Abstract: The method of X-ray Footprinting and Mass Spectrometry (XFMS) using high brightness synchrotron X-ray sources has become an established method in structural biology and is based on the radiolytic production of hydroxyl radicals which oxidatively modify protein sidechains. While other methods of producing hydroxyl radicals are available, one benefit of using high flux density sources is that hydroxyl radical scavenging reactions can be minimized, and exposure times kept short to minimize secondary reactions. Here we present an application of the XFMS method using low dose rate X-rays from a commercial instrument. We demonstrate the feasibility of the approach using short peptides, characterizing the oxidative modifications +14, +16, and +32 Da under both aerated and low-oxygen conditions, and we additionally quantify the hydrogen peroxide production for various doses using the low dose rate source. These results provide fundamental information on the oxidative damage to peptides due to hydroxyl radicals using a low dose rate X-ray source.

Abstract: Biological systems display phenomena—particularly in enzymatic catalysis, excitonic coherence, and protein folding—that appear to exploit selective stabilisation of microstates beyond what standard quantum mechanics typically predicts for warm, noisy environments. We propose that these deviations can be interpreted as signatures of an informational reservoir: a hidden, aperiodic layer of structured information accessible only to sufficiently complex biological systems. Standard quantum mechanics then emerges as a limiting, coarse‑grained description in which the reservoir term vanishes. The proposed reservoir is not reducible to any finite set of underlying parameters; instead, it functions as a high‑complexity information landscape that can be “read” only by finely organised biomolecular architectures. We outline empirically testable predictions and discuss implications for biological stability, functional directionality, and the physical foundations of living systems.

Abstract: Water scarcity compels wastewater reuse, but lax discharge standards generate a regulatory mirage, misleading the public about safety. Despite formal compliance, treated effluent severely harms Iran’s effluent‑dependent Kashaf River, driving eutrophication, salinization, and the downstream transport of unregulated contaminants of emerging concern, including PFAS and pharmaceuticals. These pressures extend beyond the river channel to adjacent natural wetlands, which act as de facto nature‑based treatment systems yet are progressively transformed into sacrificial sinks for excess nutrients, salts, heavy metals, and micropollutants. By benchmarking the Iranian Wastewater Discharge Standards (IWDS) against international guidelines (WHO, EU, FAO), this study quantifies a “Permissibility Gap” frequently greater than 10 for key parameters such as BOD₅, nutrients, and trace metals, revealing how concentration‑based limits ignore cumulative mass load and mixture toxicity at the basin scale. The Kashaf River case demonstrates that current end‑of‑pipe regulation undermines both natural wetlands and planned nature‑based solutions, including constructed wetlands, in arid regions where effluent reuse is unavoidable. The study argues that aligning discharge standards with global benchmarks, adopting mass‑based permits, and explicitly regulating contaminants of emerging concern are prerequisites for truly safe wastewater reuse and for protecting wetland ecosystems in effluent‑dependent basins.

Abstract: This paper presents the structural and safety design of a low-cost electric three-wheeler intended for use in the densely populated urban environment of Dhaka, Bangladesh. The goal of this project was to improve currently available informally manufactured or unregulated motorised vehicles, which often have unsafe structural features, such as a high centre of gravity and inadequate braking systems. The vehicle is designed to accommodate five people (one driver and four passengers), reach a maximum speed of 30 km/h, and be manufactured locally at an estimated cost of 1200–1400 EUR. The vertical centre of gravity was determined to be 0,642 m above ground level, resulting in a static stability factor of 1,09. Structural performance was evaluated using ANSYS Mechanical under combined static loading conditions and a simulated frontal impact at 30 km/h. The redesigned tubular frame reduced maximum upward deflection by 15,6% and increased energy absorption during frontal collision by 37,3% compared to previous designs. Braking performance analysis showed that the vehicle can stop within 10 metres from 25 km/h, while rotor temperatures maintained a 108 °C margin below the fade threshold for brake fade during repeated emergency braking. The results demonstrate that substantial improvements in structural safety and thermal performance can be achieved in low-cost electric three-wheelers using locally available manufacturing resources.

