Abstract: This study addresses the challenges of semantic mixing, limited interpretability, and complex feature structures in fine-grained sentiment and opinion classification by proposing an interpretable feature disentanglement framework built on the latent space of large language models. The framework constructs multi-component latent representations that separate emotional polarity, opinion direction, target attributes, and pragmatic cues during encoding, thus overcoming the limitations of traditional methods that merge diverse semantic factors into a single representation. During representation learning, the model first uses a large model encoder to generate basic semantic features and then builds multiple independent subspaces through learnable projections. A covariance constraint is introduced to reduce coupling across semantic components and to create clear boundaries in the latent space. To preserve the essential information of the original text, a reconstruction consistency mechanism integrates features from all subspaces to rebuild the global representation and enhance semantic completeness. The framework also incorporates semantic anchors to align latent components with interpretable semantic dimensions, giving each subspace a clear emotional or opinion-related meaning and improving transparency at the mechanism level. Experimental results show that the framework outperforms existing methods across multiple metrics and handles complex syntax, implicit semantics, and coexisting emotions with greater stability. It achieves high accuracy and interpretability in fine-grained sentiment and opinion analysis. Overall, the proposed disentanglement framework provides an effective approach for building structured, multidimensional, and interpretable representations of textual emotions and opinions and holds significant value for complex semantic understanding tasks.

Abstract: Cervical cancer remains a prominent cause of cancer‑related mortality among women worldwide because of chronic infection with high‑risk human papillomavirus (HPV) and disparate access to prevention and treatment. The current research evaluates the anticancer activity of Gypenoside XVII, a bioactive saponin of Gynostemma pentaphyllum, in HeLa cells as a model of cervical cancer. MTT, Annexin V-PI, and Hoechst 33342 assays showed dose‑dependent growth inhibition with typical apoptotic morphology. Flow cytometry revealed G₀/G₁ cell‑cycle arrest, while pathway interrogation revealed participation of mitochondrial and death‑receptor cascades, in agreement with caspase‑9 and caspase‑8 activation, respectively. Collectively, these findings position Gypenoside XVII as a natural‑product bioactive with potential both as an anticancer lead and as a functional‑food ingredient, deserving of further preclinical development.

Abstract:

Protected areas (PA) constitute a fundamental strategy for mitigating biodiversity loss. Land-sparing approach has expanded in response to international agreements, but expansion of PA does not guarantee conservation objectives. The objective was to assess PA effectiveness in conserving Nothofagus antarctica forests in Santa Cruz (Argentina) evaluating human impacts (fire, animal use, harvesting). The research was conducted within pure native forests in Santa Cruz, Argentina. This province encompasses 52 protected areas, representing the highest concentration of conservation units within the forested landscapes of the country. At least eight of these areas include N. antarctica forests. Three land tenure categories were evaluated: protected areas (PA), buffer of 15-km from PA boundaries on private lands (BL), and private lands (PL). 103 sampling plots were established, where 38 variables were assessed (impacts, soil, forest structure, understory, animal use). Three indices were developed to analyze ecosystem integrity: forest structure (FI), soil (SI), and animal use (AI). PA presents highest FI (0.64 for PA, 0.44 for BL, 0.30 for PL) and AI (0.60 for PA, 0.55 for BL, 0.52 for PL), and together with buffer zones, the highest SI (0.43 for PA, 0.47 for BL, 0.32 for PL. PA showed superior integrity regarding compared to BL and PL, indicating effective preservation despite anthropogenic impacts.

Abstract: The chorioallantoic membrane (CAM) is a well-vascularised extra-embryonic mem-brane that supports avian embryonic development, and can be used as an implantation site for xenograft models of various cancers. CAM tumour research models are powerful and versatile, offering a rapid, cost-effective and ethical complement to mouse xenograft studies. Their capacity for real-time observation of tumour growth, angiogenesis and metastasis within an immunocompetent living organism are particularly compelling. While CAM models have been extensively utilised for investigating solid cancers such as breast, lung and pancreatic, their potential for haematological malignancy research remains comparatively underexplored. This review examines the relevance, advantages and translational potential of avian CAM models in studying blood cancers. Their ap-plications across three primary categories are discussed – leukaemias, lymphomas and myelomas – highlighting experimental approaches that replicate aspects of human disease progression and therapeutic responsiveness. Moreover, the review evaluates species-specific considerations relevant to model fidelity, including evolutionary dis-tance and functional parallels between avian and human haematopoiesis. These com-parisons underscore both the opportunities and limitations for utilising CAM models in haematologic malignancy research. For their potential to investigate mechanisms of cancer development and treatment in simple but immunocompetent in vivo settings, we propose that CAM tumour models offer high value as a bridge between in vitro and mammalian in vivo studies for haematology translational research.

