Abstract: Obesity represents a growing global health challenge, driving the need for safer and more effective therapeutic strategies. Natural products, particularly medicinal plants, have gained increasing attention as potential sources of anti-obesity agents due to their diverse bioactive compounds and multi-target mechanisms. The genus Scutellaria (Lamiaceae) is rich in phytochemicals, especially flavonoids such as baicalin, baicalein, and wogonin, which have been reported to modulate key metabolic pathways involved in obesity. This review aims to comprehensively summarize current evidence on selected Scutellaria species with potential anti-obesity activity, focusing on their phytochemical profiles and pharmacological mechanisms. A literature search was conducted using PubMed, Scopus, and Google Scholar databases, and relevant studies were selected based on predefined inclusion criteria. The findings indicate that Scutellaria-derived compounds may exert anti-obesity effects through multiple mechanisms, including inhibition of adipogenesis, regulation of lipid metabolism, improvement of energy homeostasis, and suppression of obesity-associated inflammation. Preclinical studies provide substantial evidence supporting these biological activities; however, clinical validation remains limited. In conclusion, Scutellaria species represent promising candidates for the development of novel anti-obesity therapies. Further studies, particularly well-designed clinical trials, are necessary to confirm their efficacy, safety, and therapeutic applicability in humans.

Abstract: Laser-ignited particle combustion is critical to energy, aerospace, and defense applications, yet understanding its physicochemical mechanisms is hindered by poor reproducibility in combustion data from randomly packed samples. While classical theory attributes data inconsistency to variations in packing density, we propose instead that consistency of the surface layer morphology—given the nanoscale laser penetration depth—is the dominant factor. A two-dimensional Discrete Element Model showed that increasing particle layers markedly reduces surface topography conformity, while gravitational settling maintains packing density near its theoretical maximum. An innovative constrained droplet method was developed for sample preparation, integrating multi-stage sieving, equal-circle packing in a circle theory, alongside droplet deposition to build multilayer samples mirroring computational models. In-situ laser ignition diagnostics revealed that key combustion metrics—spectral profiles, temporal evolution, ignition delay, and combustion duration—exhibit a rapid decline in consistency with increasing layers, closely matching the simulated decay in surface morphology conformity. Contrary to long-held assumptions, this work robustly shows that surface morphology governs laser-ignition experimental reproducibility. This paradigm-shifting finding redefines the controlling mechanism in laser-ignited combustion of random particle packings, thereby provides a method for refining sample preparation and enables the accurate determination of key parameters that remain elusive with conventional approaches.

Abstract: Physics-informed neural networks (PINNs) provide a data-efficient frameworkfor solving partial differential equations, but improving their accuracy often requires enlarging multilayer perceptron backbones, which increases parameter countand computational cost. This study investigates whether PINN performance canbe improved while keeping the underlying MLP lightweight. We introduce the Cannistraci-Muscoloni-Gu Generalized Logistic-Logit Function (CMG-GLLF) as a learnable activation function for compact PINNs. To make CMG practicalfor PINN training, we reformulate its implicit logit-phase approximation into anexplicit differentiable form using a one-step Newton approximation, reducing numerical instability and computational overhead. Empirical validation on Burgers’equation shows that the explicit CMG formulation substantially outperforms boththe implicit CMG implementation and fixed tanh activation. We further show that alayer-wise CMG design achieves a favorable accuracy-parameter trade-off, addingonly two trainable parameters per hidden layer while improving over vanilla MLPsin most settings. In addition, we evaluate transponder-based contextual modula-tion, which adaptively modulates hidden-layer representations according to thenetwork input. Across Burgers, Allen-Cahn, and diffusion-reaction benchmarks,Transponder-NS consistently improves over parameter-matched vanilla MLPs andachieves the best overall ranking, with approximately order-of-magnitude errorreductions on Burgers and Allen-Cahn. Combining CMG with transponder modu-lation further improves performance on Allen-Cahn and remains competitive acrosstasks. Finally, parameter-level analysis on Allen-Cahn shows that learned CMG parameters differ from the fixed Tahn and that transponder modulation varies acrossboth layers and nodes, providing explainability on why CMG and transpondercould outperform vanilla networks through depth-dependent modulation behavior.These results suggest that learnable activation functions and contextual modulationoffer a practical route toward lightweight, accurate, and explainable PINNs.

