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Growth media for human cell culture were developed in the twentieth century, when the first immortal human cell lines were established. The nutrient compositions of these media arose not from a desire to reproduce the microenvironment of the cells in vivo, but rather to encourage continuous replicative growth. Armed with comprehensive datasets detailing the metabolomes of the various fluid compartments within which cells reside, cell culturists are now exploring the effects of media designed to reproduce the in vivo environment on cell biology. The early results of this research indicate the media composition has profound impacts on cell form and function. In parallel, taking care to maintain oxygen at the relatively low levels found in vivo also affects many cellular activities. The lessons learned from ‘physiological cell culture’ should be applied to the culture of human embryos in the in vitro fertilization (IVF) clinic, where a critical stage of growth and development might be best supported by recreating, to the greatest extent possible, the environment of the oviduct and uterus. In this review, we translate recent advancements in physiological cell culture to emerging approaches in human embryo culture. Full article

Background and Objectives: Gastric cancer remains a leading cause of cancer-related mortality worldwide and develops through complex interactions between environmental factors, microbial dysbiosis, and host molecular pathways. Although Helicobacter pylori infection is a well-established risk factor, emerging evidence suggests that broader alterations in the gastric microbiome may also contribute to carcinogenesis. However, the associations between gastric cancer-associated microbial taxa and host gene expression profiles remain insufficiently characterized. This study aimed to identify host gene signatures associated with gastric cancer-related microbial taxa through a descriptive analysis integrating microbiome-derived taxa with transcriptome data. Materials and Methods: Microbial taxa associated with gastric cancer were systematically retrieved from the Disbiome database. Taxon set enrichment analysis (TSEA) was performed using the MicrobiomeAnalyst platform to identify host genes associated with gastric cancer-associated taxa. Importantly, TSEA relies on healthy reference data from the Human Microbiome Project and does not establish gastric cancer-specific interactions or causal relationships. Gene expression levels were subsequently evaluated using The Cancer Genome Atlas (TCGA) PanCancer stomach adenocarcinoma (STAD) dataset by comparing tumor and matched normal gastric tissues. Gene interaction network and transcription factor (TF) enrichment analyses were conducted to explore predicted regulatory relationships. Results: Among 64 microbial taxa associated with gastric cancer, 43 were reported as elevated. After removing overlapping taxa across studies, 37 elevated and 21 reduced taxa were retained for analysis. TSEA identified 11 host genes associated with gastric cancer-related microbial taxa. Transcriptomic analysis demonstrated significant downregulation of DPP6 and DLG2, while KDM4D, USP34, and VDR were significantly upregulated in gastric cancer tissues compared with normal controls. Network and TF enrichment analyses revealed predicted co-expression and co-localization patterns among these genes, suggesting their potential involvement in immune-related processes, epigenetic regulation, and cellular organization. Conclusions: This descriptive study identifies distinct host gene expression signatures associated with gastric cancer-associated microbial dysbiosis. This study is purely associative and hypothesis-generating; no causal or mechanistic inferences are made. TSEA used healthy reference data and therefore does not reflect gastric cancer-specific host–microbe interactions. The findings provide a basis for future hypothesis-driven research but require validation in independent cohorts. Full article

First-principles calculations based on density functional theory (DFT) are a powerful tool in data-oriented materials research. The choice of approximation for the exchange-correlation functional is crucial, as it strongly affects the accuracy of DFT calculations. This study compares the performance capabilities of three approximations on the energetics, mechanical and electronic properties, and crystal structure of NaAlP₂O₇, which is an insulator with a wide band gap that suppresses its electronic conductivity. Two of these approximations are based on Perdew–Burke–Ernzerhof (PBE) generalized gradient approximation (GGA) and the other on the strongly constrained and appropriately normed (SCAN) meta-GGA. We explore these materials as a contribution to the development of new solid electrolytes (SEs) for sodium-ion batteries (NIBs), which have the potential to mitigate challenges related to lifecycle, safety, and low ionic conductivity. The performance of these batteries largely emanates from the extraordinary demand for high-performing energy storage technologies. This study revealed that PBEsol accurately predicted lattice parameters that closely aligned with experimental values. However, r2SCAN provided the most reliable predictions of the structural and electronic properties of the NaAlP₂O₇ solid electrolyte compared to PBE and PBEsol. Findings demonstrated that the material is structurally, mechanically, electronically, and thermodynamically stable, but exhibits vibrational instability, which may scatter ions and reduce ionic conductivity due to the presence of imaginary frequencies. Our results highlight the importance of selecting appropriate functionals for solid electrolyte DFT computations. The r2SCAN functional appears to be a promising choice for calculating NaAlP₂O₇ properties. Full article

