Abstract: Variant-based pathogenicity predictors such as REVEL evaluate missense variants in isolation, discarding the gene-length and allele-frequency context needed to compare collections of genes. In this paper, we introduce a composite gene-level metric integrating Hardy-Weinberg heterozygosity, coding-sequence length, and REVEL scores, evaluated on 55 high-confidence autism genes against the 1000 Genomes reference. It identifies elevated pathogenic burden in 48 of 55 genes, removes gene-length and variant-count confounds, and outperforms REVEL-only scoring by a large margin. Bootstrap resampling and a label-permutation control confirm the enrichment is stable and not an artefact of the scoring construction. We present it as a complementary gene-level layer for case-control and gene-set comparisons, with a nonlinear successor outlined as future work.

Abstract: Neck pain is a prevalent condition that affects a significant proportion of the global population, particularly among university employees. This study analyzes the relationship between low-density lipoprotein (LDL) levels and neck disability (ND) in university administrative and teaching staff. A cross-sectional study was conducted on 236 administrative and teaching participants from a private university in Quito, Ecuador. Most of the participants held administrative positions (64.83%). LDL levels were categorized based on the Adult Treatment Panel III guidelines, and ND was assessed using the Neck Disability Index (NDI). The results showed that 66.53% of participants had LDL levels classified as higher than desirable (near optimal or above), while most of the individuals (57.20%) exhibited mild disability. Ordinal logistic regression analysis revealed a weak inverse trend (β = −0.00444). This suggests a non-significant trend where higher LDL levels slightly correspond to a lower probability of being in a higher ND category. The p-value of 0.3095 indicates that the association was not statistically significant. Furthermore, a quadratic regression model showed that ND had negligible explanatory power over LDL variability (R² = 0.02599, p = 0.2039). In conclusion, this study found no statistically significant relationship between serum LDL levels and neck disability in university employees.

Abstract: Nowadays, the complexity of electronic health records (EHRs) requires tools capable of efficiently and accurately extracting and interpreting clinically relevant information to support clinicians. This study explores the use of the Cheshire Cat AI framework, configured with Ollama and using LLaMA3 as a language model, with the main purpose of performing automatic analysis of synthetic EHRs from Kaggle. Through specific structured queries, the model was able to successfully reconstruct patients’ clinical histories and extracted useful data such as diagnoses, treatments, visits, comorbidities and demographic data. A validation process through repeated queries was then performed, which confirmed a high level of accuracy. To preserve data privacy, only synthetic datasets were used in this work. Beyond the simple retrieval of information by means of queries, the study highlights the great potential of language models in clinical decision support. Their ability to interpret large and heterogeneous datasets certainly offers new opportunities to improve diagnostic accuracy, simplify workflows and personalise treatments. Specifically, natural language queries by tools such as Cheshire Cat AI can be used for intelligent support systems that can, for instance, integrate multimodal and real-time data to provide medical recommendations. These results represent a first step towards the exploitation of large language models not only for EHR analysis, but also to assist in clinical decision-making processes in different medical fields and, above all, for the study of specific complex diseases such as rare diseases.

Abstract: Flooding is the most recurrent and economically devastating natural hazard in Nigeria, yet no standard or consistent nationwide assessment method exists. Moreover, spatially explicit flood susceptibility information remains scarce—a critical gap for a country of over 220 million people with severely limited hydrometric monitoring infrastructure. This study presents one of the first nationwide, multi-model flood susceptibility mapping efforts for Nigeria, integrating four complementary approaches: Frequency Ratio (FR), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting (XGBoost). The framework incorporates a bivariate FR component and Height Above Nearest Drainage (HAND) as a conditioning factor. Six conditioning factors were initially evaluated—elevation, TWI, HAND, LULC, slope, and soil type—with elevation, TWI, HAND, and LULC retained for final model development. The flood inventory was derived from a HEC-RAS 100-year floodplain simulation driven by satellite-derived discharge records from the Dartmouth Flood Observatory (DFO), yielding 973,111 binary flood/non-flood observations used as training labels for the LR, RF, and XGBoost models and as a flood-pixel count reference for FR computation. The HEC-RAS floodplain was independently verified against documented DFO historical flood reports. A three-tier accuracy assessment was conducted. For statistical accuracy using a 20% test subset, XGBoost achieved the highest AUC (0.956) and Overall Accuracy (0.892). For spatial consistency against the HEC-RAS reference, LR, RF, and XGBoost achieved substantial agreement (Kappa = 0.662–0.700), while FR achieved moderate agreement (Kappa = 0.410). Validation against the 2022 Sentinel-1 SAR flood extent showed that all four models exceeded HEC-RAS flood detection accuracy. For operational flood risk management, XGBoost is recommended due to its strong predictive performance and ability to minimize missed flood-prone areas. The resulting maps provide actionable spatial intelligence for disaster risk management, land-use planning, and early warning systems across Nigeria and other data-sparse regions.

