Abstract: This research introduces an edge-optimized reinforcement learning (RL) ecosystem engineered for sustainable logistics in the blue economy, spanning maritime shipping, automated port operations, and offshore resource transportation. At its core, the system processes vast streams of real-time data from IoT sensors embedded in vessels, buoys, and drones directly at edge nodes, bypassing cloud latency to enable instantaneous decision-making in unpredictable marine conditions like storms or currents. Carbon capture analytics, derived from spectroscopic sensors quantifying direct air capture (DAC) efficiency and CO2 sequestration rates on ships, dynamically adjusts RL reward functions to favour fuel-efficient paths that maximize emissions offsets, aligning with International Maritime Organization (IMO) mandates for net-zero operations by 2050. The framework exploits 6G networks' terabit speeds, sub-millisecond latency, and non-terrestrial network integration via low-earth-orbit satellites for seamless swarm intelligence orchestration. Autonomous agents unmanned surface vessels (USVs), aerial drones, and autonomous underwater vehicles (AUVs) exhibit flocking behaviour’s inspired by particle swarm optimization, sharing pheromone-like digital signals over holographic beamforming channels to collaboratively resolve complex tasks like dynamic routing, collision avoidance, and load redistribution. Methodologically, proximal policy optimization (PPO) algorithms facilitate stable, lightweight training on resource-constrained edge hardware, augmented by federated learning to aggregate insights across privacy-sensitive multi-operator fleets without central data pooling. Rigorous evaluations in NS-3 for 6G emulation and Gazebo for maritime physics reveal transformative gains: 42% reductions in carbon footprints, 65% lower end-to-end latency versus 5G-cloud hybrids, and 30% improvements in throughput under adverse weather. Scalability tests with 1000+ agents confirm robustness in GPS-denied zones, while ablation studies highlight the synergistic impact of carbon feedback and swarm coordination over siloed baselines like genetic algorithms or centralized RL. By embedding quantum-safe encryption for 6G links and digital twin interfaces for predictive maintenance, this ecosystem not only decarbonizes blue economy logistics but also sets a scalable blueprint for AI-driven sustainability in cyber-physical systems worldwide.

Abstract: Over-the-air (OTA) updates in multi-tenant systems often face task conflicts, cache overlap, and weak fault recovery during parallel updates. This study designed a layered fault-tolerance and isolation method that combines task redundancy, cache separation, and snapshot rollback. Tests were carried out on 120 devices across six tenants with a fault rate of up to 95%. The system kept stable operation, extended the mean time between failures (MTBF) to 182 hours, and raised total availability from 98.2% to 99.7%. The average update delay per tenant stayed below 1.1 seconds, showing that higher reliability did not slow the process. The method effectively avoided tenant interference, reduced recovery time, and improved update stability. It provides a simple and practical solution for dependable OTA updates in industrial, automotive, and IoT systems.

Abstract: The use of light (photons) delivered clinically from laser or light-emitting diodes (LED), is referred to as photobiomodulation therapy (PBMt). Increasingly PBMt is accepted particularly in dental practice for pain or pre-emptive anaesthesia. Understanding its mechanism of effectiveness is the key to its increasing acceptance. Of major importance to this is how PBMt affects not only the neurons but also the Schwann cells and fibroblasts of the peripheral nervous system which are unique in morphology and function. The specific roles of the neuronal cells of the dorsal root and trigeminal ganglia, now include consideration of the axon initial segment responsible for the initiation of the action potential and the T junction from which the distal and proximal axons arise which are complex but central to normal function. This cellular complexity, organization and function is discussed leading to a review of the mechanism of effectiveness of PBMt demonstrated by clinical trials in both medicine and dentistry. This review provides evidence of the involvement of the cytoskeleton, mitochondrial organization particularly related to fast and slow axonal flow and mitochondrial membrane potential in response to light in somatosensory neurons and nerves.

Abstract: The centrosome, long recognized as the primary microtubule-organizing center (MTOC) of animal cells, is re-examined through the lens of information theory and systems biology. This preprint proposes a unifying hypothesis: the mother centriole within the centrosome acts as a non-genetic cellular ledger, a stable structural repository that accumulates molecular records of a cell’s replicative history and environmental exposures. These records—comprising specific post-translational modification (PTM) signatures, retained proteins, and structural alterations—are subsequently “read” by the cell to inform critical decisions regarding proliferation, differentiation, senescence, and apoptosis. We synthesize evidence from cell biology, gerontology, and evolutionary biology to construct the “Centrosomal Ledger Model.” This model positions the centriole not as a passive cytoskeletal component but as an active, heritable information-processing node that integrates temporal data across scales—from circadian rhythms to organismal aging. We detail the molecular mechanisms of information encoding (e.g., tubulin polyglutamylation, oxidative marks) and decoding (via ciliary signaling, proteostatic feedback, and mechanical transduction). The model’s implications challenge genetic determinism by highlighting structural inheritance, provides a material basis for cellular age, and offers novel, falsifiable avenues for experimental interrogation in aging and cancer research. Crucially, it suggests that modulating the “read-write” cycle of the centrosomal ledger could represent a new frontier in regenerative medicine.