Understanding the unsteady aerodynamic behavior of quadrotor unmanned aerial vehicle (UAV) is critical for improving flight stability, control, and performance, particularly in complex operational environments. In closely spaced multirotor configurations, coherent tip vortices shed from each blade convect downstream and form helical vortex streets that interact with subsequent blades and neighboring rotors. These interactions induce rapid fluctuations in local inflow velocity and effective angle of attack, resulting in transient lift variations, increased vibratory loads, and elevated acoustic emissions. This study presents a comprehensive computational investigation of quadrotor rotor interactions and wake dynamics using a large-eddy simulation (LES). Detailed analyses reveal that the formation and evolution of tip vortices and blade–vortex interaction phenomena significantly influence lift fluctuations and aerodynamic loading. The simulations capture transient wake structures and their effects on neighboring rotors, highlighting unsteady aerodynamic mechanisms that are not adequately predicted by conventional RANS or URANS approaches. Parametric studies examining vortex-street offset distance demonstrate the sensitivity of wake-induced instabilities to design and operational parameters. The results provide new physical insights into multirotor wake dynamics and establish the LES as a predictive framework for quantifying unsteady aerodynamic loading in quadrotor drones. The findings provide insights into the complex flow physics of multirotor systems, offering guidance for more accurate modeling, rotorcraft design optimization, and the development of control strategies that mitigate adverse unsteady aerodynamic effects. This study provides new insights into rotor–vortex-street interactions, with applications to multirotor UAVs, by isolating multi-vortex coupling effects and quantifying the influence of horizontal vortex spacing on unsteady aerodynamic loading, complementing existing high-fidelity LES research. Full article

This research evaluates the feasibility of using a lignocellulosic biosorbent prepared from mature leaves of Asplenium scolopendrium (produced through simple mechanical processing of the leaves, without applying any chemical modification or heat treatment) for the removal of methylene blue from water. Before and after adsorption the material was characterized using SEM technique and color analysis. Subsequently, the adsorption behavior was analyzed by examining equilibrium, kinetic, and thermodynamic aspects of the process. The equilibrium data were best represented by the Sips isotherm model, while the adsorption rate followed the Avrami model. Thermodynamic evaluation indicated that the retention of the dye occurs predominantly through a physical adsorption mechanism, while a minor contribution from chemisorption may be present, slightly enhancing the overall dye uptake. Process optimization was performed using the Taguchi experimental design, which also allowed the identification of the most significant operational variable. In addition, analysis of variance (ANOVA) was applied to quantify the contribution of each factor affecting dye removal efficiency. Among the investigated variables, time showed the strongest influence (72.65%), whereas temperature had a negligible effect (1.33%). The maximum adsorption capacity reached 174.1 mg/g, surpassing the performance of several comparable biosorbents reported in the literature. Overall, the findings demonstrate that Asplenium scolopendrium (hart’s-tongue fern) leaves represent an inexpensive, sustainable, and efficient material for eliminating methylene blue from aqueous solutions. Full article

Estimates suggest that approximately 400 million dogs are kept as pets worldwide. Despite their popularity, around 10% to 30% are surrendered to rescue shelters each year. Shelter workers play a pivotal role in the success of dog adoptions and provide ongoing support to help owners keep these dogs in their homes. However, research that captures their perspectives and experiences regarding the dog adoption process remains limited. Royal Society for the Prevention of Cruelty to Animals Queensland shelter teams participated in six focus group discussions to share their perspectives and experiences of the dog adoption process in their shelters. Reflexive thematic analysis of the gathered data generated three themes: 1. “Doing great adoptions” starts with an inclusive, well-resourced application process and a skilled team. 2. Finding the right fit involves navigating the duality of carer and advocate through honest, informative interactions with the whole family. 3. Successful outcomes involve supporting and educating the public to care for and keep their dog. This grounded understanding of the challenges facing shelters in their work could provide valuable feedback to help shelter leaders and staff develop policies and practices that support positive adoption outcomes, tailor programmes to local needs, and reduce return rates. Full article

This study examines the relationships between green workplace mindfulness, employee productivity, green work engagement, and perceptions of a green workplace climate within healthcare organizations. Green workplace mindfulness (GWM) refers to employees’ awareness of how their daily work activities influence environmental sustainability and resource use. Drawing on the Job Demands–Resources (JD-R) and Conservation of Resources (COR) theoretical perspectives, the study proposes that sustainability-oriented mindfulness may function as a personal resource associated with employee engagement and work outcomes. Data were collected through a cross-sectional survey of 473 employees working in public and private hospitals in Libya. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among the study variables. The findings indicate that green workplace mindfulness is positively associated with employee productivity, both directly and indirectly through green work engagement (GWE). In addition, perceptions of a supportive green work climate (GWC) perception strengthen the relationships between mindfulness, engagement, and productivity. Specifically, the indirect association between mindfulness and productivity through engagement becomes stronger when employees perceive stronger environmental support within their organizations. These findings contribute to sustainability and organizational behavior research by demonstrating how individual awareness of environmental responsibility and supportive workplace climates jointly relate to employee engagement and productivity in healthcare settings. From a practical perspective, the results suggest that healthcare organizations can encourage sustainable performance by promoting environmental awareness among employees and by developing workplace climates that support environmentally responsible practices. Such initiatives may help healthcare institutions improve operational effectiveness while contributing to broader sustainability goals. Full article