Abstract: Early diagnosis of post-traumatic epilepsy (PTE) is crucial for timely intervention. However, it is hampered by the lack of reliable biomarkers. In this review, we provide a comprehensive summary of current advances in PTE biomarker research covering (i) neuroimaging, including CT, MRI, and EEG/qEEG, which reveal structural and functional alterations associated with epileptogenesis; (ii) molecular biomarkers, including RNAs, proteins, metabolites, and extracellular vesicle (EV)–derived molecules that reflect neuroinflammation, blood–brain barrier dysfunction, neuronal injury, and synaptic remodeling; and (iii) artificial intelligence (AI)–assisted approaches, which integrate multimodal datasets to identify complex predictive patterns. While individual modalities offer valuable but incomplete prognostic information, AI-driven analytics hold the greatest promise for early predictive power when combining multimodal data. Future progress will depend on the combination of high-resolution imaging, multi-omics profiling, and rigorous validation to deliver clinically actionable biomarker panels and ultimately reduce the burden of PTE.

Abstract: After decades of in vivo isotope tracing, human solid tumors have not been shown to derive the majority of their carbon from circulating glucose. Despite this, glucose uptake by tumors continues to be widely interpreted as evidence of glucose dependence for growth. In contrast, mounting clinical and metabolic evidence indicates that glucose and glutamine are consumed primarily as regulatory and competitive substrates rather than as dominant carbon sources, with tumor biomass supplied largely by lactate, glutamine, and host-derived amino acids and lipids.Cachexia is commonly described as a secondary complication of advanced cancer, yet this metabolic behavior suggests it functions instead as a tumor-maintained systemic state that favors malignant survival at the expense of host tissues. By consuming glucose and glutamine at high rates, tumors restructure host metabolism, suppress immune function through substrate deprivation, and induce a catabolic shift that mobilizes host tissues as the tumor’s true nutrient reservoir. Dietary deprivation strategies therefore fail in solid tumors not because tumors adapt to starvation, but because restriction accelerates host metabolic collapse rather than depriving the tumor.Central to this argument is a newly proposed construct: homeostatic deception via dissociated catabolic ketosis, a tumor-orchestrated state in which physiological ketogenesis is genuinely present but decoupled from its normal protein-sparing function. Circulating ketones satisfy central energy-sensing mechanisms, silencing counter-regulatory alarms while unrestrained muscle proteolysis and lipolysis proceed. The resulting catabolic loop supplies tumors with substrates released from host tissues while the host’s regulatory systems interpret the state as normal adaptive fasting. Cachexia persists as long as the tumor driver remains active and reverses primarily when tumor burden and inflammatory signaling are controlled. A case of metastatic NSCLC, with photographic documentation, serves as the observational origin of this framework (Johnson CL, 2026, https://doi.org/10.5281/zenodo.18988466). This manuscript integrates metabolic tracing, immunometabolism, and clinical observation to propose a mechanistic hypothesis reframing cachexia as a tumor-maintained state. The framework identifies multiple targets for companion therapeutic intervention and explains the failure of diet-based strategies.

Abstract: Because of the recent urbanization and exponential population growth there has come significant pressure on urban transportation systems. Therefore, people encounter many traffic challenges now a days like traffic congestion, environmental pollution, inefficient public transport, and safety concerns. Smar transportation has been talked about recently in this regard which has emerged as a promising solution for addressing all the mentioned challenges. It integrates advanced technologies such as machine learning, internet of things (IoT) and big data analytics into transportation infrastructure. In this research we aim to explore the role of big data in enhancing smart transportation systems and improving urban mobility. In this research we discuss key challenges that are associated with urban mobility and introduce the concept of smart transportation as solution which is technology driven and automated. We also examine later in this research the characteristics and applications of big data in transportation optimization, including traffic congestion management, public transport route optimization, accident prevention, environmental sustainability, ride-sharing services, and multimodal transportation systems. We also discuss in detail a case study of data-driven ride-sharing platforms such as Uber to show how real-time data analytics can improve mobility efficiency and service reliability.