Abstract: This study assessed the spatial distribution and composition of airborne particulate matter within a 10-km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban park contexts (e.g., commercial zones, malls, bus stop), revealing mass-specific magnetic susceptibility χ values ranging from -6.71 to 61.1 × 10⁻⁸ m³kg-1. Three compositional groups were identified based on a PCA performed using elemental concentrations from SEM-EDS and magnetic data, which are associated with traffic emissions and industrial inputs. SEM-EDS images confirmed abundant magnetite-like particles (1–8 μm) with hazardous metals including Pb (up to 5.6 wt.%), Ba (up to 67.6 wt.%), and Cr (up to 31.5 wt.%). Wind direction data indicated predominant SSW-NNE transport, correlating with hotspots in central and northeast-ern park areas. Overall, vegetated zones displayed significantly lower magnetic loads (mean χ = 8.84 × 10⁻⁸ m³kg⁻¹, σ = 6.65 × 10⁻⁸ m³kg⁻¹) compared to traffic-exposed sites (mean χ = 17.27 × 10⁻⁸ m³kg⁻¹, σ = 12.44 × 10⁻⁸ m³kg⁻¹), emphasizing the pollution mitiga-tion role of green barriers. This research highlights the applicability of combined mag-netic and microscopic techniques for evaluating the dynamics of airborne pollution in urban parks and supports their use as biofunctional filters in cities facing vehicular air pollution.

Abstract: This paper undertakes a foundational exploration of the nature of mathematics from both historical and philosophical perspectives, with a primary focus on the Indian intellectual tradition. It traces the evolution of mathematical thought from ancient Vedic texts such as the ´Sulba S¯utras, through the formal grammar of P¯an. ini, to modern abstract mathematics including group theory, automata, and topology. The investigation is rooted in the dual inquiries of ontology and epistemology, examining what it means for mathematics to be and how mathematical knowledge is constructed and validated. Particular emphasis is placed on the Indian concepts of gan. ita (mathematics), ´s¯unya (zero), and ´s¯unyat¯a (emptiness), and their correspondence with Western notions such as the Cartesian dualism, the set-theoretic empty set, and symbolic logic. The paper explores the recursive cosmological cycles found in Indian time theory, mathematical cosmology, and ritual geometry, showing how these ideas anticipated or paralleled developments in modern mathematics, including measure theory, combinatorics, and fractals. With detailed references to logical systems (Ny¯aya), sacred architecture (v¯astu-´s¯astra), cyclic time constructs (kalpas and yugas), and formal structures in linguistic grammar (As.t. ¯adhy¯ay¯ı), the paper argues for a view of mathematics as both a sacred science and a system of abstract formalism. Across these investigations, mathematical structures are treated not merely as tools for calculation but as profound reflections of metaphysical principles, visualizable through mandalas, yantras, and cosmological diagrams. This study invites a reassessment of how different cultures have understood and visualized mathematics as an expression of cosmic and cognitive order.

Abstract:

Leuenbergeria bleo (Kunth) DC. (Cactaceae), previously classified as Pereskia bleo, represents a phylogenetically basal cactus species with a disjunct distribution across Central America, Southeast Asia, and southern China. Phytochemical investigations have traditionally emphasized small-molecule secondary metabolites, including phenolics, alkaloids, and terpenoids, which contribute to antioxidant and anti-inflammatory activities. However, recent peptidomic analyses have expanded this chemical space through the discovery of bleogens, a family of hyper-stable, cysteine-rich microproteins with specific antifungal and wound-healing properties. This review systematically integrates botanical characteristics, ethnomedicinal applications, and pharmacological profiles, providing a comparative analysis of the plant’s small-molecule constituents versus its peptidyl biologics. It identifies the co-existence of these distinct chemical classes as a defining feature of the plant’s efficacy while highlighting the need for future research into their potential interactions.

Abstract: The increasing demand for recombinant proteins has driven innovation in bioprocessing strategies using Komagataella phaffii as a host organism. Conventional fed-batch cultivation with pure methanol induction remains widely used but presents challenges including high methanol consumption, extended downtime, and elevated operational costs. This study evaluates alternative strategies combining mixed induction (methanol/sorbitol) with continuous cultivation to enhance productivity, sustainability, and improved economic outcome. Using KEX2 protease as a model industrial recombinant protein, we compared four cultivation modes: fed-batch with methanol (benchmark), fed-batch with mixed induction, continuous with methanol, and continuous with mixed induction. Cell growth, volumetric yield, and specific productivity were evaluated at 5L scale and then modelled to simulate industrial scales (40 L and 400 L). Results demonstrate that continuous cultivation with mixed induction significantly improves yield up to 9-fold compared to conventional fed-batch and reduces methanol usage and oxygen demand. Techno-economic simulations reveal that a 40 L continuous process can match or exceed the output of two 400 L fed-batch runs, while lowering capital and operating costs and minimising environmental footprint. This integrated strategy offers a scalable, cost-effective, and safer alternative for recombinant protein production, supporting the development of compact and sustainable manufacturing platform

