Abstract: Leaf senescence progressively remodels the photosynthetic apparatus, leading to impaired electron transport and declining carbon assimilation. Here, we investigated how dark-induced senescence (DIS) and exogenous 6-benzyladenine (BA) affect photosystem function, cyclic electron flow (CEF), photosynthetic protein remodeling and CO2 assimilation in two barley (Hordeum vulgare L.) cultivars differing in senescence characteristics, Carina (spring) and Lomerit (winter). DIS markedly reduced chlorophyll content, PSI and PSII photochemistry, electron transport and CO2 assimilation in both cultivars, although the underlying mechanisms differed. Carina maintained higher CEF despite stronger PSII inhibition, whereas Lomerit exhibited a greater decline in CEF accompanied by stronger donor- and acceptor-side limitations of PSI. These physiological responses coincided with selective remodeling of proteins forming the PSI-associated electron transport network, including coordinated changes in cytochrome f, PGRL1, NdhS, FNR and photosystem antenna proteins, indicating functional reorganization of photosynthetic electron transport rather than uniform chloroplast protein degradation. BA delayed senescence by preserving chlorophyll, maintaining PSI and PSII activity, sustaining CEF and partially alleviating the decline in CO2 assimilation. The protective effects of BA were more pronounced in Carina and coincided with more effective preservation of proteins associated with PSI-dependent electron transport. Collectively, our findings identify selective stabilization of the PSI-associated electron transport network as a central mechanism underlying cytokinin-mediated delay of leaf senescence in barley and demonstrate that cultivar-dependent regulation of this network determines the effectiveness of cytokinin-mediated protection of photosynthesis.

Abstract: Paradoxically, whilst historic Environment, Culture, Heritage, and Arts (ECHA) organisations curate knowledge for the public, thousands of them lack the required operational knowledge to manage their rapidly changeable and demanding operations. Their management requires extensive up-to-date knowledge, in accessible recorded formats (knowledge artefacts) interacting with human experience. For decades, the ECHA ecosystem, ECHAsystem [EE-kah-siss-tuhm], has been in a protracted crisis resulting in reduction of service for, and closures of, thousands of these organisations. The purpose of this cross-sectional, parallel mixed methods applied research was to develop a beneficial operational knowledge curating design to improve the ECHAsystem’s organisational sustainability. The findings demonstrated that accessible and up-to-date knowledge significantly improves processes and organisational sustainability performance. It also showed that a wise, allied development of knowledge resources, and allied application of available organisational resources, produces the greatest results. The research proposed the Athena [ah-thee-NAH] de-sign: a Knowledge Alliance coordinating the Operational Wisdom Library (OWL), in-formed by 31 networks across the ECHAsystem’s habitats and operations. The design can help individuals to grow professionally, organisations to thrive, and the ECHAsystem to achieve its aspirations. The research advocates a unified ECHAsystem benefiting from collaborative application of knowledge to deliver its public-good, intergenerational, organisational sustainability objectives.

Abstract: This study examines the environmental transformation of Puerto Princesa City, Palawan, from 2014 to 2022, with particular attention to how urbanization and population growth have reshaped land cover and local climate conditions. Using remote sensing data and demographic information, the analysis tracked changes in vegetation, built-up areas, and surface temperature patterns. Over the eight years, developed land nearly doubled, expanding from 34.4 km² to 67.7 km², largely at the expense of open spaces along the urban peripheries. Rural barangays showed mixed patterns, with some experiencing stagnation or decline in population and land conversion, while others exhibited localized forest recovery. The land surface temperature rose steadily, with mean values increasing from 26.9°C in 2014 to 28.1°C in 2022, and higher temperatures were consistently recorded in densely populated urban barangays. Built-up areas showed a stronger association with rising surface temperatures compared to vegetation cover, underscoring the role of urban expansion in driving thermal changes. The urban heat island effect, though fluctuating across the study years, became more widespread by 2022, with moderate-intensity zones expanding beyond the urban core. These findings point to significant ecological and climatic shifts, emphasizing that both rural development and urban densification contribute to rising heat stress. The resulting impacts on vegetation and surface temperatures of these land cover changes emphasize the need for integrated land use planning and climate-resilient urban development strategies in Puerto Princesa City.