Abstract: Fundamental physics faces three profound difficulties: ontological incompatibility ($G,c,\hbar$ presuppose different ontological categories), the chasm between continuity and discreteness (classical continuum vs. quantum discreteness), and divergent mathematical languages (differential geometry, Hilbert spaces, renormalization groups). This paper proposes a first-principles axiomatic system for the Cosmic Continuum (15 axioms A0–A14). A0 asserts: All state changes in the universe arise from energy redistribution; there is no state change without energy change. This axiom replaces Newton's "inertia" as the most fundamental physical assumption—inertia is merely the special case $\Delta E = 0$. Built upon this first principle, the axiomatic system is structured through ontological axioms (A1–A3) as the cornerstone, structural axioms (A4–A7) as the skeleton (including the connection axiom A4b which provides a unified description of the four fundamental interactions), dynamical axioms (A8–A12) as the realization (including the field recursion axiom A9b which unifies multiple fields as different levels of a recursive structure), and meta-axioms (A13–A14) as verification. The framework clearly distinguishes mass, energy, and dark mass beings and their corresponding space, time, and dark space dimensions, unified via the New Equivalence Principle (A3). The component (A4) serves as the skeleton, unifying particles and gauge fields into an inseparable tensor product. The scale topos and the $U_{q}(\mathfrak{e}_{8})$ modular tensor category provide the mathematical realization, rigorously capturing the relative continuum (A6) and embedding the Standard Model gauge group. The core breakthrough of this paper is to elevate ``interaction'' from an externally added Yang-Mills term to an intrinsic logic of component fusion. Gauge bosons emerge from the fusion decomposition $P \otimes P^*$ of the Planck particle $P$ ($\dim_q(P)=2$), with coupling strengths uniquely determined by fusion rules and braiding, not free parameters. Simultaneously, from A9 (fibred category) and A10 (2-category lifting), the quantum Boltzmann equation is rigorously derived, whose collision kernel is uniquely determined by the braiding $R$-matrix moduli and fusion coefficients, with no free parameters, achieving a complete axiomatic derivation from microscopic fusion rules to macroscopic transport phenomena. From these axioms we derive: the mirror 2-morphism $M$ is equivalent to CPT; the singularity is a phase boundary from ordinary spacetime to dark space; the singularity flux is quantized as $dN/dt = \mathrm{sgn}(-t)/t_{P}$; dark space entropy $S_{\mathrm{dark}} = k_{B}\ln 2\cdot N$ resolves the black hole information paradox; the mirror cyclic universe predicts $w_{a} > 0$ (dark energy weakens over time), consistent with current DESI/Planck data at $1.3\sigma$; wavefunction collapse is interpreted as a natural projection in the fibred category, with coherent information entering dark space. The framework is fundamentally deterministic (causal): the Born rule probabilities arise from limited access to information stored in dark space, not from intrinsic randomness. The framework proposes five testable predictions: (1) dark energy evolution direction $w_a>0$ ($w_a=0.12\pm0.09$); (2) CMB Planck-scale oscillations $\alpha\approx0.032$; (3) shear viscosity-to-entropy density ratio $\eta/s \gtrsim \hbar/(4\pi k_B)$; (4) LISA-band gravitational wave background peak $f_{\text{peak}}\approx0.2\,\text{Hz}$; and (5) black hole shadow quantum correction $\gamma\approx0.3$. This framework is the first to rigorously derive all four laws of thermodynamics within a single axiomatic system; the second law of thermodynamics is reframed as an apparent emergent phenomenon — the underlying dynamics are time-reversible and deterministic, while the apparent irreversibility arises from the observer's limited access to information in dark space. This paper presents a self-consistent, testable conceptual framework of axioms for fundamental physics, achieving a unification of classical physics, general relativity, quantum mechanics, thermodynamics, non-equilibrium statistical physics, and cosmology, thereby offering a fundamental response to the call for the axiomatization of physics raised by Hilbert's Sixth Problem.