Abstract: The aim of this work is to demonstrate that, for spacetimes with sufficient symmetry, spin coefficient conditions may reduce to compact metric-component identities that are useful for practical metric analysis. This idea is illustrated using the symmetries of the Kerr spacetime. For a vacuum Petrov Type D spacetime admitting geodesic, shear-free null congruences, as characterised by the Goldberg–Sachs theorem, the shear spin coefficient, $\lambda = 0$, can be reformulated using a principal-null-aligned (Kinnersley) tetrad. The resulting relation can be expressed solely in terms of metric components and their radial derivatives within a Kerr-like coordinate gauge.To the best of the author’s knowledge, this is one of the first explicit, coordinate-dependent metric identities corresponding to the vanishing of a Newman–Penrose spin coefficient. The resulting condition eliminates explicit tetrad dependence, yielding a purely metric-level identity that can be evaluated once a Kerr-like Boyer–Lindquist gauge is fixed.This reformulation provides a practical diagnostic for verifying the shear-free property of principal null congruences directly from the metric, without constructing a tetrad or imposing a specific ansatz. As such, it offers a useful tool for constraining or partially reconstructing stationary, axisymmetric spacetimes under appropriate symmetry and geometric assumptions. The expression has been validated numerically for several Kerr-like spacetimes, including Kerr, Kerr–Newman, Schwarzschild, and static de Sitter metrics. This points toward a bridge between tetrad-based geometric characterisations and coordinate-level analyses of spacetime structure.

Abstract: Background: Autism spectrum disorder (ASD) affects approximately 1-2% of childrenworldwide, yet its etiology remains incompletely understood. Emerging evidence suggeststhat offspring of parents with autoimmune diseases show elevated autism prevalence.Notably, children of parents with psoriasis (OR 1.59), type 1 diabetes (OR 1.49-2.36), andrheumatoid arthritis (OR 1.51) demonstrate particularly strong associations. Hypothesis: I propose that autism may be conceptualized as an immune-metabolic disorderin which TNF-α-mediated mitochondrial dysfunction contributes to cerebral energydeficiency. This energy deficit impairs three critical processes: (1) synaptic pruning duringneurodevelopment, (2) real-time social cognition including gaze processing and emotionrecognition, and (3) protein synthesis of critical synaptic scaffolding molecules.The proposed mechanism is TNF-α pathway dysregulation arising from inheritedinflammatory susceptibility and/or direct fetal exposure to elevated maternal TNF-α duringpregnancy.I further propose that the well-documented “firstborn effect” in autism reflects maternalimmune maladaptation during primigravid pregnancies. Additionally, for cases withoutparental autoimmune history, a speculative secondary mechanism is proposed: mitonuclearimmune conflict, where paternal immune genes may partially recognize maternalmitochondria as non-self, generating endogenous TNF-α.

Abstract: We study the canonical string-based Assembly Index (ASI), defined as the minimum number of binary concatenations needed to construct a target word under full reuse. NP-completeness of ASI-DEC over general finite alphabets and an equivalence between ASI plans and straight-line programs (SLPs) under the same size convention has been established. We emphasize that all transfers between decision variants are effected by explicit polynomial-time mappings and (where needed) an explicit reparameterization of the threshold by an absolute constant or a simple affine function. The remaining technical obstacle for the binary alphabet is that a naive encoding reduction may allow an optimizer to exploit “cross-boundary” substrings created by overlaps of codewords. We give a fully self-contained binary-alphabet proof: we construct an explicit self-synchronizing (comma-free) codebook of 17 fixed-length binary codewords and prove a boundary-normalization lemma showing that optimal plans can be assumed aligned to codeword boundaries. This yields a polynomial reduction from fixed-alphabet ASI-DEC to binary ASI-DEC, proving NP-completeness over {0, 1}. Using the recalled ASI–SLP equivalence (with a short proof for completeness), we obtain NP-completeness of binary SLP-DEC. We additionally provide an explicit, fully formal translation between our binary-rule counting convention and the standard SGP size measure (sum of right-hand side lengths), showing that the NP-completeness classification transfers to common one-string SGP/SLP decision variants over {0, 1}.