Abstract: Background: Pilgrims with diabetes are at increased risk of acute metabolic complications such as diabetic ketoacidosis (DKA), hyperosmolar hyperglycemic state (HHS), and severe hypo- or hyperglycemia during Hajj, due to extreme heat, prolonged physical exertion, crowding, dehydration, and disruption of daily routines. Recent advances in continuous glucose monitoring (CGM) and artificial intelligence (AI) offer a unique opportunity to predict high-risk glycemic events before clinical deterioration.Objective: To develop and internally validate a machine learning-based predictive model using CGM, physiological, clinical, and environmental data to anticipate acute glycemic crises in pilgrims with diabetes during Hajj, and to evaluate the feasibility and preliminary effectiveness of an AI-assisted alert system in a pilot interventional study.Methods: This two-phase prospective study will be conducted among adult pilgrims with type 1 or type 2 diabetes. In Phase 1 (prospective cohort), approximately 800–1000 pilgrims will be equipped with CGM and wearable devices for continuous monitoring of glucose, heart rate, activity, and other vital signs. Environmental variables and contextual data will be collected. Supervised machine learning models will be trained to predict severe hypoglycemia, severe hyperglycemia, DKA, and HHS over short-term windows (30–60 minutes) and internally validated. In Phase 2 (pilot interventional study), approximately 300–400 pilgrims will be allocated to AI-assisted care with real-time alerts versus standard care. Incidence of acute glycemic events and healthcare utilization will be compared between groups.Expected Results: The AI model is hypothesized to achieve: (1) AUC-ROC ≥0.80 [95% CI] for discrimination, (2) sensitivity ≥80% at the 30–60 minute prediction horizon for severe hypo-/hyperglycemia, (3) calibration slope 0.9–1.1 with intercept near zero, and (4) AI-assisted care will reduce severe hypo-/hyperglycemia incidence by ≥30% compared with standard care.Conclusions: This study aims to provide a practical, scalable framework for AI-enabled risk prediction in high-risk pilgrims with diabetes during Hajj, with potential application to other mass gatherings and hot climates.

Abstract: This study presents a comprehensive data visualization–based evaluation of Singapore’s waste management performance, focusing on behavioural, industrial, and environmental dimensions. Using multi-source datasets from 2014 to 2023, the research examines key factors shaping the nation’s waste profile, including the growth of plastic waste, public participation in recycling, and the dominance of non-domestic waste sectors. Through interactive dashboards and comparative time-series analyses, the findings reveal persistent structural challenges despite strong policy initiatives and public awareness campaigns. The COVID-19 pandemic significantly influenced consumption habits, triggering a surge in single-use plastics due to food delivery dependence, while household recycling rates remained low. Industrial and imported waste volumes continued to rise, underscoring the need for upstream policy interventions. The study also quantifies energy and crude oil savings from recycling, highlighting non-ferrous metals and plastics as the most resource-efficient materials. Overall, the research underscores the importance of integrating behavioural incentives, industrial accountability, and policy innovation to achieve Singapore’s Zero Waste Masterplan and Sustainable Development Goal 12 targets.

Abstract:

Script identification is the first step in most multilingual text processing systems. To improve the time efficiency of language identification algorithms, it is first determined whether there is content written in a certain script in the text; if so, the content written in that script is then obtained. Then, it is determined whether the total length of the texts corresponding to the identified scripts is equal to the original text length; if so, the script identification process ends. Finally, considering the frequencies of various scripts on the Internet, those that appear more frequently are prioritized during script identification. Based on these three approaches, an improved script identification algorithm was designed. A comparison experiment was conducted using sentence-level text corpora in 261 languages written in 24 scripts. The training and testing times of the newly proposed method were reduced by 8.61- and 8.56-fold, respectively, while the F1 score for script identification was slightly higher than those reported in our earlier studies. The method proposed in this study effectively improves the time efficiency of script identification algorithms.