Abstract: Large language models (LLMs) are being adopted quickly across biomedicine. Their value in clinical practice depends on more than accuracy: it also depends on whether they can run within the latency, hardware, privacy, cost, and staffing limits of real settings. In this scoping review of efficiency-oriented biomedical LLM research, we organize the literature along two axes: a taxonomy of efficiency techniques (prompting and retrieval, parameter-efficient and data-efficient adaptation, model compression, efficient architectures and inference, and agentic workflows) and a map of biomedical application domains. Across the corpus, prompting and parameter-efficient fine-tuning delivered efficiency most often, and studies reported it as savings in memory, trainable parameters, compute time, and human-workflow time, while energy and carbon were almost never measured. The reported gains are large and concrete: low-rank adaptation combined with low-precision quantization often shrinks the memory needed for adaptation enough to train and deploy a model on a single consumer or edge device, usually at a small and measured cost in task quality. Yet the evidence still leans toward retrospective benchmarking, with external validation, prospective evaluation, and clinical deployment all rare. We map which techniques serve which clinical domains, show how to read an efficiency claim against its comparator and clinical context, and identify what the field still needs to measure to turn demonstrated resource savings into validated clinical value.

Abstract: The identification of natural yellow dyes in ancient textiles is complicated, regardless of whether destructive or non-destructive techniques are used. The main limitation is the need for sampling, followed by deterioration and interference by the materials used in consolidation and restoration processes. In addition, different yellow dye sources share the same main fluorophores or components such as luteolin, kaempferol, and quercetin-based chromophores. We propose a minimally invasive methodology to identify sweet-scented marigold (Tagetes lucida), zacatlaxcalli (Cuscuta tinctoria), and weld (Reseda luteola). This methodology was tested on yellow wool samples that were dyed in an artisan workshop in the last 3 to 10 years. Another two samples of yellow wool fibres were obtained from the textile collection of the Franz Mayer Museum in Mexico City. Confocal scanning laser microscopy (CSLM), micro-Raman spectroscopy, attenuated total reflectance Fourier transformed infrared spectroscopy (ATR-FTIR), and variable pressure environmental scanning electron microscopy (VP-ESEM) were used to analyse the samples. The CLSM results showed that dyes are absorbed into the matrix of the fibres. The wool and dyes presented different emission spectra, which can be associated with the main groups of autofluorescent compounds in plants. The FTIR-ATR results supported the proteinaceous origin of the fibres, and the chemical composition and molecular structure of the autofluorescence phytocompounds were identified by micro-Raman spectroscopy. The findings indicate that the proposed methodology is adequate for identifying natural yellow dyes in wool fibres and can be applied to cultural heritage textiles.

Abstract: Background/Objectives: Nanvuranlat, a selective inhibitor of LAT1, has demonstrated clinical activity in advanced biliary tract cancer (BTC). We performed post hoc analyses to identify clinical and biomarker-defined populations that may derive greater benefit from LAT1 inhibition. Methods: Data from Phase 1 and Phase 2 studies were analyzed. Clinical outcomes were evaluated according to BTC subtype, prior primary tumor resection status, LAT1 expression, and accumulated drug exposure. Overall survival (OS), progression-free survival (PFS), and tumor size changes were assessed using Kaplan–Meier and Cox proportional hazards analyses. Results: In the Phase 2 study, nanvuranlat improved PFS versus placebo in the overall population (HR 0.56, 95% CI 0.34–0.90). Among the exploratory subgroups, lower hazard ratios were observed in patients without prior primary tumor resection (PFS HR 0.43, 95% CI 0.22–0.85; OS HR 0.53, 95% CI 0.28–1.01). Patients with high LAT1 expression demonstrated lower hazard ratios for PFS and OS than the overall population. Among patients with IHC, EHC, or GBC and high LAT1 2622551206500expression, PFS and OS hazard ratios were 0.31 (95% CI 0.15–0.64) and 0.50 (95% CI 0.25–1.00), respectively. Clinical outcomes also differed according to accumulated treatment exposure. Conclusions: These exploratory analyses suggest that BTC subtype, prior primary tumor resection status, and LAT1 expression may identify candidate populations for prospective evaluation in future nanvuranlat studies. The findings support further evaluation of biomarker-informed patient selection strategies in advanced BTC.