Abstract: In the current paper, the nonlinear absorption characteristics and laser modulation performance of the ternary anisotropic semiconductor material ZrGeTe₄ were successfully explored. The recovery time of the ZrGeTe₄-PVA thin film was measured to be 5.74 ps by pump-probe technology. By employing ZrGeTe₄ as a saturable absorber, a passive mode-locked Yb-doped fiber laser was demonstrated for the first time. In the 1 µm mode-locked operation, the central wavelength is 1031.29 nm, the pulse repetition rate is 24.85 MHz, and the pulse width is 786.3 ps. In an Er-doped fiber laser operating at the wavelength of 1561.10 nm, the pulse width as short as 1.26 ps with a repetition rate of 4.38 MHz. The results show that ZrGeTe₄ has excellent broadband nonlinear optical characteristics.

Abstract: We introduce a divergence-based framework for structural normalization and constrained reconstruction in generative models for poetic translation. The central hypothesis is that a text admits a contextualized, language-independent structural representation capturing semantic, prosodic, rhetorical, cultural, and aective invariants independently of surface linguistic form. A normalization operator embeds each text into a domain-dependent structural manifold conditional on a contextual knowledge state Kt. Reconstruction in a target language is formulated as divergence-minimizing projection under explicit constraint functionals. Structural preservation is quantied through domain-dependent divergence between probability measures induced by structural representations. Cross-linguistic transfer is interpreted as analogical alignment between contextualized structural states. Because structural representation depends on the contextual knowledge state, epistemic updates modify the geometry of structural comparison and may induce time-indexed optimal realizations. The proposed formulation establishes a mathematical perspective on translation as constrained structural projection in contextualized measure spaces, separating relational invariants from surface realization and enabling controllable generative reconstruction under explicit structural validation.

Abstract: In-cylinder pressure is a critical parameter for assessing the combustion process and per-formance of spark ignition internal combustion engines. However, obtaining measured in-cylinder pressure data across the full operating range, particularly at various spark advance angles (SA), is costly and technically demanding, limiting its widespread application in engineering practice. This study proposes two artificial neural network (ANN) based in-cylinder pressure reconstruction methods utilizing in-cylinder pressure and heat release rate data from a three-cylinder motorcycle gasoline engine under varying spark advance angle conditions, aiming to achieve cost-effective, high-precision pressure prediction through machine learning technology. Both pressure reconstruction methods employ crank angle and spark advance angle as input features. Method 1 (ANN-P) directly predicts the in-cylinder pressure curve, achieving a coefficient of determination R² exceeding 0.99 on both training and validation sets with a root mean square error (RMSE) below 0.13 bar, accurately reproducing the pressure evolution throughout the compression, combustion, and expansion processes while achieving high-precision prediction of indicated mean effective pressure (IMEP). Method 2 (ANN-HRR) adopts an indirect strategy of "predicting heat release rate followed by integration to reconstruct pressure." This method first derives the apparent heat release rate (HRR) from measured in-cylinder pressure data, trains a machine learning algorithm with HRR data to predict the target operating condition's HRR curve, then reconstructs in-cylinder pressure through integral inversion based on a single-zone thermodynamic model, thereby avoiding error amplification caused by differential operations. This approach demonstrates superior performance in predicting combustion characteristic points (CA10, CA50). The results demonstrate that both methods accurately capture the influence of spark timing on combustion phasing and peak pressure. Method 1 achieves high accuracy in predicted pressure curves but exhibits lower accuracy in capturing combustion characteristics; Method 2 effectively compensates for the limitations of Method 1 in characterizing combustion features through heat release rate curve prediction. This study provides an economical and efficient technical approach for gasoline engine combustion diagnosis, performance calibration, and control optimization.

Abstract: This systematic literature review aims to first, explore the influence of remote work on occupational health and safety, in terms of mental and physical health and second, to shed light on best practices that can be adopted to improve the health and safety of employees working remotely. Twenty-four (24) peer-reviewed articles published from 2020 to 2024, were selected through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review identified four themes, namely–positive impact of remote work; negative impact of remote work; challenges associated with remote work, and best practices for effective remote work practices. Findings from the study revealed that the impact of remote work on OHS was mixed. Eight (8) papers found that remote employees performed better in their OHS, whereas 18 papers found the opposite. The most dominant health disorders reported were depression, stress, anxiety and musculoskeletal pain. In contrast, the study identified that vertical trust levels and job design that considers physical and psychosocial aspects of the job can enhance safety while working remotely. Remote workers are encouraged to follow ergonomics best practices, take regular breaks during work to stretch and move around to reduce musculoskeletal disorders.