Abstract: One of the most important adverse consequences of antibiotic use is the development of bacterial resistance. This study investigated the prevalence and antimicrobial resistance patterns of multidrug-resistant (MDR) Escherichia coli in a tertiary care hospital during 2013–2015 and 2024, together with trends in antibiotic consumption and the molecular characteristics of resistance genes in 2024 isolates. Identification and susceptibility testing were performed using the Vitek® 2 system, antibiotic consumption was assessed according to WHO ATC/DDD methodology, and resistance genes were detected by PCR. No significant differences were observed in the total number of isolates, patients, or the proportion of E. coli isolates during 2013–2015. However, the isolation rate of MDR E. coli significantly declined from 36.9% to 30%. Total antibiotic consumption remained stable, with no correlation between consumption and MDR isolation rates, although ampicillin resistance increased significantly. In 2024, MDR E. coli accounted for 24.3% of isolates despite lower antibiotic consumption. Compared with 2015, resistance significantly increased to amoxicillin-clavulanic acid, cefotaxime, cefepime, ceftazidime, ciprofloxacin, and levofloxacin, while ceftriaxone resistance decreased. The blaCTX-M gene was detected in 53% of bloodstream isolates, indicating widespread dissemination of ESBL-producing E. coli and highlighting the need for continuous surveillance and antimicrobial stewardship.

Abstract: One of the main causes of drug resistance in malignancies is metabolic reprogramming in the tumor microenvironment (TME). To maintain growth and alter the TME, cancer cells frequently rewire their metabolism, resulting in circumstances including food deprivation, hypoxia and acidity that hinder antitumor immune responses. The TME’s immune cells also experience metabolic changes, often taking on immunosuppressive characteristics that accelerate tumor growth and dampen the effectiveness of treatment. B cells are integral to the adaptive immune response, primarily through MHC class II-mediated antigen presentation and immunoglobulin secretion. Emerging evidence indicates that the functional role of tumor-infiltrating B cells is highly heterogeneous across malignancies. While their antigen-presenting capacity is generally associated with anti-tumor immunity, the contribution of B cell-derived antibodies remains ambiguous and context-dependent. Additionally, regulatory B cell subsets (Bregs) exert immunosuppressive effect within the TME by secreting anti-inflammatory cytokines such as interleukin (IL)-10 and tumor growth factor (TGF)-β, thereby facilitating immune evasion. Herein, we summarise the current understanding of B-cell functions in tumor immunology, which may shed light on potential therapeutic strategies against cancer.

Abstract: Background: Adrenal chromaffin cells integrate cholinergic and peptidergic signaling to regulate catecholamine secretion during stress. Although pituitary adenylate cyclase-activating polypeptide (PACAP] sustains adrenal secretion during intense sympathetic activation, the systems-level mechanisms coordinating chromaffin-cell activity remain incompletely understood. Perspective: We suggest that chromaffin cells function as dynamically recruited chromaffin-cell ensembles rather than independent endocrine units. Within this framework, connexin-mediated gap junctions act as activity-dependent amplifiers that enable PACAP- and acetylcholine-dependent recruitment of progressively larger ensembles according to physiological demand. We further suggest that ensemble recruitability is continuously modified by physiological state and previous experience. Conclusions: Building upon recent evidence and our experimental observations, we propose that early-life adversity remodels hypothalamic autonomic circuits, altering subsequent PACAP-dependent recruitment of chromaffin-cell ensembles during adult stress. This unified framework integrates peptidergic signaling, gap junction plasticity, and developmental programming, generating experimentally testable hypotheses and providing a new systems-level view of sympathoadrenal regulation.

