Abstract: Identifying fast radio burst (FRB) sources likely to repeat can guide targeted observations and advance our understanding of their populations. We investigate repeater-candidate selection using 18 observational parameters from a Blinkverse-based sample of 3990 sources, including 104 confirmed repeaters. Eight model configurations are compared across four positive-training sizes and 30 random seeds. Positive–unlabelled (PU) Bagging-RF achieves competitive performance, recovering an average of 92.27% of 44 held-out repeaters within the highest-ranked 30% of test sources when trained on 60 confirmed repeaters. We further combine this model with cross-fitted calibration to estimate conditional repeater probabilities for 3886 unlabelled sources. Analysis across assumed repeater fractions quantifies prior sensitivity, while repeated fits characterize ranking stability. The resulting catalogue integrates these estimates with source-specific observational information to support follow-up target selection.

Abstract: For d ≥ 3 with −d ≡ 0,1 (mod 4), let h(−d) be the number of classes of primitive positive definite binary quadratic forms of discriminant −d. We prove that d→ h(−d) is computable in deterministic polynomial time with an oracle for a language in the second level \( \Sigma_2^{\text{P}} \) of the polynomial hierarchy. The proof uses only reduction theory of binary quadratic forms, Lagrange’s theorem and Hermite normal forms; it does not assume the generalized Riemann hypothesis, does not take a generating set of the class group as input, and uses no randomness. A \( \Sigma_2^{\text{P}} \) oracle finds a generating set of O(log d) forms, and an NP oracle then determines the lattice of relations, whose index is h(−d). Consequently, if P = NP, class numbers of imaginary quadratic fields are computable in polynomial time. As an application, the number D_n of representations of n by the ternary form 8x² +2y² + 16z² in Tunnell’s congruent number criterion equals 2h(−4n) for square-free n ≡ 2 (mod 8) and vanishes for square-free n ≡ 6 (mod 8); hence D_n lies in \( \text{FP}^{\Sigma_2^{\text{P}}} \) on even square-free n, and an exact counting reduction from #SAT to D_n would collapse the polynomial hierarchy to \( \Delta_3^{\text{P}} \).

Abstract: Inoculating legume plants with indigenous rhizobial strains provides a foundation for evaluating their host compatibility and ability to adapt to different environmental conditions. Evaluating the ability of rhizobial strains to nodulate different legume species helps determine their suitability for application as biological inoculants. Cross-infectious, host range, and nitrogen fixation are the main aspects of plant-microbe symbiosis in legume-rhizobia interactions. Cross-infectivity is described as the ability of a microbial strain that is associated with multiple host plants, while host range refers to the spectrum of compatible plant species. Effectiveness of this process depends on the compatibility between host and strain, effectiveness of the formation of nodule, and physical environmental conditions such as soil nutrient status and climate. Expanding host range through cross-infective strains has significant implications for sustainable agriculture, such as improving crop yields in nitrogen-limited soils and reducing fertilizers. Therefore, this review merges the recent knowledge on the fundamental cross-infectivity and host range, and their direct impact on nitrogen fixation efficiency. It further explores advances in molecular biology and genomics that aim to manipulate these interactions for enhanced agricultural productivity. Understanding how host compatibility influences the effectiveness of rhizobial symbiosis is important for understanding the interaction between host specificity and symbiotic performance which is essential for development of effective bio-inoculants and promoting ecological sustainability in the agricultural system.

Abstract: Silvery needle white tea (SNWT) is rich in catechins with potent antioxidant properties; however, its dose-dependent biological effects and safety profile remain unclear. This study evaluated the antioxidant activity and dose-dependent effects of SNWT using integrated in vitro and in vivo models. SNWT extraction was optimized using hot- and cold-water methods and characterized for total phenolic and catechin content. Antioxidant activity was assessed using DPPH assays. Cytotoxicity and cellular antioxidant activity were evaluated in CV-1 cells. In vivo effects were investigated in healthy and CCl₄-induced Wistar rats administered two dose levels, focusing on antioxidant enzyme activities, lipid profile, histopathology, and Nrf2 expression. Hot-water extraction (10 min) yielded higher catechin content and stronger antioxidant activity. In vitro, SNWT demonstrated protective effects against oxidative stress at lower concentrations, while higher concentrations induced cytotoxicity. In vivo, both doses increased catalase and glutathione peroxidase activities, whereas the higher dose was associated with elevated SGOT and greater histological alterations in liver and kidney tissues. These effects were accompanied by dose-dependent modulation of Nrf2 expression. SNWT exhibits dose-dependent antioxidant and pro-oxidant effects, suggesting a hormetic response. Lower doses confer protective effects; higher doses may induce toxicity, highlighting the importance of dose optimization for safe use.