Abstract: The Pauli exclusion principle is traditionally introduced in quantum mechanics as a postulate encoded in the antisymmetry of the fermionic wavefunction. While extraordinarily successful, this formulation leaves open a deeper question: why must nature forbid the perfect overlap of identical fermions? In this work, we propose a reinterpretation of Pauli exclusion within the framework of Viscous Time Theory (VTT), where physical law emerges from the geometry of informational state space under constraints of memory, recoverability, and causal trace preservation. We propose that the coincidence of two identical fermionic states can be interpreted, in informational-geometric terms, as a loss of injectivity of the causal mapping, i.e., to an informational singularity where distinct histories become non-separable. To prevent this collapse of recoverability, the joint state manifold naturally develops a “diagonal barrier”: a forbidden submanifold where the informational cost diverges and admissible trajectories are repelled. Within this perspective, antisymmetry of the wavefunction appears not as the cause of exclusion, but as its mathematical symptom. Within this perspective, Pauli exclusion can be interpreted as a geometric and informational constraint rather than a primitive quantum axiom. The framework further suggests a unified interpretation of the difference between fermions and bosons: the former may be viewed as carriers of identity-bearing, non-overwritable informational structure, while the latter correspond to additive excitations that do not threaten causal injectivity. In this way, the exclusion principle appears as a consequence of informational geometry in a universe characterized by viscous time and memory.

Abstract: Monitoring equity drawdown risk requires real-time indicators that can be implemented without look-ahead bias and that may add information beyond standard volatility measures. This study develops a leakage-safe residual-stress indicator from cross-sectional PCA reconstruction errors in U.S. sector excess returns and examines whether it contributes to drawdown-risk monitoring. Using daily adjusted prices for SPY and 11 U.S. sector ETFs over 2020–2025, this study computes sector excess returns relative to SPY, estimate the common component with principal component analysis (PCA), and define residual stress as the cross-sectional root-mean-square magnitude of out-of-sample reconstruction residuals. The PCA mapping is estimated using information available only through t − 1, stress is computed at t, and high-stress regimes are defined using rolling train-only quantile thresholds shifted forward by one trading day. Performance is evaluated using drawdown- onset events and early-warning metrics including ROC-AUC, PR-AUC, and horizon-H precision and recall. The results indicate that although residual stress is not a superior standalone alternative to realized volatility, it remains the stronger benchmark in overall classification performance. Residual stress is most useful as a complementary indicator of cross-sectional market dislocation rather than as a replacement for volatility. In the baseline sample, residual-stress spikes cluster around drawdown onsets, and conditional regime analysis shows that when volatility is low, high residual stress is associated with a materially higher probability of a drawdown onset within the next H = 21 trading days than the low-stress/low-volatility regime. Event-overlap and lead-time diagnostics further suggest that residual stress can flag a subset of onset episodes not captured by a simple volatility- threshold rule, although its primary incremental value lies in conditional risk stratification rather than in systematically earlier triggering. A longer-history proxy-sector analysis yields similar evidence of conditional complementarity while also confirming the stronger standalone performance of volatility. The paper’s contribution is to develop a leakage- safe, interpretable, cross-sectional residual-stress diagnostic that improves conditional drawdown-risk stratification, especially in otherwise low-volatility states, rather than a standalone replacement for realized volatility. This interpretation is supported by both a modern ETF baseline sample and a longer-history proxy-sector sample, with the broader sample providing more stable evidence of complementarity across a larger set of drawdown onsets

Abstract: We study real-valued transition profiles on the real axis that admit holomorphic extension to a horizontal strip in the complex plane. The functions considered have a continuously differentiable and nondecreasing real trace and are normalized to take values strictly between zero and one. We assume that the associated conformal transform obtained by rescaling and shifting the profile extends holomorphically to the strip and maps it into the unit disk. Under these conditions strip analyticity imposes a sharp pointwise bound on the rate of change along the real axis. The bound depends only on the width of the analytic strip and is optimal. We further prove a rigidity result: if the bound is attained at any real point then the profile is uniquely determined up to translation and coincides with the logistic transition. The argument is purely analytic and follows from the Schwarz–Pick contraction principle applied to the strip geometry. No classification of non-saturating profiles is attempted.