Abstract: This study investigates the dynamic transmission of geopolitical risk across the Brent crude oil market, gold market, U.S. Dollar Index (DXY), and the Thai stock market using a Bayesian Time-Varying Coefficient Vector Autoregressive (Bayesian TVC-VAR) model. Monthly data covering the period from January 1990 to December 2025 are employed to capture the evolving effects of major geopolitical events, including the Gulf War, the Asian Financial Crisis, the September 11 terrorist attacks, the Global Financial Crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict. The analysis integrates Time-Varying Impulse Response Functions (TVIRFs), Generalized Forecast Error Variance Decomposition (GFEVD), the Total Connectedness Index (TCI), directional connectedness measures (TO, FROM, NET, and NPDC), and network analysis to examine both the magnitude and direction of shock transmission.The empirical findings indicate that geopolitical risk generates substantial time-varying spillover effects across commodity, foreign exchange, and equity markets. The intensity and direction of connectedness vary considerably across different geopolitical regimes, with oil and the U.S. dollar emerging as dominant transmitters of shocks during periods of heightened uncertainty, whereas gold primarily serves as a safe-haven asset that absorbs market disturbances. The Thai stock market exhibits greater vulnerability to external shocks during global crises, reflecting its high degree of integration with international financial markets. The network analysis further reveals that the topology of financial connectedness changes significantly during major geopolitical events, highlighting shifts in dominant transmission channels over time. This study contributes to the literature by providing a comprehensive Bayesian time-varying connectedness framework that simultaneously evaluates geopolitical risk, commodity markets, foreign exchange, and an emerging stock market. The findings offer valuable implications for investors, portfolio managers, central banks, and policymakers seeking to improve portfolio diversification, risk management, and financial stability under geopolitical uncertainty.

Abstract: Plant specialized metabolites sit at the boundary between plant genetics, environmental response, and useful natural products. Their accumulation is rarely constitutive; instead, it changes with tissue type, developmental stage, stress exposure, hormone signaling, and cellular storage capacity. In this review, we revisit basic helix-loop-helix (bHLH) transcription factors as regulatory switch points in plant specialized metabolism, with particular attention to the jasmonate-JAZ-MYC module. In resting tissues, JAZ repressors dampen MYC/bHLH activity. After wounding, herbivory, pathogen challenge, or elicitation, jasmonoyl-isoleucine promotes COI1-dependent JAZ turnover, freeing MYC factors to bind E-box/G-box motifs, recruit co-regulators such as MED25, and activate biosynthetic genes or downstream transcription-factor cascades. This logic has been repeatedly adapted in different plant lineages to regulate terpenoids, alkaloids, phenylpropanoids, flavonoids, glucosinolates, phytoalexins, and related metabolites. Examples discussed include Arabidopsis sesquiterpenes and glucosinolates, Taxus taxanes, Artemisia artemisinin, Catharanthus terpenoid indole alkaloids, Salvia phenolic acids and tanshinones, Ginkgo terpene trilactones, rice diterpenoid phytoalexins, and cotton gossypol. Rather than treating bHLHs as stand-alone master regulators, we frame them as context-dependent nodes whose outputs depend on dimer choice, promoter grammar, chromatin accessibility, hormone crosstalk, partner transcription factors, and cell-type competence. We also outline evidence standards and engineering principles for using bHLH switches in crop defense, food-quality improvement, medicinal-plant production, and synthetic biology.

Abstract: We prove that the total area of the power circles associated with an odd p-gon inscribed in and circumscribed about a pair of homothetic ellipses remains invariant throughout the Poncelet family. Our proof is based on a simple affine averaging principle, which also yields several related quadratic invariants, including explicit formulas for the sums of the squared distances from the center to the vertices, to the side midpoints, as well as for the sum of the squared side lengths. As an application, we show that a convex Poncelet pentagon and the corresponding star Poncelet pentagon, both circumscribed about the same inellipse, have equal total power-circle area. These results unify several metric invariants of odd Poncelet polygons within a common affine-geometric framework.