Abstract: Tea residues can be converted into pellet fuel, but interactions between fuel properties and combustion conditions complicate emission prediction. This study evaluated 60 independent combustion tests, comprising two moisture classes (8–9% and 11–12%), three pellet diameters (6, 8, and 10 mm), and ten tests per combination. Ridge regression, radial-basis support vector regression (SVR), and random forest regression were compared with global-mean and treatment-mean benchmarks. Two predictor sets were assessed: moisture class and diameter alone, and an expanded set that also included O₂, CO₂, lambda, and combustion-air temperature. Hyperparameters were selected using nested cross-validation. Repeated treatment-stratified validation assessed new tests under represented conditions, whereas leave-one-combination-out validation examined transfer to unobserved treatment combinations. Mean reported CO concentrations ranged from 1476.9 to 8786.4 mg m⁻³, and NOx from 87.3 to 607.5 mg m⁻³. With fuel properties alone, SVR achieved mean cross-validated R² values of 0.869 for CO and 0.903 for NOx, with RMSE values of 946.1 and 51.1 mg m⁻³, respectively. However, RMSE improvements over the treatment-mean benchmark were only 3.2% and 6.8%. Adding gas-state variables did not improve the best internal prediction performance. For unobserved treatment combinations, fuel-only SVR yielded negative R² for both targets, demonstrating limited transferability. The results support restricted prediction within the tested design rather than general fuel optimization. Confirmation of the concentration-reporting basis and prospective validation across additional operating conditions are required before operational deployment.

Abstract: Hyperrealistic artificial intelligence (AI) generated faces are often misclassified as real, although spontaneous neural activity may still encode their artificial origin. We performed a secondary multivariate reanalysis of electroencephalography (EEG) data acquired from 26 participants viewing real and StyleGAN2-generated faces during an orthogonal target-detection task. Condition-averaged Event-Related Potentials (ERPs) from 14 selected channels and four regional Principal Component Analysis (PCA) features within 200–600 ms were analyzed using mass-univariate mapping, ridge encoding, group-aware logistic decoding, classical machine learning, a spatiotemporal Convolutional Neural Network (STCNN), and explainable-AI methods. Linear encoding was weak, whereas logistic decoding reached 0.623 ± 0.106 accuracy. In a 25-fold classical machine-learning benchmark, Linear Discriminant Analysis achieved 0.700 accuracy, increasing to 0.720 after optimization (Receiver Operating Characteristic Area Under the Curve (ROC-AUC) = 0.780), with permutation testing confirming above-chance classification (p = .001). The optimized STCNN achieved 64.0% ± 5.8% accuracy and AUC = 0.670, with significant held-out classification performance (p = .0028). Explainability analyses revealed a predominantly right-lateralized posterior occipito-temporal/temporo-parietal pattern with frontal involvement, with saliency peaks around 298, 355, and 500 ms. These findings provide preliminary evidence that late, distributed ERP activity contains decodable information distinguishing AI-generated from real faces.