With the rapid development of the low-altitude economy, constructing an accurate unmanned aerial vehicle (UAV) air-to-ground channel model is crucial for ensuring communication quality. However, due to the significant fluctuations in UAV operation altitudes and the complex propagation environment, traditional empirical models struggle to achieve universal high-precision prediction within a 3D airspace. This paper proposes a Physics-Informed Feature Engineering (PIFE) method and constructs a 3D signal strength prediction model in combination with Gradient Boosting Decision Tree (XGBoost). Unlike traditional purely data-driven methods, this paper explicitly extracts physical propagation features such as three-dimensional Euclidean distance and height-to-angle ratio, and specifically designs a height–path loss interaction term to capture the nonlinear coupling relationship of signal attenuation at different operating heights. The experimental results demonstrate that the model proposed in this paper performs excellently in multi-altitude airspace scenarios ranging from 70 m to 150 m. At the typical operation height of 70 m, the model achieves a high goodness of fit ($R^2$) of 0.843. Ablation experiments further confirm that the introduction of physical interaction features successfully breaks through the performance bottleneck of pure geometric features, proving the necessity of explicitly modeling the height–distance coupling effect in complex three-dimensional airspace. The research in this paper demonstrates the effectiveness of integrating physical priors with machine learning algorithms, providing an important theoretical basis and technical support for future drone network planning and coverage optimization in complex low-altitude environments. Full article

Certain extensions of $F$-controlled self-mappings in metric spaces to the, as called in this manuscript, $F_{\tau \text{'}\tau }$ and modified $*F_{\tau \text{'}\tau }$ controlled self-mappings, which are parameterized by two parameters, are addressed. Those parameters govern the properties of local expansivity, asymptotic nonexpansivity, and contractivity properties of the generated sequences. Also, further generalizations to parameterizations by two real sequences of parameters, which are referred to as $F_{{\left\{{\tau }_j^{\text{'}}\right\}}_{j=0}^{\infty }{\left\{{\tau }_j\right\}}_{j=0}^{\infty }}$-controlled self-mappings, are studied. The main formulated results rely on the asymptotic contractivity and the asymptotic nonexpansivity in metric spaces and some of their relevant properties. In particular, the properties of boundedness of the sequences of distances, as well as those of boundedness of the elements of the sequences themselves, are investigated under asymptotic contractivity or nonexpansivity related to the various types of the above-mentioned $F_{\left(.\right)}$-controlled self-mappings. Also, existence and uniqueness results of fixed points are proved if the metric space is complete, and the resulting Cauchyness properties of sequences and properties of the convergence of such sequences to fixed points are also proved. Finally, two illustrative examples are described if the $F_{\left(.\right)}$-controlled self-mappings are of a cyclic nature when defined using the union of two nonempty closed subsets of the metric space, in the case that those sets intersect, and also in the case when they are disjointed. Full article

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. Full article

This work proposes a general conceptual framework for integrating TRIZ (Theory of Inventive Problem Solving) structured reasoning into large language model (LLM)-based workflows for chemical and materials science. We argue that persistent AI challenges in this domain—data scarcity, weak scaffold transferability, unphysical predictions, and limited interpretability—are most naturally framed as TRIZ-style contradictions and that embedding contradiction-resolution logic into LLM reasoning can elevate AI from pattern-matching to inventive, researcher-like problem solving. Unlike prior AI–TRIZ integrations such as AutoTRIZ and TRIZ-GPT, which address general engineering tasks, the present framework extends TRIZ tools to physicochemical phenomena and targets local, privacy-preserving deployment. To illustrate the concept and identify directions for further development, we implement and evaluate a simplified three-stage proof-of-concept pipeline on nine local LLMs across eleven chemical problems. Results show that the TRIZ-guided pipeline substantially reduces token consumption—both overall and especially in the solution-generation stage—without an obvious loss in solution quality under the adopted evaluation criteria, suggesting considerable room for further improvement as the framework matures. Full article

