horn clauses, and (3) the logic engine eXaLog, which integrates a second-order meta-interpreter capable of inferring ontological properties.We evaluate eXa-LM on three standard benchmarks—PrOntoQA, ProofWriter and FOLIO—comparing it to GPT-4o baselines including Standard prompting, Chain-of-Thought, Logic-LM, LINC, and LLM-TP. Results show that eXa-LM matches or exceeds recent neuro-symbolic systems while providing full traceability of reasoning and intrinsic explainability. On FOLIO, eXa-LM achieves 92.9% accuracy, a +5.5 point gain over LLM-TP, the strongest competing GPT-4o-based method in our comparison.This approach demonstrates the feasibility of a transparent neuro-symbolic reasoning pipeline in which LLMs produce not direct inferences but formally controlled linguistic representations. eXa-LM opens the way to neuro-symbolic architectures that are safer, verifiable and extensible, ultimately integrating hypothetical, abductive or inductive reasoning. Program and data will be made publicly available upon publication.

Abstract: In line with circular bioeconomy goals, the reported research focuses on circular building materials, intended as reused components, recycled and bio-based materials, including those derived from sub-products and waste, as a strategic solution to simultaneously cut embodied and operational carbon emissions in buildings. In particular, the research aims to provide a methodology for an early, rapid and effective assessment of the contribution that circular materials can give to reducing climate-altering emissions and resource consumption. The research started with the collection, selection and analysis of multiple case studies of buildings using circular materials and adopting different circular design strategies. The paper reports in particular the mapping of circular design strategies and materials in ten case studies, representing different approaches. Moreover, by collecting and comparing fifteen existing frameworks of indicators for circularity evaluation at the building and product level, selecting relevant indicators and integrating specific ones, the research develops a set of eight KPIS, a specific evaluation framework that allows to assess the effects of alternative combinations of materials reused, bio-based and recycled building materials. The KPIs set was tested on a selection of three relevant case studies of buildings using circular materials, to verify the effectiveness of the indicators in supporting the designer in taking material related choices.

Abstract: Highway–rail grade crossing (HRGC) safety research relies on federal incident and inventory datasets that span multiple decades. However, inconsistencies in geographic identifiers and incomplete reconstruction of crossing denominators can distort exposure-based rate metrics. This study develops, documents, and validates a reproducible nine-stage reconciliation pipeline applied to 51 years (1975–2025) of national HRGC incident data from the Federal Railroad Administration Form 57 and Form 71 datasets. The hierarchical pipeline integrated deterministic alignment and AI-assisted inference to produce an audited, geographically consistent dataset. The study formalizes four longitudinal county-level exposure metrics that quantify spatiotemporal risk. These metrics include accumulated incidents per million population (AIPM), accumulated incidents per crossing (AIPC), crossings per million population (CPM), and crossings per 100 square miles (CPHSM). All four metrics exhibited pronounced right-skewness: AIPM, CPM, and CPHSM approximated exponential forms, and AIPC approximated a log-normal form. Anderson–Darling tests detected statistically significant tail deviations in three metrics; CPM did not reject the exponential fit at conventional significance levels. Spatial analysis shows coherent regional concentration in incident rates in the Central Plains and lower Mississippi corridors. The national time series exhibits a late-1970s plateau, sustained exponential decline beginning around 1980, and stabilization but persistent incident rates after 2001. Population-normalized AIPM remained statistically indistinguishable between the reconciled and record-dropped datasets; however, crossing-based metrics changed materially when reconstructing denominators from the reconciled crossing universe. Median ratio comparisons confirmed that incident-only denominators introduced substantial measurement bias in local risk assessment. State-level rank reversals persisted even when omnibus distributional tests failed to reject equality. By formalizing multistage data cleaning and quantifying its analytical impact over an unprecedented longitudinal horizon, this study establishes denominator integrity and geographic reconciliation as prerequisites for valid HRGC exposure assessment and provides a replicable platform for future predictive modeling.

Abstract: Behavioural ecotoxicology is a field of applied ecotoxicology, where researchers consider the alterations in the behavioural markers due to the impact of environmental toxicants or contaminants. In fact, understanding the changes in behavioural manifestations helps to understand the respective underlying neurological mechanisms in the organisms and therefore, it effectively helps to describe or predict the neuro-behavioural context of behavioural modifications due to exposure to anthropogenic pollutants. Through this review we are addressing how environmentally available chemicals (such as pesticides, heavy metals (or metalloids), plastics and also pharmaceuticals can have significant acute and/or long-term impact on the behavioural profile of organisms (bioindicator species).