Abstract: Medical image restoration (MedIR) involves heterogeneous modalities with distinct imaging characteristics, degradation processes, and restoration objectives. Learning a unified model is challenging because shared representations must accommodate modality-dependent structures and task-specific degradations. We propose DRSCP-Net, an all-in-one MedIR framework coupling a degradation-aware routing network with structure-aware cross-modal priors. The framework follows two training stages. First, Structure-Aware Vision–Language Adaptation (SAVLA) extends Degradation-Aware CLIP with edge-aware structural modeling and uses medical image–text contrastive learning to obtain structure-sensitive degradation representations. Second, a Degradation-Aware Routing Instruction Mechanism (DARIM) combines image features with a learnable instruction dictionary to generate expert-selection instructions, while deriving global and layer-specific prompts from the image features. Within Prompt-Guided Expert Selection Transformer (PGES-Former) blocks, routing instructions determine expert activation, and degradation representations from SAVLA are incorporated through cross-attention followed by hierarchical prompt-conditioned gating. At the bottleneck, Context-Gated Dual-Domain Modulation (CGDM) integrates spatial and frequency-domain features under global prompt guidance, whereas Adaptive Gated Fusion (AGF) controls feature transfer across encoder–decoder skip connections. No text input is required during restoration. Experiments on MRI super-resolution, CT denoising, and PET synthesis demonstrate competitive performance in both task-specific and all-in-one settings. In the all-in-one setting, DRSCP-Net achieves an average PSNR of 34.37 dB, tying for the highest among the compared methods, and obtains the best or tied-best results on three of the six task-wise metrics. Joint training yields positive transfer on all three tasks, and downstream segmentation exhibits improved average agreement with reference masks generated from high-quality images. Code will be made available at https://github.com/fanamber831/DRSCP-Net}{\textcolor{red}{https://github.com/fanamber831/DRSCP-Net.

Abstract: Vitamin B12 (cobalamin) deficiency is classically recognized by macrocytic anemia, but a minority of patients present with life-threatening or misleading hematological pictures. In a series of 201 consecutive patients, about 10% had severe manifestations, including symptomatic pancytopenia, pseudo-thrombotic microangiopathy (pseudo-TMA) and hemolytic anemia. Case reports describe further presentations that mimic thrombotic thrombocytopenic purpura (TTP), myelodysplastic syndrome (MDS), acute leukemia, Evans syndrome and immune thrombocytopenia, or that present with venous thrombosis, including cerebral venous sinus thrombosis. The resulting diagnostic errors expose patients to plasma exchange, corticosteroids, marrow-directed work-up and central venous access. Drawing on a targeted narrative review, we summarise reported phenotypes, identify features that may distinguish these presentations from their mimics, and propose a parallel diagnostic approach incorporating serum B12 and, when indicated, methylmalonic acid (MMA), total homocysteine and holotranscobalamin. Testing must not delay emergency treatment for suspected TTP. The evidence consists of isolated observations and one consecutive single-center series, and should be read as a prompt for vigilance rather than as a measure of frequency.

Abstract: Terpenolol is an engineered, high- shear dispersed aqueous formulation containing cannabidiol (CBD), medium- chain triglycerides (MCT), sunflower- seed lecithin, and purified water, produced via controlled high- energy, high- shear mechanical processing. The formulation was previously described as a nanomicellar, hydrophilic CBD -derived system (Scrimali, 2026c). The present article integrates published membrane- biophysics and ion- channel evidence with Terpenolol- specific human electrodermal observations to evaluate the hypothesis that biophysical membrane modulation represents a central mechanistic component of Terpenolol neuromodulation. The contemporary literature establishes that CBD partitions into lipid bilayers, modifies membrane physical properties, and modulates voltage- gated sodium channels (Nav) through mechanisms that encompass both direct protein interactions and a parallel, bilayer- mediated component. Separately, primary bilayer studies demonstrate that alterations in bilayer elasticity can regulate sodium- channel inactivation and use- dependent current behavior. Preliminary analysis of completed, Terpenolol- specific unpublished observations suggests a baseline- dependent electrodermal pattern, with a reduction when initial autonomic activation is elevated and substantially smaller change within physiological baseline conditions. These findings do not exclude CBD polypharmacology or direct channel binding. Rather, they support a convergent model in which membrane- mediated modulation constitutes one central component of the formulation’s neuromodulatory action. Two focused studies are proposed to consolidate the formulation-specific chain: external physicochemical characterization of Terpenolol and targeted in vitro biophysical testing using an appropriate heterologous cellular system, a defined synthetic lipid-bilayer model, or both, to distinguish formulation-specific membrane effects from direct CBD–protein interactions.