Abstract: This paper presents a rigorous wavelet-based time–frequency anomaly detection framework designed to address volumetric User Datagram Protocol (UDP) flooding attacks in 5G networks within a Zero Trust Architecture (ZTA) context. By leveraging the Discrete Wavelet Transform (DWT) with the Daubechies db4 wavelet family, the proposed Multiscale Adaptive Zero Trust Anomaly Detection (MAZAD) framework decomposes user-plane packet-rate telemetry into multiple resolution levels, enabling the detection of both high-frequency traffic bursts and long-term congestion trends. The framework operates exclusively on metadata-based telemetry at the User Plane Function (UPF) and N6 interface, that is, the user-plane reference point between the UPF and external data networks in the 5G System, ensuring compatibility with fully encrypted 5G traffic environments. The paper uses classical DWT/MRA theory as the signal-processing foundation and contributes a task-specific MAZAD framework that combines db4-based multiscale packet-rate decomposition, scale-wise energy persistence, robust anomaly scoring, and bounded Zero Trust risk translation for metadata-only UDP flooding detection in 5G-inspired user-plane environments. A weighted multiscale anomaly scoring function is introduced to fuse information across frequency bands, capturing subtle deviations that are typically missed by single-scale approaches. Unlike conventional wavelet-based anomaly detectors that produce standalone alerts, MAZAD treats multiscale traffic deviations as policy-actionable risk evidence by converting scale-wise anomaly scores into bounded Zero Trust risk tiers for adaptive enforcement logic. The novelty of MAZAD lies not in the individual use of DWT, entropy, kurtosis, robust normalization, or sigmoid mapping, but in their task-specific integration into a lightweight, multiscale anomaly-scoring and Zero Trust risk-translation framework for metadata-only detection of 5G user-plane UDP flooding. Experimental evaluation using the Center for Applied Internet Data Analysis (CAIDA) DDoS Attack 2007 dataset demonstrates strong detection performance, achieving true positive rates (TPR) ranging from 92.1% to 100%, with false positive rates (FPR) as low as 1.4%–3.9%, and mean detection delays between 0.6 and 1.8 seconds. Comparative analysis shows consistent improvement over entropy-based methods, CUSUM, Isolation Forest, and short-time Fourier transform (STFT)-based approaches. The computational complexity of the framework is O(W) per analysis window, with an observed throughput of 2,439 windows per second, indicating promising suitability for real-time-oriented, metadata-based anomaly detection in 5G-inspired edge monitoring environments. However, further validation on live UPF telemetry and operational Zero Trust enforcement infrastructure remains necessary.

Abstract: The thread-per-request model's concurrency ceiling is not a theoretical concern — it manifests as latency collapse the moment an edge node saturates its OS thread pool. On constrained hardware with 2–8 cores and 4–16 GB of RAM, that ceiling arrives far sooner than in a data center, and no amount of pool tuning recovers throughput once you hit it. We built an asynchronous, non-blocking gateway framework around an event-driven I/O model, a lock-free request pipeline, and a back-pressure-aware routing layer, then measured it against an Apache mpm_prefork synchronous baseline on a commodity i5-1235U edge node. Peak sustainable throughput capacity reached 112,400 req/s — 3.8× the baseline's peak capacity of 29,600 req/s — with P99 latency falling from 69 ms to 18 ms at 80% offered load and CPU utilization dropping by 31% at equivalent offered load (range: 29–33% across five runs). Back-pressure propagation from an overloaded upstream to client-visible flow control ran a median of 4.2 ms (IQR: 3.8–4.9 ms). M/M/c queuing bounds for throughput, queuing delay, and resource utilization were derived before benchmarking; measurements stayed within 5% of model predictions for ρ ≤ 0.8 under stable load. At edge scale, async design is the baseline requirement for sustaining cloud-native connection counts.