Abstract: Peters-Plus syndrome is a rare autosomal recessive disorder characterized by multisystem involvement, with a primary manifestation in the anterior segment of the eye. The hallmark feature, Peters anomaly, presents as central corneal opacity with iridocorneal adhesions. Clinically, patients often exhibit the classic triad of anterior chamber defects, short stature, and brachydactyly, accompanied by craniofacial dysmorphisms such as cleft lip and/or palate, hypertelorism, and short palpebral fissures, as well as variable intellectual disability. Skeletal abnormalities include rhizomelic limb shortening, clinodactyly, and restricted growth, frequently responsive to growth hormone therapy. Additional manifestations may involve congenital heart defects, genitourinary anomalies, and endocrine disturbances such as hypothyroidism. Prenatal growth restriction is common, and structural brain anomalies can occasionally be present, though they do not consistently correlate with neurodevelopmental outcomes. Diagnosis is suspected based on characteristic clinical findings and confirmed through the identification of biallelic pathogenic variants in the B3GLCT gene. Genetic counseling is essential due to the autosomal recessive inheritance pattern. Management is individualized, including early corneal transplantation between 3–6 months to prevent amblyopia, treatment of glaucoma or cataracts, and multidisciplinary follow-up addressing ophthalmologic, endocrine, neurologic and developmental needs. Prognosis varies widely depending on the severity of ocular and systemic involvement. This overview underscores the importance of early recognition, genetic confirmation, and comprehensive, patient-centered care in optimizing outcomes for individuals with Peters-Plus syndrome.

Abstract:

Pipelines play a critical role in industrial production and daily life as essential conduits for transportation. However, defects frequently arise because of environmental and manufacturing factors, posing potential safety hazards. To address the limitations of traditional object detection methods, such as inefficient feature extraction and loss of critical information, this paper proposes an improved algorithm named FALW-YOLOv8, based on YOLOv8. The FasterBlock is integrated into the C2f module to replace standard convolutional layers, thereby reducing redundant computations and significantly enhancing the efficiency of feature extraction. Additionally, the ADown module is employed to improve multi-scale feature retention, while the LSKA attention mechanism is incorporated to optimize detection accuracy, particularly for small defects. The Wise-IoU v2 loss function is adopted to refine bounding box precision for complex samples. Experimental results demonstrate that the proposed FALW-YOLOv8 achieves a 5.8% improvement in mAP50, alongside a 34.8% reduction in parameters and a 30.86% decrease in computational cost. This approach effectively balances accuracy and efficiency, making it suitable for real-time industrial inspection applications.

Abstract: This paper extends the substrate stress framework developed for Schwarzschild collapse to the Kerr geometry. The analysis begins with the exact Kerr Kretschmann scalar and expands all polynomial terms explicitly. A stress invariant is defined as σ(r, θ) = √|K(r, θ)|, and a critical value σc is interpreted as the threshold at which the continuum description fails; σc is treated as a phenomenological parameter that characterizes the substrate’s curvature tolerance. The condition σ(r, θ) = σc then determines the failure radius rc(θ), which depends on latitude due to the anisotropic curvature introduced by rotation. The resulting structure is oblate, with its largest radius near the equatorial plane and its smallest radius near the rotation axis. The Kerr ring singularity is never reached within this framework because the stress threshold is encountered at a finite radius. This produces a geometric picture of rotating collapse bounded by a stress-limited surface rather than a classical singularity, and the structure reduces to the Schwarzschild result when the spin parameter is set to zero. This construction yields a covariant framework for analyzing curvature-driven failure in rotating collapse and clarifies how spin modifies the internal geometric structure of black holes.