Abstract: Healthcare workers are exposed to workplace violence, yet the extent and nature of this exposure may differ considerably across professional groups. Psychosocial working conditions, including high work demands, emotional demands, working-time pressures and employment relations, may shape the conditions in which workplace violence occurs and is experienced. This study investigates psychosocial risks, workplace violence and symptoms of depression, anxiety and stress among nurses, physicians, healthcare assistants and administrative staff, and examines whether workplace violence is statistically associated with the relationship between psychosocial risks and these symptoms. A cross-sectional study was conducted with 289 Portuguese healthcare workers using three validated instruments: the Psychosocial Risk Factors Scale (INSAT_ERPS), the Aggression and Violence at Work Scale and the Depression Anxiety Stress Scales (DASS-21). Group differences were tested with Kruskal–Wallis tests, associations with Spearman correlations, and mediation and moderation analyses were performed using SPSS and the PROCESS macro. Results showed that workplace violence affected all professional groups but was more intense among nurses. Psychosocial risk was strongly associated with workplace violence, and this association did not differ across professional groups. However, neither psychosocial risks nor violence were associated with depression, anxiety or stress, and no statistically significant indirect association was observed. These findings highlight the importance of addressing workplace violence as an organizational and occupational health issue rather than a problem confined to specific professions.

Abstract: The rectilinear motion of a slender body in a fluid beneath an ice cover is considered in this study. The ice cover is modeled as a thin viscoelastic plate. The fluid is assumed to be ideal and incompressible, and its motion is irrotational. The problem is solved within the framework of potential flow theory. The flow around the body is modeled using source–sink theory, and the calculated wave resistance values are validated through comparison with known numerical solutions obtained for viscous fluid theory. The wave resistance of an underwater body moving at a distance of two diameters from the ice–water interface is analyzed. The maximum values of wave resistance and the corresponding critical speeds of the body are investigated as functions of the water depth, length of the body, and parameters of the ice cover. It is found that as the water depth decreases, the maximum values of wave resistance increase. For shallow water, the critical speeds of the underwater body are proportional to the square root of the water depth. As the water depth increases, the critical speeds approach a relationship—dependent on the body length and ice cover parameters—that corresponds to the motion of a body beneath an ice cover in an infinitely deep fluid.

Abstract: Centralized cloud platforms simplify application development and deployment, but they can also introduce vendor lock-in, recurring costs, privacy concerns, and dependence on third-party infrastructure. This paper presents dweb OS, a local-first peer-to-peer platform that transforms a personal machine into a self-hosted cloud node. Its three-layer architec- ture combines an Alpine Linux foundation; a HyperDHT and WebRTC networking layer for peer discovery, .dweb name resolution, direct connectivity, and relay fallback; and a multi- provider AI build engine supporting both local inference through Ollama and cloud-hosted models. A browser-based portal integrates service management, Git workflows, domain pub- lication, code generation, and deployment. In a single test environment, local cache resolution averaged 0.3 ms, while same-machine DHT, LAN WebRTC, and relay-assisted WAN resolution averaged 1.2 s, 2.8 s, and 4.5 s, respectively. The AI evaluation used five full-stack prompts executed five times per model and demonstrated that a locally hosted 7B coding model could generate runnable projects, with a trade-off in latency and success rate relative to the evaluated cloud models. Resource mea- surements showed a peak observed footprint of 280 MB of memory and 18% CPU utilization when all measured services were active. These results establish dweb OS as a functional pro- totype for sovereign development, private hosting, and decentralized application deployment, while identifying broader multi-network evaluation, stronger sandboxing, and persistent de- centralized availability as priorities for future work.

Abstract: Lung adenocarcinoma (LUAD) remains clinically heterogeneous, and competing endogenous RNA (ceRNA) derived prognostic biomarkers require further characterization. This study integrated TCGA-LUAD RNA-seq, miRNA-seq, survival, mutation, and methylation data to generate a ceRNA network and develop a survival-associated signature. Differential expression analysis identified 4,686 mRNAs, 2,220 lncRNAs, and 264 miRNAs dysregulated between tumor and normal samples. The final ceRNA network contained 9 lncRNAs, 9 miRNAs, 117 mRNAs. Cox and LASSO analyses identified a two-RNA prognostic signature composed of ITGB1-DT and IGF2BP1. High-score patients showed shorter overall survival and the risk score remained independently associated with survival after adjustment for age, gender, and stage. External validation in CPTAC-LUAD supported the model mainly as a continuous prognostic score. The high-risk group showed immunosuppressive and hypoxia-related features, increased tumor mutation burden, higher TP53 mutation frequency, and reduced IGF2BP1 regulatory-region methylation associated with increased IGF2BP1 expression. These findings suggest that the ITGB1-DT and IGF2BP1 signature may serve as a prognostic biomarker for LUAD and provide insight into ceRNA-associated tumor features. Further experimental validation required.