Abstract: Predicting whether asset prices will rise or fall is essential for investment decision-making, since even modest improvements in directional accuracy can produce substantial economic benefits. This study proposes a dynamic sliding-window (DSW) framework for the daily directional classification of Exchange-Traded Funds (ETFs). Unlike conventional forecasting approaches that rely on either a fixed training sample or an expanding window, the DSW method allows the length of the estimation window to change at every prediction step. The underlying premise is that observations associated with market conditions that are most relevant to the current regime may provide more useful predictive information than a larger volume of older data. For each forecast, the optimal dynamic sliding-window size is selected through Bayesian optimisation applied to an internal validation segment of the available training sample, with a Gaussian process used as the surrogate model. A linear Support Vector Machine is then estimated using the observations contained in the selected window and a set of 39 technical indicators representing different dimensions of market behaviour. The empirical evaluation is conducted on 156 US-listed ETFs obtained from Yahoo Finance over the period from September 2019 to September 2024, yielding more than 170,000 daily observations. The dynamic sliding-window model achieves statistically significant improvements across all evaluation measures when compared with both an expanding, or stretching, window and a conventional train–test split. These results show that dynamically adapting the amount of historical data used for model estimation can improve financial time-series classification. The proposed DSW framework therefore provides a scalable and transparent approach to forecasting in markets characterised by changing regimes and persistent volatility.

Abstract: Issue addressed: High consumption of ultra-processed foods (UPFs) is linked to poor health outcomes, yet consumers often struggle to recognise and interpret food processing. Digital tools using artificial intelligence (AI) can support nutritional literacy and UPF awareness. This study evaluated a HISS (Human Interference Scoring System)–based mobile application designed to classify foods by processing level and support dietary self-reflection. Methods: A three-day quantitative usability study was conducted in New Zealand. Thirty-one participants (13 adolescents aged 12–18 years, eight tertiary students aged 19–25 years, and nine Māori and Pacific health coaches) logged all meals, snacks and beverages using the HISS app. AI classification accuracy was assessed against expert ratings of food images. App engagement was measured using in-app metrics, and usability and perceived impact were assessed via surveys. Results: The AI system achieved 93% accuracy for HISS category classification. App engagement varied across features, with most time spent on meal logging and AI interaction screens. Adolescents and health coaches reported high usability and usefulness, while tertiary students expressed more mixed intentions regarding ongoing use. Conclusions: The app demonstrated high classification accuracy and was generally well received, particularly among users with lower baseline nutrition literacy. Findings support the feasibility of using AI-enabled image recognition to support awareness of UPF intake. So what?: Evidence based AI food classification tools such as HISS show promise for scalable UPF reduction. With further development and evaluation, such tools may support nutrition education and behaviour change in community and clinical settings.

Abstract: Second-order power electronic systems are integral to many grid-tied and motor-drive applications, yet their inherent LC-filter dynamics often trigger undesirable oscillations and overshoot during transient events. These control challenges are further compounded by dc-link constraints, under which controller saturation degrades performance. Model predictive control (MPC) is commonly employed to handle such constraints, but its real-time implementation presents a fundamental trade-off: short prediction horizons lead to sub-optimal tracking, while long horizons deliver near-optimal performance at the cost of a prohibitive computational burden. As a result, previous approaches have been unable to achieve near-optimal reference tracking within the computational limits of standard microcontroller units (MCUs). To overcome these limitations, this paper proposes a time optimal control (TOC) scheme based on Pontryagin's minimum principle (PMP), tailored specifically for second-order systems in transient conditions. A rigorous mathematical derivation of the control scheme is presented, covering both undamped and damped systems across low- and high-frequency time-varying reference tracking scenarios. The resulting quartic equation is solved in a form that ensures real-time implementation. Experimental results show that the proposed TOC algorithms reduce total arithmetic operations by at least an order of magnitude compared to MPC, while maintaining excellent transient response and tracking accuracy.