Abstract: We study minimum-time heliocentric transfers for a spacecraft propelled by an electric thruster that draws constant electrical power P while continuously varying its exhaust speed (variable Isp). The vehicle is assumed to depart from and arrive on circular heliocentric orbits (i.e., initial and final velocities match the local circular velocity at the respective radii). First, we derive an analytic solution of the one-dimensional, gravity-free brachistochrone and discuss how a finite exhaust-speed ceiling modifies the solution, producing a boost–coast–brake structure. Next, we formulate the full planar Sun-field optimal-control problem, derive two closed-form first integrals, and show that the indirect formulation reduces to a seven-dimensional boundary-value problem. Finally, we present a practical numerical continuation strategy that obtains a coarse feasible endpoint via global optimization and then refines it by homotopy and Powell’s local solver. Numerical examples for a 1GW engine with an initial/dry mass of 3000 t→1000 t demonstrate Earth–Jupiter-class transfers in roughly 200–220 days that commonly exploit a solar Oberth pass. Reproducible code and data are available at the project repository.

Abstract: This study proposes an intelligent audit risk assessment method that integrates causal structure modeling, causal identifiability reasoning, and interpretable representation learning to address the lack of transparency in risk identification, the presence of confounded variable relationships, and the limitations of correlation-based inference in complex audit scenarios. The method first constructs a structured causal graph of the audit workflow to formalize the triggering relationships and interaction paths among audit features, and then applies structural equations and identifiability analysis to reveal latent causal dependencies. Based on this foundation, the model generates interpretable feature embeddings through causally constrained representation learning, allowing inference results to map back to the business semantic space along causal paths and enabling visual analysis of risk formation. To validate the effectiveness of the approach, this study conducts comparison experiments, ablation experiments, and multidimensional sensitivity analyses on a public audit dataset, and evaluates the method across model accuracy, interpretability, noise robustness, distributional shifts, and hyperparameter variations. The experimental results show that the method achieves significant improvements over existing models in accuracy, precision, recall, and F1-score, while maintaining stable performance under noise interference, class imbalance, learning rate changes, and latent dimension adjustments. The model also produces clear causal chain explanations that help auditors understand risk sources, identify key process components, and trace potential triggering mechanisms through structured reasoning logic. Overall, this study achieves a deep integration of causal inference and intelligent auditing and provides a complete methodological framework and empirical evidence for building transparent, trustworthy, and highly interpretable audit risk assessment systems.

Abstract: Prosopis juliflora is a highly invasive tree species in semi-arid and arid regions in eastern Africa. Its ability to displace herbaceous and woody species has been attributed to allelopathic effects, but this has rarely been tested in competition experiments on natural soil and experimentally binding potentially allelopathic substances. We tested the effect of soil collected underneath and outside of P. juliflora canopies, or treated with P.julilfora leaf litter, on the survival, growth and competitive ability of three resident tree species in the presence and absence of activated carbon. Survival and growth of tree seedlings was reduced on, compared to seedlings growing on soil collected outside P. juliflora canopies. When activated carbon was added to the soils, seedling performance significantly increased and did not differ anymore from that on soil collected outside P. juliflora canopies. Competition significantly reduced seedling height irrespective of the type of competitor (P. juliflora or resident tree species). There was no significant interaction between soil type and competition, suggesting that the effect of competition was independent of soil type. The results suggest that P. juliflora releases allelochemicals into the soil which have allelopathic effects on resident tree species and that at least part of these allelochemicals originate from leaf material.

Abstract: Aspergillus fumigatus is the primary pathogen causing aspergillosis. Recent molecular population genetic studies have demonstrated that A. fumigatus exhibits high local genetic diversity and with evidence for limited differentiation among geographic populations. However, research on the impacts of geomorphological factors on shaping the population diversity patterns of this species remains scarce. In this study, large-scale sampling and in-depth population genetic analysis were performed on soil-derived A. fumigatus from Guizhou Province, a representative karst landscape in southern China. This area is dominated by plateaus and mountains (accounting for 92.5% of the total area) and rep-resents a classic example of conical karst landscapes. A total of 206 A. fumigatus strains were isolated from 9 sampling sites across Guizhou. Genetic diversity, genetic differen-tiation, and population structure of these strains were analyzed based on 9 loci short tandem repeats (STRs). The results revealed that A. fumigatus in the karst region of Guizhou harbors abundant novel alleles and genotypes, with high genetic diversity. Gene flow among geographical populations was infrequent, and significant genetic differentiation was detected between multiple pairs of geographical populations, with the overall regional genetic differentiation reaching PhiPT = 0.061. Furthermore, the Guizhou populations showed significant differences from those reported in other regions world-wide. Surprisingly, only one of the 206 (0.49%) A. fumigatus isolates from this region exhibited resistance to the two medical triazoles commonly used for treating aspergillosis, and this resistance frequency was far lower than those reported in previous studies from other regions