Abstract: In Four Null Spacetime Theory, spacetime is built from cells spanned by four lightlike vectors whose six pairwise Lorentzian inner products are equal, and curvature is concentrated at hinges in signed integer multiples of \(\pi/3\). At each past or future end, four legs, each with spin \(j = \frac{1}{2}\), form a singlet with two states of opposite cell chirality, defined here as the sign of the cell's oriented volume, not the handedness of a particle. A full cell carries four past and four future legs. We ask what the geometry of a shared face fixes when such cells are glued. Matching the pair products of the shared legs fixes the face exchange up to a spin frame and a phase, with no adjustable transfer strength. Around a hinge with deficit \(k_h\pi/3\), the composed face maps act on cell chirality as \(C \mapsto (-1)^{k_h}C\): odd curvature quanta reverse it, while even quanta preserve it. We derive this analytically and verify it on all 2,728 enumerated hinge configurations with proper transports and returning hinge labels. The curvature parity obeys a \(\mathbb{Z}_2\) Bianchi identity and is unchanged under Lorentz frame maps of the faces; a unitary Lorentz covariant quantum network remains open. With balanced past and future corners and full \(S_4\) relabeling symmetry, the eight leg cell map is unique and preserves chirality. A specified spin probe reads the oriented deficit modulo \(2\pi\). These results are kinematic. Face constraints do not fix the handoff of a spin reference inside a cell. Full transfer follows only if flat loop transparency is assumed, or, within the stated hinge class, if cell operations are assumed to be relabelings.

Abstract: Must EU ETS and CORSIA carbon costs be identical across airline business models? This paper finds a per-tonne uniformity to be legally well-grounded and no right to deductions for connections, shipped cargo, multi-class cabin configuration, special services, or tankering procedures, while the geography involved does already differentiate costs. It then asks whether narrow, service-based credits for carrying passengers with reduced mobility and other special services should exist.

Abstract: Accurate wear-time detection is essential for valid digital mobility assessment, yet existing lower-back device algorithms rely on heuristic thresholds validated only against stationary non-wear time, with no study benchmarking these methods on a common dataset. We benchmarked the full spectrum of wear-time detection approaches for lower-back devices, from signal processing to foundation models, using three complementary datasets: real-world recordings from 14 younger adults with labelled wear/non-wear periods, 12 recordings of non-wear with artificial device movement, and the Mobilise-D Technical Validation Study (n=101, target clinical population, 100% wear). Twenty-eight methods were evaluated using a common validation framework: 12 existing literature algorithms, a novel gyroscope-based signal processing method, and five machine learning, deep learning, and foundation model approaches, including two novel foundation models (SIGMA and ATLAS). Performance increased with model complexity: the best existing algorithm achieved a performance index of 0.866, the novel gyroscope-based signal processing method improved on this (0.889), and machine learning, deep learning, and foundation model approaches performed best (0.871-0.961), reducing daily wear-time duration error to as little as 4-5 minutes. Best-performing methods were implemented in the Mobilise-D pipeline (Mobgap), with models openly available for benchmarking and future development.

Abstract: To address the transmission capacity constraints and high reserve offer costs associated with cross-regional reserve sharing under high renewable energy penetration, this paper proposes a method for designing cross-regional virtual reserve services based on spatially transferable loads in data centers. First, a data center task migration model that considers task queues, transmission bandwidth, processing capacity, and deadline constraints is established to quantify the relationship among load reduction on the task-exporting side, additional electricity consumption on the task-importing side, and the reserve capacity of supporting generating units. Second, a novel Cross-Regional Virtual Reserve Consortium (CRVRC) business model is proposed to incentivize data centers and supporting generating units to jointly provide cross-regional reserve through joint bidding and revenue allocation. Then, a robust joint clearing model for energy and reserve that considers uncertainty in renewable energy output is established and solved using the Benders decomposition algorithm. Finally, case studies based on a modified IEEE 14-bus system show that, compared with the baseline, the proposed method reduces the joint clearing cost by 5.02% and the reserve offer cost by 3.60%, increases data center net profit by 89.73%, and maintains transmission margins on all cross-regional tie lines under wind power forecast deviations.