Abstract: Membrane Distillation (MD) is a heat-driven seawater desalination technology that uses a hydrophobic microporous membrane as its core component. Due to its low energy consumption, high separation efficiency, and ability to handle high-concentration saline wastewater, it has become an effective solution to the shortage of freshwater resources. Neverless, issues such as membrane wetting, membrane fouling, and low membrane flux severely limit its large-scale application. Composite membranes prepared using metal-organic framework (MOF) materials as fillers have become a research hotspot due to their advantages, such as permeable microporous channels, customizable pore structures, and modifiable active sites. These properties enable them to effectively reduce temperature polarization and concentration polarization phenomena. This article describes the characteristics of metal-organic framework materials and their current applications in the field of membrane distillation. Comparative analysis of the applicability of MOF polycrystalline membranes and MOF composite membranes in membrane distillation. Discussed the working principle of MOFs in enhancing the performance of membrane distillation. Finally, the problems and challenges associated with the use of MOFs in membrane distillation applications were analyzed. Aims to provide theoretical guidance for the application of metal-organic framework materials in the field of membrane distillation seawater desalination.

Abstract: The TM7x strain is a genetic variant of the bacterium Nanosynbacter lyticus, which belongs to the Saccharibacteria phylum within the Candidate Phyla Radiation (CPR) or Patescibacteria group. Its biology differs significantly from that of other bacterial phyla, and its ecological role in the oral cavity remains largely undefined. Through a organyzed and comprehensive literature review, we aim to define the role this bacterium plays within the oral ecosystem. We identified relevant studies from primary sources, included scientific articles from preclinical and clinical studies obtained from three digital databases. The bacterial strain TM7x is an obligate epibiont that exhibits autonomous energy me-tabolism and utilizes a type IV pili system to adhere to its direct host, Schaalia odontolytica. It interacts with its host in two stages: initially as an epipatobiont and subsequently as an episymbiont. TM7x plays a complex ecological role by modulating the host’s metabolism and structure toward a less virulent phenotype resistant to phage attack, while also in-fluencing the human host through immunomodulation and tissue protection. This organism has transitioned from being considered 'biological dark matter' to a key model for understanding coevolution within the human microbiome. Its ability to protect the host from phages, induce protective biofilms, and suppress destructive inflammatory responses positions it as a vital component of human oral microbiome homeostasis.

Abstract: Background: Modern warfare has introduced novel mechanisms of injury, particularly drone-induced blast trauma, resulting in complex craniomaxillofacial injuries. These injuries differ substantially from traditional ballistic trauma and require adapted surgical strategies. This study aimed to evaluate the clinical characteristics, management approaches, and long-term outcomes of midfacial blast injuries. Methods: A retrospective analytical study was conducted on 41 patients with drone-induced midfacial blast injuries treated at a tertiary referral center in Armenia following the 2020 Nagorno-Karabakh war. All patients underwent surgical management after initial stabilization and were followed for 5 years. Clinical outcomes, complications, and reconstructive needs were assessed. Results: All patients presented with comminuted midfacial fractures, frequently associated with polytrauma (87.8%). Burns were observed in 82.9% of cases. Surgical management included radical debridement and early definitive osteosynthesis using titanium fixation systems. No cases of postoperative osteomyelitis, bone sequestration, or implant failure were observed during the 5-year follow-up. Patients with extensive soft tissue defects, particularly nasal and lip amputations required multiple reconstructive procedures. Long-term follow-up revealed progressive soft tissue thinning over titanium meshes, especially in the zygomatico-orbital region, necessitating secondary interventions such as lipofilling. Conclusions: Drone-induced midfacial blast injuries represent a distinct and severe form of trauma. Early definitive reconstruction following adequate debridement was associated with favorable outcomes. However, soft tissue reconstruction remains challenging and often requires staged procedures. Long-term follow-up is essential to manage delayed complications and optimize aesthetic outcomes.