Abstract: Background: Monocyte Distribution Width (MDW) is a hematological parameter automatically generated during routine blood count analysis and currently represents the only blood count-derived biomarker for early sepsis risk assessment in adult emergency department (ED) patients. Evidence regarding its ability to identify blood culture (BC) positivity and bloodstream infection (BSI) remains limited. This cohort study aimed to evaluate its diagnostic accuracy. Methods: Adult patients presenting to the ED of a tertiary hospital with suspected infection and BC request were evaluated. Between March 2022 and December 2024, MDW, C-reactive protein (CRP), and BC results were retrospectively collected from the laboratory information system. Samples were categorized as BC-negative or BC-positive and further stratified into microbiological categories. Diagnostic performance was assessed using ROC curve analysis and decision curve analysis (DCA). Analyses were performed both including and excluding CoNS. Results: A total of 2,748 samples were available. MDW values were significantly higher in BC-positive than in BC-negative samples (25.8±6.7 vs. 23.4±4.7) and were highest in Gram-negative infections (27.7±7.5). When CoNS were excluded, MDW achieved an AUC of 0.65 at the optimal cut-off of 24.5, showing a sensitivity of 58% and specificity of 66.2%. Rule-out thresholds of 17.7 and 18.7 achieved sensitivities of 95% and 92.9%, respectively, whereas rule-in thresholds of 29.4 and 32 achieved specificities of 90.0% and 95.2%. MDW consistently outperformed CRP and demonstrated positive clinical net benefit in DCA. The addition of CRP did not provide improvement in diagnostic accuracy (AUC 0.65). Conclusions: MDW is associated with BC positivity and clinically significant BSI and outperforms CRP in identifying patients with BC-positive in the ED supporting its integration into diagnostic stewardship strategies and early risk stratification pathways.

Abstract: Gel-based novel drug delivery systems (NDDS) occupy a mechanistically distinct niche among controlled-release platforms because they decouple three design variables—network cross-link density, continuous-phase polarity, and stimulus sensitivity—that in particulate carriers (liposomes, polymeric nanoparticles) are often interdependent. This critical review synthesizes 49 primary and secondary sources published predominantly between 2010 and 2026 to interrogate, rather than merely catalogue, how hydrogels, organogels, aerogels, nanogels, in situ gelling systems, and hydrogel-forming microneedles have been engineered for site-specific pharmacotherapy. Beyond a taxonomic overview, the review quantitatively contrasts formulation parameters—sol–gel transition temperatures (typically 32–37 °C for poloxamer 407/188 systems), swelling ratios, mesh sizes, and reported drug-release half-lives—across oncology, chronic diabetic wound care, ophthalmic and nasal-to-brain delivery, musculoskeletal (intra-articular) therapy, subunit vaccine depots, periodontal pocket therapy, and glucose-responsive insulin delivery. Particular attention is paid to the mechanistic basis of burst release, the porosity–mechanical-integrity trade-off inherent to interconnected hydrogel networks, and the divergence between preclinical rodent efficacy and the comparatively sparse controlled human trial data available for most gel platforms. The review concludes that while stimuli-responsive and 3D/4D-printed gel architectures have matured substantially as engineering constructs, clinical translation remains bottlenecked less by materials science than by inconsistent characterization standards, unresolved terminal-sterilization compatibility, and a paucity of head-to-head comparative trials against existing standard-of-care formulations.