Abstract: Graphene (GRA) and graphene oxide (GO) have drawn significant attention in materials science, chemistry, and nanotechnology because of their tunable physicochemical properties and wide range of potential uses in biomedical and environmental applications. Building reliable, large-scale molecular models of GRA and GO is essential for molecular simulations of wetting, adsorption, and catalytic behavior. However, current methods often struggle to generate large, chemically consistent sheets at high oxidation levels. In addition, the resulting structures are frequently incompatible across different simulation packages. This work introduces a step-by-step protocol with custom Tool Command Language (Tcl) and modified Python scripts for building large-scale, AMBER-compatible GO structures with oxidation levels from 0% to 68%. The workflow applies a systematic surface modification strategy combined with post-processing and atom-type assignment routines to ensure chemical accuracy and force field consistency. The dataset includes fifteen MOL2 format files of 20 × 20 nm² GO sheets, ranging from pristine to highly oxidized surfaces, each validated through oxidation-ratio analysis and structural integrity checks. Together, the dataset and protocol provide a design of scalable and chemically reliable GO molecular models for molecular dynamics simulations.

Abstract: The position paper serves scientists from all scientific disciplines to get a quick, concise, and easy-to-understand primer that describes the basic principles of design and applications of massively-parallel computations and models. The thesis of the position paper is, “What is the estimated direction of development of massively-parallel computing techniques utilizing emergents as observed in all scientific disciplines?” The birth of massively-parallel computations (MPCs) in the 1940s is closely related to the development of both early computers and simulations of nuclear processes that are operating within matter. It would be demonstrated that MPCs are becoming front and center in many research areas after a long delay that was forced by the previous inaccessibility of MPC computers. Another impetus to the development of MPCs came in the form of generalization of mathematical descriptions of observed natural phenomena using differential equations. More specifically, discretization schemes of differential equations got implemented in MPC simulations. Those achievements opened doors to the development of advanced descriptions of natural phenomena. Currently, there exist a number of MPC techniques that are gaining an increasing influence on the description of self-organization, emergence, replication, self-replication, and error-resilience within living and nonliving systems. A promising class of cellular automata, which is capable of describing a wide range of processes observed within natural phenomena, is called emergent information processing (EIP). The EIP approach is opening doorstowards future descriptions of many observed biological phenomena that are notoriously resisting mathematical and computational descriptions. Fixed and ad hoc networks, which are observed in living and non-living systems, could be interpreted using EIP methodology; this offers an opportunity for computational studies of emergent processes operating within them. Demonstrated approaches represent a toolkit that could be applied in all areas of research that are dealing with living systems. The usefulness of the EIP approach is demonstrated on a special case of emergent synchronization simulation, which could be applied in the design of artificial, biocompatible pacemaker implants and computers utilizing emergent computations.

Abstract: As Large Language Models (LLMs) expand into sensitive applications, concerns about fairness and bias have grown significantly. Traditional evaluation benchmarks capture static performance on curated datasets, but they often fail to measure the nuanced ways bias emerges across different contexts. This paper introduces the concept of multi-agent evaluators—independent LLMs configured to assess each other’s outputs—as a scalable methodology for fairness benchmarking. The framework enables adaptive, context-aware assessments where evaluators detect subtle disparities across demographic groups, task formulations, and linguistic variations. By combining redundancy, diversity, and adversarial prompting, multiagent evaluation offers a promising path toward more reliable fairness auditing. The study also explores how such approaches integrate with governance frameworks, illustrating their potential in domains such as recruitment, healthcare communication, and automated decision support. Ultimately, the findings argue for fairness benchmarking as a continuous process powered by collaborative LLM evaluators, rather than one-time testing on static datasets.