Phenol derivatives display a prominent role in many biologically active molecules. Boron-containing molecules are considered valuable precursors for their synthesis. Therefore, the rise of photochemistry has led many researchers to develop novel, sustainable protocols that exploit the advantages offered by different irradiation sources. For this reason, the application of novel photocatalysts that promote challenging organic transformations is highly valued. Graphitic carbon nitride (g-C₃N₄) is a semiconductor photocatalyst widely used in organic chemistry for promoting complex organic transformations. Herein, we report a green and efficient methodology for the hydroxylation of boronic acids to the corresponding hydroxyl derivatives, using g-C₃N₄ as the photocatalyst. The heterogeneous photocatalyst (g-C₃N₄) was prepared by thermal polycondensation of melamine and characterized by XRD, FESEM/EDS, and UV–Vis diffuse reflectance spectroscopy. Green LED irradiation was employed as the energy source and air as the active oxidant. A variety of substrates were tested, showcasing excellent functional group tolerance in the aerobic photochemical protocol. Mechanistic studies were conducted to investigate the reaction pathway and to identify the oxygen species generated. Full article

With the continuous promotion of the dual carbon target, effective control of high-concentration dust pollutants in industrial sites is of great value for the healthy creation of healthy industrial environments and efficient energy utilization. In this study, we used the spraying method to improve and prepare the dust removal material, polyphenylene sulfide (PPS) fiber filter material, and test the filtration performance, resistance characteristics, and dust-cleaning effect of the improved rGO-PPS material. The results showed that, compared with PPS filter material, rGO-PPS material significantly improved particle filtration efficiency, with a filtration efficiency 0.058–19.417% higher in the particle size range of 0.265–5.75 μm. The higher the spraying concentration of the composite filter material, the higher the filtration efficiency at the same particle size. The comprehensive filtration performance of rGO-PPS composite filter material with a concentration of 3 g/L was better, as it better met the requirements of “high efficiency and low resistance”. With an increase in dust load, the filtration resistance of the filter material showed a continuous upward trend. The dust peeling rate increased with an increase in blowback wind speed. When the blowback wind speed reached 0.3 m/s, the dust-cleaning effect of the filter material tended to stabilize. Under this condition, the dust peeling rate of PPS filter material was 61.58%, and the dust peeling rate of 3 g/L rGO-PPS composite filter material reached 74.52%. These research results provide an experimental basis and technical support for the development and engineering application of high-efficiency purification filter materials for industrial multi-source pollutants. Full article

Chinese cabbage is one of the major vegetable crops in northern Asia. Its leaves are the major organ for photosynthesis and production, and leaf color directly influences its yield and quality. Here, we obtained a pale green mutant pgm3. This mutant line was derived from EMS mutagenesis of Chinese cabbage DH line FT. pgm3 exhibited chlorosis and etiolation, delayed growth, reduced photosynthetic pigment content and net photosynthetic rates, and impaired development of the chloroplast inner membrane system. Genetic analysis revealed that the pale green phenotype was controlled by a single recessive nuclear gene, Brpgm3. Mutmap analysis indicated that Brpgm3 is located on a 13.9 Mb region in A03. Within this region, a single SNP (A03: 7194530) with an SNP-index of 1, located in BraA03g015750.3C (BrClpC1), was identified from 40 differential SNPs. KASP genotyping demonstrated that the SNP co-segregated with the pale green phenotype in the F₂ population. Sanger sequencing confirmed a G-to-A SNP in exon 4 of BrClpC1, which resulted in an amino acid substitution from S to G. Furthermore, multiple sequence alignment of homologs from 28 species demonstrated that this mutated residue is highly conserved. BrClpC1 was predominantly expressed in leaves and exhibited the highest transcript abundance among the nine members of the Class I Clp gene family in Brassica rapa. This is the first report identifying ClpC1 in Brassica crops. Our results not only confirmed BrClpC1 as a strong candidate gene for the pale green mutant of Chinese cabbage, but also highlighted BrClpC1 as a target for chloroplast biology research in Brassica crops. Full article

Ship collision accidents remain a critical concern in maritime safety because of their potential to cause operational disruption as well as environmental and economic damage in areas with dense shipping activity. Complex traffic interactions, differences in vessel characteristics, and dynamic environmental conditions make collision risk increasingly difficult to manage using traditional navigation measures alone. This paper presents a structured review of ship collision research, focusing on collision impacts, collision avoidance strategies, risk assessment methodologies, and safety index development. The review synthesizes reported collision cases and their environmental consequences, examines commonly used analytical frameworks including probabilistic, data-driven, and multicriteria approaches, and discusses recent developments in AIS-based analysis, sensor-based monitoring, and intelligent prediction techniques. The analysis identifies several methodological gaps in existing studies. Collision avoidance methods and risk assessment models are often developed independently, while their integration with safety index frameworks remains limited. In addition, safety index formulations differ considerably in terms of indicator selection and modeling approaches, which reduces comparability between studies conducted in different waterways. The findings highlight how different analytical approaches contribute to maritime safety evaluation at strategic, operational, and real-time levels and provide insights for developing more integrated safety assessment frameworks to support navigation risk monitoring in high-traffic maritime environments. Full article