Abstract: The use of spot-on pet parasiticides has risen substantially. Imidacloprid, a commonly used active ingredient (AI), was removed from outdoor agricultural use in 2018 due to evidenced environmental risks. Imidacloprid is an AI in certain spot-on pet parasiticides and, with its metabolites, is now a domestic contaminant. We report two studies of dust and surface contamination in >50 homes in London, UK. In study 1, a time series pre-and-post spot-on application, imidacloprid rapidly contaminates the home at concentrations far exceeding the environmental quality standards that exist. Seven days post-application, imidacloprid concentration in domestic mop water exceeded the acute toxicity maximum acceptable concentration (MAC) by 600-fold and rinsate from fabric on which animals frequently sat was almost 5000-fold the MAC. In study 2, of dust in 50 homes, the 10 homes without resident pets had the lowest imidacloprid concentration. Homes using spot-ons had much higher concentrations (38+/-17 µg/g), comparable to days 5-7 in Study 1. The imidacloprid acceptable daily intake (ADI) for humans applies to the gastric route (residues in food). Multiple routes of human contamination exist; transdermal and inhalation have no standards. There is evidence that imidacloprid is associated with cardiac, neurological and endocrine disruption in mammals, including humans. A precautionary approach is advisable, with responsive use rather than prophylactic use which maintains high levels of domestic contamination.

Abstract: Silicon carbide (SiC) nanowires possess unique one-dimensional structural features, excellent mechanical strength, thermal stability and wide bandgap properties, showing great potential in high-temperature electronics, catalysis, sensing and composite reinforcement. Nevertheless, pristine SiC nanowires suffer from inert surface activity, weak interfacial compatibility and limited optoelectronic and catalytic performance. Surface coating and heterojunction engineering are effective strategies to address these deficiencies. This review systematically summarizes the synthesis routes of pristine SiC nanowires, including carbothermal reduction, chemical vapor deposition, template-assisted growth and molten salt synthesis, as well as their morphological regulation, physicochemical properties and inherent limitations. Meanwhile, typical coating methods such as wet chemical, hydrothermal, CVD and PIP are elaborated, and the influences of coating thickness, uniformity, adhesion and lattice/thermal compatibility on performance are summarized. The classification and interfacial charge mechanism of Type II, Z-scheme and Schottky heterojunctions are discussed, and the advances of coated SiC nanowires in photodetection, photocatalysis, gas sensing, electromagnetic shielding and energy storage are reviewed. Current challenges including coating stability, scalable preparation and integration bottlenecks are pointed out, and future research directions focusing on interface control, multifunctional integration and AI-assisted material design are prospected.

Abstract: This work provides an experimental comparison between classical PID, analytically compensated PID, and fuzzy control applied to the speed control of a rover actuator based on a permanent magnet DC motor. Unlike most studies, which focus on classical metrics such as transient response and steady-state error, this work incorporates kinematic indicators such as acceleration and jerk to characterize the dynamic effort applied to the actuator. The results indicate that the fuzzy controller achieves the fastest transient response and the best disturbance rejection, although at the cost of an IAJ 2.378 times higher than that of the classical PID and a peak jerk 79.36% higher under nominal conditions. The classical PID exhibits the smoothest kinematic profile under nominal operation, but under disturbances it generates jerk peaks 2.39 times higher than the fuzzy controller and an IAJ 1.67 times higher than the compensated PID, evidencing its inadequacy under variable loads. The compensated PID achieves the lowest cumulative IAJ under disturbance, outperforming the fuzzy controller by 6.7%, and provides the best overall balance between response speed, disturbance rejection, and cumulative mechanical wear.

Abstract: This manuscript develops a timelike-boundary reading of locality and reality within the established Lorentzian causal structure of special relativity and the standard record language of quantum measurement. The central object is a timelike boundary equipped with a boundary observer field and observer-adapted cuts. Such a cut is treated as the local comparison surface on which selected quantities are read relative to a coarse-grained reference structure. A local record appears when a boundary-relative deviation becomes resolvable on that cut. The framework separates two roles that are often compressed into one event statement. Lorentzian geometry supplies causal admissibility: it determines which prior data or contextual contributions may be relevant for a candidate event. The boundary comparison supplies record content: it identifies the deviation that becomes locally manifest. Thus the causal cone constrains the admissible domain, but it does not by itself provide a microscopic route or a measurement record. The proposed reading therefore assigns locality to cut-local record formation under Lorentzian causal admissibility. Reality is associated with stable, record-accessible deviations rather than with direct exposure of the underlying reference structure. The result is a compact assignment framework in which causal structure, reference structure, resolved deviation, and local record formation are organized on the same timelike boundary without replacing the established mathematical content of special relativity or quantum mechanics.