Abstract:

Polyurethane (PU) is widely recognized for its efficient oil sorption properties. However, this capacity is highly dependent on its intrinsic chemical composition and morphological structure which can be altered by mechanical or chemical treatments commonly applied before using as a sorbent. In this study, we present a comprehensive investigation of the oil sorption behavior of both soft and rigid PU foams, and their blade-milled ground (BMG) counterparts obtained by mechanical treatment of several recycled PU-based products, including seats, mattresses, side panel of cars, packaging components, insulating panels of refrigerators and freezers. We found that blade-milling of the soft PU foams leads to a significant reduction in oil sorption capacity, proportional to the extent of grinding. Pristine soft PU foams and the BMG-PUs with intermediate particle size (1 mm –250 μm) exhibited the highest oil uptake (30 -20 g/g), whereas the finest fraction (250 μm – 5 μm) showed lower capacity (3-7 g/g). In contrast, rigid PU foams showed consistently low oil sorption (~5 g/g), with negligible differences between the original and ground materials. At the macroscopic level, optical and morphological analyses revealed the collapse of the 3D porous network and a reduction in surface area. On the microscopic scale, spectroscopic, structural, and thermal analyses confirmed phase separation and rearrangement of hard and soft segmented domains within the polymer matrix, suggesting a different mechanism for oil sorption of BMG-PU. Despite reduced performance compared to pristine foams, BMG-PU powders, especially those with intermediate dimensions and originating from soft PU foams, present a viable, low-cost, and sustainable alternative for oil sorption applications, including oil spill remediation, while offering an effective strategy for effective recycling of PU foam wastes.

Abstract:

Approximately a third (1.3 billion tons) of the food that is generated globally is lost each year, and it accounts for over 20% of the global greenhouse gas emissions. Most of this loss is by-products generated during post-harvest and food processing, which account for 30–50% of raw materials, including shells, skins, pulp, stems, and seeds. While generally wasted, such by-products contain precious bioactive molecules such as phenolic acids, bioactive peptides, carotenoids, fibers, and secondary metabolites (e.g., terpenes, polyphenols, alkaloids) and minerals, amino acids, and vitamins. This review outlines how these high value agrifood by-products can be utilized towards achieving sustainable development goals (SDGs). It encompasses extraction methods, characterization, and potential uses of such active compounds in the food, pharmaceutical, packaging, and cosmetic sectors. Moreover, it examines the interaction between valuing agrifood by-products and key SDGs like eliminating hunger (SDG 2), ensuring good health and well-being (SDG 3), promoting affordable and clean energy (SDG 7), promoting economic growth and decent work (SDG 8), ensuring responsible consumption and production (SDG 12), and tackling climate action (SDG 13). These approaches have high potential to improve food security and economic sustainability of the world's food systems.

Abstract:

This article examines how large-scale fiscal–financial crime schemes in Brazil exploit legal and regulatory fragmentation, non-bank intermediation channels and institutional blind spots to generate systemic fiscal risk. Drawing on recent national operations in the fuel, logistics, beverage, retail and e-commerce sectors, it analyses how fintechs, payment institutions, investment funds, holding companies and shell entities have been used as parallel financial systems to sustain chronic tax evasion (devedores contumazes), money laundering and competitive distortions. Methodologically, the study adopts a qualitative, document-based approach, relying on official investigations, judicial records, government reports and regulatory documents. It integrates insights from financial regulation, public financial management, macro-supervision and organized crime to construct an analytical framework for understanding how fiscal–financial crime operates within the legal architecture of emerging markets. The findings show that fragmented supervisory mandates, gaps in the regulatory perimeter and limited data-sharing across tax, financial and sectoral authorities enabled criminal groups to operate at scale for long periods. These structures weakened state capacity, eroded public revenue and embedded illicit flows in key markets, thereby amplifying systemic vulnerabilities. The article contributes to the legal and regulatory literature by consolidating lessons from Brazil’s recent large-scale operations—such as Carbono Oculto, Poço de Lobato and Tank—into an integrated model of chronic tax evasion as a source of systemic fiscal risk. It concludes with a set of regulatory and public financial management recommendations that are relevant for both emerging markets and advanced jurisdictions facing similar legal and supervisory challenges.