Abstract: Earthen levees are crucial flood defense systems but are susceptible to failure due to internal erosion and saturation-induced instability, potentially causing breaches and endangering lives, infrastructure, and ecosystems in protected areas. Understanding soil saturation dynamics is vital for improved monitoring and resilience. This study presents a novel method of levee health monitoring by deploying a wireless sensor network consisting of nine small sensing spike packages with long-term UAV-deployability potential. In-package pressure, temperature, humidity, and—most importantly—soil conductivity are all measured by the suite of environmental sensors integrated into each spike package. This integrated sensing capability gives spatiotemporal information about the embankment’s subsurface moisture conditions. A 2 m long, 1 m wide, and 0.45 m tall sand-filled embankment replica was built for a controlled flume experiment to verify the system, enabling close examination of moisture permeability and propagation. The deployment of cutting-edge analytical methods for data interpretation is one of this study’s main contributions. Discrete conductivity measurements were converted into continuous, two-dimensional maps of soil saturation using interpolation techniques, namely radial basis function (RBF). This makes it possible to see the changing moisture front inside the embankment structure in detail. Important information about critical moisture thresholds and early warning signs of possible instability (piping failures) is provided by this combined analytical framework. Using a network of nine wireless sensing spike packages, the proposed framework monitored moisture propagation within a 2 m long earthen embankment over a 105 min lab experiment and detected piping failures at approximately 19 min and 34 min, which exited soon after detection. Improving flood risk forecasting models, improving infrastructure health monitoring applications, and guiding the development of more resilient levee systems are all directly impacted by this. This study is a major advancement in autonomous levee monitoring and management given that it combines robust spatial analytics and an interpolation method with inexpensive, quickly deployable sensing technology.

Abstract: Diffuser-augmented wind turbines present a compelling solution for power extraction in low-wind-speed regions. However, the systematic optimization of plain flaps for compact diffusers has remained largely unexplored. This study conducts a comprehensive parametric CFD investigation of a plain flap integrated into an already optimized compact diffuser, utilizing a validated high-lift airfoil at a 5 m/s freestream velocity. Thirty design points were evaluated across five flap bend locations and six deflection angles using 2D axisymmetric steady RANS simulations with the γ-Re_θt transition turbulence model and an actuator disc rotor representation. Results identify a global optimum at xf/c = 0.90 and βf = 20°, delivering a velocity augmentation ratio of γ = 1.302, a 4.71% improvement over the baseline, and a corresponding 14.9% gain in power coefficient. A wide performance plateau (γ ≥ 1.30) exists for xf/c = 0.85–0.90 and βf = 5°–20°, demonstrating excellent robustness to geometric variations. Flow visualization and wall shear stress analysis reveal that optimal performance does not rely on flow reattachment; instead, a stabilized circulation zone functions as a virtual aerodynamic surface. These findings offer clear, practical design guidelines: integrating a plain flap with xf/c = 0.85–0.90 and βf = 5°–20° into compact diffusers achieves near-optimal performance while allowing generous manufacturing tolerances.

Abstract: Piezo1 are mechanogated Ca2⁺-permeable channels that are important participants of calcium signaling in cells. The changes of Piezo1 properties and regulation can lead to modulation of fundamental cellular processes and reactions. Current concepts suggest that Piezo1 activity is regulated via the combined action of plasma membrane lipids, components of extracellular matrix, and cytoskeleton. At the same time, the question about regulation of Piezo1 conductive properties by F-actin structure remains unexplored. Here, we selected human melanoma SK-MEL-2 cell line to register single Yoda1-induced Piezo1 currents before and after F-actin destruction by depolymerizing agent cytochalasin D (CytD). Single-channel analysis evidenced the decrease of Piezo1 amplitudes and conductance values after acute CytD treatment. F-actin dynamics visualization showed that time-dependent changes in actin organization coincide with the dynamics of decrease in single-channel Piezo1 currents. The results demonstrate a novel mechanism for regulating fundamental Piezo1 properties by the state of actin structures in living cells.

Abstract: We study the adjunction-defect calculus underlying MacPherson--Vilonen gluing. For an open--closed decomposition \(X=U\sqcup Z\), recollement gives adjoint triples \(j_!\dashv j^*\dashv j_*\) and \(i^*\dashv i_*\dashv i^!\). We package this data as a MacPherson--Vilonen adjunction package and compute the twist--cotwist defects of its four constituent adjunctions. The nontrivial open-adjunction defects are \(T_{D^b_c(X)}(j_!\dashv j^*)\simeq i_*i^*(-)[-1]\) and \(T_{D^b_c(X)}(j^*\dashv j_*)\simeq i_*i^!(-)[1]\). By contrast, full-faithfulness in genuine recollement forces several open and closed defects to vanish, so imposing sphericality on all four raw recollement adjunctions is degenerate. Thus compatible sphericalization is formulated only conditionally, requiring shared-object data and transportability of compatibility morphisms. The main unconditional result identifies the boundary residual \(\operatorname{BRes}_{Z}(M):=\operatorname{Cone}(j_!j^*M\to j_*j^*M)\) as an extension \(i_*i^*M\to \operatorname{BRes}_{Z}(M)\to i_*i^!M[1]\to\). Iterating over closed-stratum filtrations yields residual towers, with filtered--graded, duality, motivic, and schober-theoretic outlooks.