Abstract: Could the loss of identity of the universe’s fundamental constituents in a pregeometric regime reset cosmic entropy and permit a new cosmological beginning? We ask what any such reset would require. Three quantities often conflated are separated: the number of classes under an indiscernibility group, the Shannon entropy over those classes, and thermodynamic entropy. Relabelling cannot lower fine-grained entropy under reversible dynamics; a genuine reduction equals the information discarded by a many-to-one physical map, which a reservoir-coupled process must export at a Landauer cost; a non-unitary transition would need its own entropy law. Exact enumeration of all graphs on four to seven vertices and two stochastic models with exact orbit-level dynamics show that orbit entropy cannot certify a physical reset: erasing 15 physical edge bits lowers the entropy by 8.31 kB and requires at least 8.31 kBT of heat, while the orbit entropy falls by only 3.77 kB. Symmetrizing a state never lowers its entropy, and removing labels leaves relational distinctions intact. Under stated Hamiltonian and measure assumptions, labelled and quotient-state models predict different equilibrium populations (6:1 versus 1:1). Four requirements (individuation, dynamics, accounting, selection) are applied to proposals for a fresh cosmological start; none yet meets them all.

Abstract: Weight stigma exposure occurs across interpersonal relationships and media environments. However, Italian assessment tools rarely assess these sources of weight stigma comprehensively. The present study examined the psychometric properties of the Italian Weight Stigma Exposure Inventory (WeSEI) among adults. A total of 310 participants (M_age = 33.03 years, SD_age = 12.49) were recruited to complete an online survey. WeSEI’s 35 items were evaluated using confirmatory factor analysis. Reliability, convergent and discriminant validity, and measurement invariance across sex were also examined. The hypothesized seven correlated factors provided better fit than second-order, unidimensional, and bifactor alternatives. Subscale internal consistency was high. Average variance extracted exceeded .50 across dimensions, and all heterotrait-monotrait ratios were below .85. Changes in approximate fit indices were compatible with invariance through the strict level, although corrected difference tests did not support all equality constraints. WeSEI scores were positively associated with weight self-stigma, avoidance of physical activity and sport, and psychological distress, and negatively associated with body esteem. The findings provide preliminary support for multidimensional assessment of weight stigma exposure among Italian individuals.

Abstract: Antidepressant and mood-stabilizer pharmacology is still largely described in terms of occupancy at individual receptors, yet clinical response is heterogeneous across patients given identical compounds at identical doses. G protein-coupled receptor (GPCR) heteromerization offers one underused explanation. Within a heteromer, allosteric receptor–receptor interaction, cross-antagonism, redirected or biased signalling and altered trafficking make the functional consequence of occupying a receptor depend on which partner shares its membrane, so that occupancy underdetermines outcome. This review applies that principle to the serotonin–cannabinoid interface, which is of direct interest for mood disorders because both systems are targets of current and emerging treatments. The demonstrated complexes are cross-diagonal: CB1–5-HT2A, supported by bimolecular fluorescence complementation, bioluminescence resonance energy transfer (BRET), proximity ligation assay (PLA) and by disruption experiments in which abolishing the complex removes Δ9-tetrahydrocannabinol-induced cognitive impairment while sparing other drug effects; and CB2–5-HT1A, supported by BRET and PLA, modulated by cannabidiol, and the basis of a dual-agonist program for depression and anxiety. The widely assumed CB1–5-HT1A pairing has never been demonstrated as a physical complex. A parallel network of 5-HT1A heteroreceptor complexes in raphe–hippocampal circuits, and the 5-HT2A–mGluR2 complex that distinguishes hallucinogenic from antipsychotic signalling, together indicate that receptor context is a tractable variable for drug design, for patient stratification, and for the unresolved question of whether the subjective state produced by rapid-acting serotonergic agents is required for their antidepressant effect. Evidential strength is graded throughout, and a reproducibility ledger and research agenda are provided.