Abstract: Electroencephalography (EEG) is a non-invasive technique for investigating brain activity; however, deep learning analysis remains challenging due to inter-subject variability and heterogeneous recording conditions. Most previous studies have focused on single disorders or datasets, while systematic evaluation across heterogeneous public datasets has received less attention. In this multi-dataset case study, a unified preprocessing, data harmonization, and leakage-free evaluation pipeline was applied to systematically evaluate four established deep learning architectures (CNN-1D, EEGNet, ShallowConvNet, and TCN) for subject-independent five-class EEG classification of healthy controls, Alzheimer’s disease, depression, Parkinson’s disease, and epilepsy. EEG recordings from OpenNeuro and PhysioNet included 144 subjects and 24,354 epochs. Standardized preprocessing, training-data-based channel-wise normalization, and subject-wise stratified partitioning were applied to prevent data leakage and ensure reliable evaluation. Five independent random seeds were used to assess result stability and reproducibility. Mean accuracy ranged from 73.14% to 83.66%, with macro-AUC values between 0.9389 and 0.9785. ShallowConvNet achieved the best performance, with an accuracy of 83.66 ± 4.01%, macro F1-score of 82.46 ± 3.68%, and macro-AUC of 0.9785±0.0102. Gradient-based saliency analysis identified EEG channels contributing to model predictions, providing insight into learned representations and supporting future investigations of potential EEG biomarkers. The findings indicate that the proposed evaluation pipeline enables a fair and systematic comparison of existing deep learning architectures across heterogeneous public EEG datasets and may support future validation on additional independent datasets.

Abstract: This article introduces the concept of Normative Phonetic Inclusiveness (NPI), defined as the degree to which a speech community recognizes multiple phonetic realizations of the same phonological category as equally legitimate within a shared linguistic norm. Using the Dutch rhotic (/r/) system as an exploratory case, the paper argues that the sociolinguistic significance of phonetic variation extends beyond its role as a marker of social identity to include the social organization of linguistic legitimacy. Contemporary Dutch accommodates a wide range of rhotic realizations without systematically privileging a single variant, providing an opportunity to examine how linguistic communities regulate diversity within a stable normative framework. Rather than proposing a causal relationship between pronunciation and broader cultural values, the article suggests that linguistic norms and other social norms emerge within the same historical and institutional ecology. Drawing on sociolinguistics and classical sociological theories of norms, the study conceptualizes NPI as a property of speech communities rather than individual speakers and distinguishes it from established concepts such as phonetic variation, linguistic prestige, and language ideology. The Dutch case serves as a theoretically motivated illustration for developing a broader framework of linguistic legitimacy, with the aim of stimulating future comparative research on how different speech communities organize the acceptance of phonetic diversity.

Abstract: The banana pseudostem is a plentiful lignocellulosic biomass residue that results from banana cultivation and which is typically thrown away without proper usage. The effect of particle sizes and moisture content on biogas formation from banana pseudostem under anaerobic digestion conditions has been studied in this experiment with FTIR used to assess structural degradation of the lignocellulosic biomass. Three different sizes (8, 14, and 18 BSS) of banana pseudostems have been taken and their moisture contents varied into three percentages, namely 70%, 80%, and 90%. Anaerobic digestion studies have been performed for 21 days with cow dung slurry as the inoculum. It was clearly evident from the findings that both particle sizes and water contents had a profound effect on biogas formation. In week one, the maximum amount of biogas (70 mL) formed by any treatment was that of 8 BSS with 80% water content. However, in weeks two and three, the maximum amount of biogas (95 mL) was formed by the 18 BSS treatment having 80% water content. The cumulative analysis of biogas formations found the treatment combination of 18 BSS particle size with 80% water content to be the optimum treatment producing 238 mL of biogas. The gas analysis by gas chromatography confirmed the presence of methane gas in the produced biogas. Further, it was observed through FTIR analysis that there were gradual changes in the functional groups of cellulose, hemicellulose, and lignin components. This research demonstrates that banana pseduostem is a potential renewable feedstock for biogas production, and an optimized particle size and moisture content have significant effects on improving anaerobic digestion efficiency. These results contribute to the utilization of agricultural waste from bananas as renewable energy sources.