Abstract: This conceptual article examines how artificial intelligence can support sustainability assurance in the transition from voluntary ESG disclosure to regulated, assurance-oriented sustainability reporting. The study is situated in the context of the Corporate Sustainability Reporting Directive, the European Sustainability Reporting Standards, ISSA 5000 and the EU Artificial Intelligence Act. It argues that AI can strengthen ESG verification by supporting disclosure identification, ESRS mapping, anomaly detection, consistency checks, greenwashing risk screening, external data triangulation and working-paper documentation. At the same time, AI introduces specific assurance risks, including data quality risk, reliability and hallucination risk, explainability risk, bias risk, overreliance risk, documentation risk, confidentiality risk, boundary and materiality risk, and accountability risk. The article develops a Responsible AI-Assisted Sustainability Assurance Framework that integrates ESG data inputs, AI analytical procedures, assurance risk assessment, human professional judgement, validation controls and documented assurance outputs. The central conclusion is that AI should be used as an analytical support layer within a human-in-the-loop assurance process, not as an autonomous source of assurance conclusions.

Abstract: Photovoltaic (PV) tilt optimization is commonly guided by latitude-based rules, but these heuristics do not explicitly account for sub-daily irradiance variability, diffuse-fraction behavior, or local atmospheric attenuation. This study presents FT-PVOT, a geometry-resolved framework that integrates National Solar Radiation Database (NSRDB) irradiance data with solar-position and plane-of-array (POA) transposition equations to identify irradiance-maximizing fixed and seasonal PV tilt angles. Direct normal irradiance, diffuse horizontal irradiance, and global horizontal irradiance were evaluated using a brute-force tilt sweep from 0° to 90° at 1° increments. The method was tested for Gainesville, Florida (29.65° N), using 2018-2023 NSRDB data and benchmarked against PVWatts tilt trends. The annual fixed optimum remained highly stable across the six-year period, ranging from 28° to 29° with a mean of approximately 28.8° and a standard deviation of approximately 0.41°. PVWatts produced an annual optimum of 29°, yielding a mean difference of approximately 0.17°. Relative to flat mounting, latitude tilt increased annual POA irradiation by approximately 9.2%, annual optimization by 9.6%, biannual adjustment by 13.5%, and monthly adjustment by 15.1%. However, monthly adjustment added only 1.6 percentage points, or approximately 27 kWh/m²/year, beyond biannual adjustment. Cloudy-sky conditions reduced annual POA irradiation by approximately 35.5% relative to the clear-sky case, but the annual optimum fixed tilt remained approximately 29°. These results show that high-resolution irradiance integration can convert latitude-based tilt guidance into a quantified, reproducible, location-specific design recommendation while preserving a clear distinction between irradiance optimization and full PV electrical-output prediction.:

Abstract: Since the heavy clutter seriously restricts the ability of radar to detect target, it is significant to build the target detector under heavy clutter. For practical situations without the secondary data or prior knowledge of target and clutter, this paper proposes a polarization-space-time detector. First, a general radar model is constructed for multiple pulses, multiple arrays, and multiple polarizations. Based on the theory of ternary hypothesis, the secondary data free (SDF) GLRT detector is proposed, which can maintain the constant false alarm probability (CFAR) in inhomogeneous clutter. Then, this paper proposes a matrix transform operator and an adaptive detection method using sliding window respectively, which do not need to know the steering vector of radar and the noncentral parameter of clutter in advance, so that the SDF-GLRT detector can adapt to different application scenarios. In addition, this paper optimizes the polarization waveform of the radar system by constructing a projection matrix, which can give the closed-form solutions of the optimal polarization and worst polarization, instead of relying on numerical solution. Finally, the performances of SDF-GLRT detector and other three detectors are compared by simulated and real data, which verifies that SDF-GLRT detector in this paper can still maintain superior performance in various clutter environments without secondary data.