Abstract: The prevalence of communicable diseases (also known as transmissible or infectious diseases) has been a significant health issue for sustainable development, hindering economic progress globally. Communicable diseases may affect economic development both directly (via the immediate effects of ill health on productive activities) and indirectly (through the effects of disease on intellectual capacity, mortality, morbidity, and fertility). This study examined the impact of communicable diseases on the economic growth of the SADC region between 2011 and 2024. The study utilised the two-step System-Generalised Method of Moments (GMM) to analyse the data. HIV prevalence and TB incidence served as proxies for communicable diseases, and GDP per capita served as a proxy for economic growth. The study took a comprehensive analytical approach by including variables such as current health expenditure and government expenditure on education as control variables. The findings reveal a significant positive correlation between HIV prevalence, TB incidence and economic growth of the SADC nations, and this is attributed to the fact that the economic growth of most SADC countries does not immediately translate to better healthcare access, as the poor often remain highly vulnerable. Current health expenditure and government expenditure on education negatively affected GDP per capita. This study therefore recommends harmonising the cross-border strategies, strengthening regional surveillance and integrating health into economic planning to ensure a productive workforce within the SADC region.

Abstract: Hilbert’s Sixth Problem challenges us to rigorously axiomatize physics, particularly the bridge between microscopic dynamics and macroscopic laws. Yet, a conceptual gap remains: probability is usually treated as a fundamental assumption rather than a derived consequence of physical evolution. To address this, we introduce a Viscous Time Theory (VTT) framework governing evolution through admissibility, coherence, and recoverability. Applying an informational action principle, probability naturally emerges as an induced statistical measure over bundles of admissible trajectories. We validate this approach by analyzing a viscous-time kinetic transport operator, mapping out its contraction semigroup structure, spectral gap, and hypocoercive convergence. We further extend the model to nonlinear interaction kernels and evaluate its hydrodynamic scaling limit. Our analysis proves this diffusion-driven operator achieves strict spectral stability, exponential entropy decay, and global nonlinear stability. Furthermore, the macroscopic scaling limit rigorously yields nonlinear diffusion dynamics for coherence density. Ultimately, this provides an analytically tractable layer connecting microscopic evolution to macroscopic behavior. It demonstrates that probability, irreversibility, and transport laws can cohesively emerge from informational geometry, advancing the structural program envisioned by Hilbert.

Abstract: Muscle stem cells (MuSC) are the cellular source for generation and regeneration of skeletal muscle. To ensure correct muscle growth, MuSC self-renewal and differentiation need to be tightly regulated. Several signaling systems have been implicated in the control of MuSCs, among them Bone Morphogenetic Proteins (BMPs) and Notch, both of which promote MuSC proliferation and suppress differentiation. To better understand the mechanisms of function and the target genes regulated by BMP signaling in myogenesis, we investigated the transcriptional responses of adult mouse MuSCs to BMP6/4 using RNA-sequencing. BMP6/4-stimulation of freshly isolated MuSCs for one hour rapidly increased the expression of classical BMP target genes like Id1 and strongly induced expression of genes of the Notch pathway (Hes1, Hey1, Lfng, Snai1). In parallel, using Cleavage Under Targets and Tagmentation (CUT&Tag), we generated whole-genome binding profiles for the BMP pathway effectors pSMAD1/5/9 and SMAD4 and detected binding in promoters and potential regulatory elements of BMP targets and Notch pathway genes (Hes1, Hey1, Lfng, Snai1) indicating that BMP signaling directly influences Notch and that crosstalk between the two pathways regulates myogenesis.