Abstract: The cislunar space, governed by the circular restricted three-body problem (CR3BP) butsubject to additional perturbations in high-fidelity models, presents significant challenges for mission design due to its complex stability structure. Traditional numerical integration is computationally prohibitive for a systematic energy-regime census of millions of orbits. Here, we present a novel approach based on global volunteer computing via the BOINC platform to overcome this barrier. Using the public “Million Orbit” dataset from Lawrence Livermore National Laboratory (generated with a high-fidelity model including solar and planetary perturbations), we distributed the computation of time-resolved Jacobi constant sequences across thousands of volunteer devices, producing over 16 billion individual values. The resulting dataset is freely available. Analysis reveals that the majority of orbits are high-energy escapes (Region V), while a non-negligible fraction belong to the low-energy Region I, with a small number of intermediate cases. A single rare Region IV orbit (ID 754482) is identified and analysed. Furthermore, we develop a lightweight deep learning surrogate that predicts whether an orbit belongs to Region I using only the first K Jacobi constants. Our model combines an LSTM encoder with attention and an XGBoost classifier, achieving test AUC of 0.984 with K = 500 and 0.929 even with K = 10, outperforming a raw XGBoost baseline. This work demonstrates the transformative potential of volunteer computing for large-scale astrodynamics and provides an efficient machine learning tool for real-time orbit screening.

Abstract: The increasing number of job applications received by organizations has made manual resume screening a time-consuming and resource-intensive process. Transformer-based language models have emerged as a promising solution for automating recruitment tasks by understanding contextual information and semantic relationships within resumes and job descriptions. This study benchmarks several state-of-the-art transformer models, including BERT, RoBERTa, DistilBERT, and DeBERTa, using publicly available recruitment datasets to evaluate their effectiveness in automated resume screening. A qualitative research approach based on secondary data analysis is employed to compare the models across multiple performance indicators reported in previous studies, such as accuracy, precision, recall, F1-score, computational efficiency, and inference time. The findings indicate that while larger transformer models generally achieve higher predictive performance, lightweight models provide competitive results with lower computational costs. The study highlights the strengths and limitations of each model and proposes recommendations for selecting suitable transformer architectures in recruitment systems. The research contributes to the growing field of AI-driven human resource management by providing a comprehensive comparative analysis.

Abstract: The rapid development of rural e-commerce has become an important pathway for promoting sustainable rural transformation, yet its impacts on territorial resilience and underlying land-use mechanisms remain insufficiently understood. This study investigates the effects of Taobao Village development on county-level territorial resilience and examines the mediating role of ecological land-use transition in China. Using panel data from 1,503 counties between 2014 and 2022, we employ a staggered difference-in-differences model, spatial Durbin model, and Bootstrap mediation analysis to identify the causal effects, spatial spillovers, and transmission pathways. The results show that: (1) Taobao Village development significantly improves territorial resilience, with stronger effects observed in central and northeastern China, while the impacts vary considerably across regions due to differences in economic foundations, digital infrastructure, and land-use conditions. (2) Spatial analysis reveals that the resilience-enhancing effects of Taobao Villages are mainly localized, with no significant spillover effects to neighboring counties, indicating the constraints of existing administrative and spatial governance systems. (3) Ecological land-use transition serves as an important transmission pathway linking rural e-commerce development and territorial resilience, suggesting that land-use restructuring induced by digital transformation can generate both development opportunities and ecological trade-offs. (4) The mediation analysis identifies a suppression effect, indicating that although Taobao Village development may initially exert pressure on ecological spaces, ecological optimization ultimately contributes positively to long-term resilience improvement. These findings highlight the importance of integrating digital rural development with ecological sustainability and territorial spatial governance. The study provides policy implications for promoting resilient and sustainable rural transformation through differentiated land-use strategies.