Abstract: The topological properties of planetary fluids are typically analyzed by mapping classical fluidequations onto complex quantum mechanical models. Here we present a purely real, six-dimensional Stueckelberg quantum mechanical formulation of the rotating shallow water equations to demonstrate that these topological features are intrinsic to the classical kinematics itself. Operating entirely within R^6, we decouple the complex quantum geometric tensor into an independent, real Fubini-Study metric and a real antisymmetric Berry curvature. Our real-variable approach explicitly derives a topological magnetic monopole of charge C=2 and captures the inherentscale invariance of the fluid's geometry without the need for complexification. We suggest that continuous variations in the Coriolis parameter may dynamically model the deep-time planetary evolution of the Archean Earth, and we propose a laboratory rotating-tank experiment to physically measure this topological phase transition. Finally, we show that our real 6D formulation naturally maps to unbroken supersymmetric quantum mechanics. By identifying a purely real supercharge and calculating a fluid Witten index of W = -2, we advance a mathematically supported viewpoint that steady-state geostrophic weather patterns represent the unbroken, zero-energy supersymmetric ground states of the rotating fluid system. Consequently, the topological isolation of this vacuum naturally restricts the spectral flow across the equator, providing a theoretical explanation for the unidirectional eastward motion of equatorial boundary waves.

Abstract: Credit risk assessment requires both accurate prediction and structured decomposition of how hetero-geneous evidence contributes to each decision. Monolithic Large Language Models can incorporate unstructured evidence and natural-language reasoning into such workflows, but in high-stakes un- derwriting they may be distracted by noisy inputs, miss rare but decisive risk cues, and offer limited control over policy-dependent decision thresholds. We present CREDITAGENT, a hierarchical credit review system with three stages: evidence filtering, specialist risk analysis by agents, and decision fusion. Our central contribution is holding the adapted backbone, specialist-agent outputs, hard-stop rules, and data split fixed, we vary only the final fusion strategy to isolate the effect of hierarchical fusion on underwriting quality. On a held-out set of 6,000 personal credit cases from Chinese financial institution, CREDITAGENT achieves 83.32% accuracy and a Business Efficiency Coefficient of 0.7647 outperform flagship model. We present these findings as an institution-specific case study while identifying which components (hierarchical fusion, GRPO training recipe) are mechanism-portable versus institution-specific (hard-stop rules, cost ratios). To ensure reproducibility, we make code and dataset publicly available at https://github.com/kouzhizhuo/Credit_Agents.

Abstract: The functioning of complex natural structures, such as living systems, still lacks a generally accepted theoretical basis with respective empirical experimental verification for decades. We propose a class of experiments to test whether such systems could be subject to an unknown ordering principle that cannot be captured by known physical laws. We hypothesise that the quantum mechanical uncertainty principle enables ordering phenomena in nearly chaotic systems in the sense of a strong emergence principle, which would not be expected when they are modelled conventionally, as several authors have already formulated in various forms. To account for the harsh conditions prevailing in living systems that may preclude fragile macroscopic quantum coherence, our hypothesis does not require such coherence at all, contrary to earlier related proposals. To test this hypothesis, two virtually identical and sufficiently complex experimental setups should be compared. One setup will operate with deterministic pseudo-random number generators at key sensitive points, while the other one will use quantum-based physical random-number generators, the two setups being otherwise identical. Existing artificial neural networks are proposed as possible test objects, and their performance under identical training conditions can be used as a quantitative benchmark. As this working hypothesis extends far beyond artificial networks, a successful outcome of such an experiment could have significant implications for many other branches of science.

Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by a decline in functional capacity and increasing limitations in daily activities. Clinical assessment tools provide valuable information on symptom severity but do not fully capture functional capacity. The aim of this study was to determine the usefulness of the Senior Fitness Test (SFT) for assessing functional capacity in patients with PD, depending on met (group A) or did not meet (group B) health-promoting physical activity (PA) recommendations. The study included 74 patients with idiopathic PD classified as Hoehn and Yahr stage II. PA was assessed using ActiGraph GT3X+ triaxial accelerometer and activity diaries, functional capacity using SFT, and symptom severity by MDS-UPDRS scale. Group A achieved significantly better results in all SFT components and had lower MDS-UPDRS scores than group B. The synthetic functional index was higher in the A group (1.28 ± 2.25 vs. -0.64 ± 2.28; p < 0.001), whereas the total MDS-UPDRS score was lower (31.09 ± 4.97 vs. 34.99 ± 5.28; p = 0.022). The results indicate that the SFT may be a useful and practical tool to complement the clinical assessment of patients with PD and may support more individualized rehabilitation planning and monitoring.

Abstract: Non-invasive methods for monitoring the condition of laboratory animals play a key role in ensuring animal welfare and improving the reliability of scientific data. This study evaluates the effectiveness of two non-invasive approaches - daily body weight measurement and urine analysis by qPCR - for monitoring the health of BALB/c mice infected with tick-borne encephalitis virus (TBEV). Calculation of the first derivative of body weight change allowed precise determination of disease onset, which correlated with clinical symptoms and detection of viral RNA in urine. Mathematical analysis of body weight change dynamics (first derivative with type 2 cubic spline smoothing, rh = 1) showed that a derivative threshold value of ≤ −0.6 reliably distinguishes infected BALB/c mice from healthy ones (AUC = 1 in ROC analysis). Urine analysis by qPCR allowed for the detection of viral RNA as early as the second day after infection, with a peak on the seventh day. The mathematical model was further tested on C57BL/6, CBA, and BALB/c mice of different ages and proved to be suitable. The threshold values of the derivative were found to be dependent on the mouse strain. The proposed non-invasive methods offer a humane and accurate alternative to invasive procedures, contributing to higher ethical standards and quality of research in virology.

Abstract: Water hyacinth (Eichhornia crassipes) is an invasive aquatic plant with high lignocellulosic content, offering potential as a natural fiber resource for craft-based industries. However, its extremely high initial moisture content (≈95%) presents a major challenge in fiber processing, particularly for small-scale industries that rely on traditional sun-drying methods. These methods are highly dependent on weather conditions, prone to contamination, and produce inconsistent fiber quality. This study adopts a research and development (R&D) approach to design and evaluate an innovative dryer machine specifically for water hyacinth fiber processing. The proposed system utilizes LPG-based heating and controlled airflow to achieve stable drying conditions. Experimental results show that the dryer machine can process 10 kg of wet water hyacinth within 280 minutes, significantly shorter than approximately four days required for manual drying. The system reduces the moisture content to below 10%, resulting in improved fiber cleanliness, uniformity, and usability. Although the dried mass produced by the machine is slightly lower compared to manual drying, this is attributed to more effective moisture removal, leading to lower residual water content in the final product. Productivity analysis indicates improved operational consistency and higher processing capacity over extended periods (30–180 days), particularly under varying weather conditions. These findings demonstrate that controlled drying technology provides a reliable and efficient solution for lignocellulosic fiber processing in small-scale industries, contributing to improved material utilization and sustainable biomass management.

Abstract: Soil health and irrigation water quality are fundamental to sustainable agricultural productivity, particularly in semi-arid environments. This study evaluated the influence of irrigation water quality on soil physical and chemical properties within the Kagitumba Irrigation Scheme in Eastern Rwanda. An observational analytical design integrated field sampling, laboratory analysis, and statistical evaluation. Soil samples (n = 20) were col-lected at depths of 0–30 cm and 30–60 cm, alongside irrigation water samples (n = 5) from intake and distribution points. Soil parameters analyzed included texture, bulk density, pH, electrical conductivity (EC), organic matter, and nutrient content, while water quality assessment focused on pH, EC, turbidity, dissolved oxygen (DO), and oxidation–reduction potential (ORP). Data were subjected to descriptive statistics, Pearson correlation, and ANOVA at a 95% confidence level. Findings revealed predominantly sandy loam soils with low bulk density, moderate water-holding capacity, and near-neutral pH. Soil salin-ity remained low, indicating limited risk of degradation. Irrigation water was generally suitable for agricultural use in terms of pH and salinity; however, elevated turbidity showed a strong negative correlation with infiltration rate (r = −0.73). Additionally, low soil nitrogen levels were significantly associated with water quality, suggesting nutrient leaching. These results underscore the critical role of irrigation water quality in shaping soil health and emphasize the need for improved water filtration and integrated nutrient management to enhance long-term sustainability.