Abstract: For over three decades I have collected and maintained sand scorpions in my research laboratory. I have learned that these animals are not only easy to collect but also accepting of laboratory conditions. In this note, I followed a population of 34 sand scorpions collected from a sand field near Monahans, Texas and housed under stable conditions in jars in my lab at the University of Oklahoma. I kept track of their feeding and watering schedules and noted their date of death. In the end, I generated a post-collection survivorship curve for this population. I hope that this account can help guide those interested in working with these exquisite animals and provide a baseline for comparing with other populations of collected animals held under laboratory conditions.

Abstract: Somatostatin receptors are being investigated as therapeutic targets for high‑risk neuroblastoma stage 4 (NBS4) due to their overexpression on chromosome 17q. This study explores the design of potential somatostatin receptor agonists that activate SSTR2 signalling pathways. It evaluates their ability to bind two additional overexpressed GPCRs located on chromosome 17: galanin receptor type 2 (GALR2) and corticotropin‑releasing hormone receptor 1 (CRHR1). Using a multi‑platform computer‑aided drug design workflow—including OpenEye Scientific Suite, Samson Suite, Flare Suite, and Discovery Studio Visualizer—eight candidate compounds were identified and assessed for docking performance, pose reliability, and structure–activity relationship (SAR). Their absorption, distribution, metabolism, excretion, and toxicity profiles were evaluated using T.E.S.T., DEEP‑PK, and SwissADME. Retrosynthetic analysis using ChemAIRS and Spaya confirmed synthetic feasibility, with compound 8 achieving an RScore of 1.0. Compound 8 demonstrated strong and consistent predicted binding to SSTR2, CRHR1, and GALR2, with interaction patterns consistent with those reported for known SSTR2 agonists. These findings suggest that compound 8 is a promising multitarget GPCR ligand candidate for high-risk NBS4 and warrants further experimental evaluation of its pharmacological activity.

Abstract: This work develops a comprehensive functional-analytic framework for the study of viscous fluid flows within the setting of critical Besov spaces and Littlewood--Paley theory. We establish optimal global well-posedness for the incompressible Navier--Stokes equations in the scale-invariant spaces \(B^{n/p-1}_{p,q}\) for all \(1\le p<\infty\), \(1\le q\le\infty\), providing a complete proof of the bilinear estimates via Bony's paraproduct decomposition and demonstrating the requisite commutator estimates within this framework. We then prove a sharp version of the energy conservation criterion in the inviscid limit: any sequence of weak solutions with regularity \(L^3(0,T;B^{1/3}_{3,\infty})\) has vanishing energy dissipation anomaly, resolving Onsager's conjecture for the Navier--Stokes equations. The core of the paper is devoted to the compressible Navier--Stokes--Fourier system, for which we identify the critical regularity indices \(s_\rho = n/p\) for the density and \(s_u = n/p-1\) for the velocity, and establish global well-posedness using Strichartz estimates for the acoustic operator. We then study the singular high-Mach number limit for arbitrary large initial data (under the technical condition \(p\le n\) for the compactness argument), proving that the acoustic modes decouple completely and the velocity field converges strongly to a solution of the incompressible Navier--Stokes equations. As a direct consequence, we derive the limiting vorticity equation and show that the baroclinic torque vanishes, leading to the conservation of circulation in the limit. This rigorously justifies the persistence of vortex structures in highly compressible flows. Finally, we incorporate a quaternionic bifurcation analysis for rotating flows, illustrating the versatility of the framework for studying symmetry-breaking instabilities. This work establishes new mathematical foundations for turbulence modeling, large-eddy simulation, and vortex dynamics, contributing to the broader effort toward resolving the global regularity problem for the Navier--Stokes equations.