Abstract: The complete blood count (CBC) is a diagnostic test to analyze abnormalities of blood cells. Currently, automated hematology analyzers and artificial intelligence technology are being used with automated blood analyzers to ensure accuracy and reliability. This study aimed to evaluate the performance of artificial intelligence (AI) based automated blood cell analyzer, Awalife AI-100Vet Multifunctional Morphological Analyzer, in dog and cat blood samples by comparison with the CBC manual method. In dogs, PCV, hemoglobin, RBC, MCH, WBC, % Neutrophil, %Lymphocyte, %Monocyte, %Eosinophil and %Reticulocyte were all significantly correlated. While in cats, PCV, Hemoglobin RBC, WBC, % Neutrophil, % Lymphocyte, and % Eosinophil were all also significantly correlated. AUC values obtained by the Awalife AI-100Vet analyzer for Hematology testing in dogs and cats were 0.72 and 0.92 respectively. These findings suggest that the Awalife AI-100Vet analyzer demonstrated good accuracy using dog blood for hematology testing as well as excellent accuracy when using cat blood. The AI-based automated blood analyzer has the potential to analyze hematological data and is close to the reference method. However, there are still differences in some parameters. Further optimization of the AI algorithm, which will involve increasing the accuracy of identifying unusual cell shapes, improving stability against various samples, such as stains, and achieving good results when working with unique pathologies, should be carried out.

Abstract: In vitro-transcribed modified mRNA is a promising platform for transient protein replacement in regenerative medicine, including cardiomyocyte regeneration, but repeated dosing is limited by variable innate immune activation and ISR-mediated translation shutdown across individuals. We propose a Human-specific PRR/ISR Immunogenicity Atlas: a focused, genotype-aware computational framework linking patient genetic variation in pattern recognition receptors and ISR components to predict immune and translation responses for IVT modRNA. The Atlas generates individualized “genetic passports” that stratify responder risk, estimate cytokine and PKR/eIF2α activation, and prioritize clinically feasible temporary knockdown strategies (LNP-siRNA/ASO or small molecules). We outline a six-stage roadmap covering data integration, feature engineering, multi-modal modeling, uncertainty quantification, a knockdown prioritization module, and open deployment. Ethical, privacy, ancestry-representation, and regulatory considerations are discussed, along with a staged validation strategy. This Atlas provides a conceptual and practical framework to support safer, more consistent protein replacement in regenerative medicine by moving from one-size-fits-all to genotype-guided approaches.

Abstract:

Dixit et al. proposed an asymptotic drag scaling for zero-pressure-gradient flat-plate turbulent boundary layers based on the approximation $M\sim U_{\tau}^2\delta$, where $M$ is the kinematic momentum rate through the boundary layer, $U_{\tau}$ is the friction velocity, and $\delta$ is the boundary-layer thickness. In the present paper, an explicit Reynolds-number-dependent correction to this approximation is derived from the logarithmic mean-velocity profile. Integration of the log law across the layer yields $M\sim U_{\tau}^2\delta\,f(Re_{\tau})$, where $Re_{\tau}=\delta U_{\tau}/\nu$ is the friction Reynolds number and $f(Re_{\tau})$ is given analytically. Application of the correction to the dataset compiled by Dixit et al. shows that the corrected scaling gives an exponent consistent with the asymptotic value $-1/2$ within bootstrap confidence intervals, whereas the uncorrected formulation does not. The correction should be viewed as a leading-order amendment, since the derivation uses the logarithmic law outside its strict range of validity.