Abstract:

The Warburg effect, classically defined as the preferential use of glycolysis by cancer cells in the presence of oxygen, has been a central concept in cancer biology since a long time. Otto Warburg had originally proposed that defective mitochondrial respiration was the primary cause of aerobic glycolysis in cancer cells. While this hypothesis profoundly influenced early cancer metabolism research, it has now become increasingly clear that this interpretation has gaping. Advances in biochemistry, molecular biology and metabolomics demonstrate that mitochondria in many cancers are functional and play essential roles in biosynthesis, signaling and energy production. Aerobic glycolysis in cancer cells is now recognized as an adaptive metabolic strategy that supports rapid proliferation by providing metabolic intermediates, maintaining redox balance, and enabling cellular signaling rather than maximizing ATP yield. This review discusses the Warburg effect through the lens of modern cancer metabolism. It contrasts classical misconceptions with current evidences, discusses key regulatory pathways like HIF-1α, PI3K/Akt/mTOR, c-Myc and PKM2, and examine the central role of lactate as both a metabolic fuel and a signaling molecule. It further explores metabolic heterogeneity, the reverse Warburg effect, immune–metabolic interactions, and the relevance of oxidative phosphorylation in cancer. Finally, some unresolved questions are highlighted that is critical for future understanding of cancer metabolism.

Abstract: This systematic review examined the validity and reliability of wearable inertial sensor systems to quantify spatiotemporal gait parameters in post‑stroke adults, a population in which gait asymmetry, altered motor control, and compensatory strategies challenge accurate measurements. Four databases were searched up to December 2025, and studies were included when they assessed concurrent validity or test–retest reliability of wearable derived spatiotemporal parameters against laboratory-based reference systems. Fifteen studies involving a total of 286 participants were analyzed. Spatial parameters as gait speed, cadence, and step and stride length showed consistent good‑to‑excellent agreement with reference instruments and high test–retest reliability. Temporal parameters demonstrated greater heterogeneity, with larger absolute errors, wider limits of agreement, and lower relative agreement, particularly for swing time. Paretic‑side measurements showed reduced between instrument agreement compared to the non‑paretic side, although within‑subject reliability remained moderate to high. No consistent influence of sensor number on measurement accuracy. Overall, wearable inertial sensors provide robust estimates of spatial gait parameters in post‑stroke populations, while temporal outcomes remain limited, likely due to the challenge to detect gait events that arise from stroke-related alterations in gait biomechanics. Taking these findings as a whole suggests that deviations from regular gait biomechanics, whether due to reduced speed particularly at low walking speeds of 0.4 m/s, asymmetry, or to the use of assistive devices, compromise the ability of wearable-based algorithms to accurately identify gait events.

Abstract: This paper presents a transformer-infused semantic sensing ecosystem that integrates post-quantum signatures with 6G-enabled digital twins to enable adaptive orchestration in next-generation smart systems. Conventional IoT architectures struggle with semantic understanding across heterogeneous sensor streams, vulnerability to quantum attacks, and synchronization delays between physical and digital representations. The proposed platform deploys transformer models optimized for multi-modal sensor fusion to extract contextually rich semantic features from raw measurements, feeding these insights into digital twins synchronized over 6G networks with microsecond precision. Post-quantum lattice-based signatures ensure data integrity and authentication across the high-velocity sensing-orchestration pipeline, resisting both classical and quantum adversaries. The adaptive orchestration engine leverages twin predictions and semantic context to generate control policies that optimize system performance under dynamic conditions. Evaluation across industrial, urban, and autonomous transport scenarios demonstrates 3.8× improvement in semantic inference accuracy, 92% reduction in twin synchronization error, and 28% latency reduction compared to baseline architectures, while maintaining quantum-resistant security guarantees. The framework establishes a blueprint for secure, semantically-aware smart ecosystems capable of real-time adaptive orchestration at 6G scale.

Abstract: Central tolerance is provided by the AIRE-expressing medullary thymic epithelial cells, through high avidity recognition of self-antigens. Nevertheless, peripheral mechanisms regulate adaptive immunity by deleting autoreactive T-cells that escape thymic selection or inducing their functional unresponsiveness. These mechanisms require interaction with antigen presenting cells exposing cognate antigen. As regard multiple types of extrathymic AIRE-expressing cells, residing in secondary lymphoid organs, were described. In this study we aimed to provide evidence for AIRE binding to promoter regions of known autoantigens in human peripheral blood mononuclear cells (PBMC) in an attempt to elucidate whether this non-classical transcriptional factor could play a role in the pe-ripheral expression of self-antigens. Chromatin immunoprecipitation (ChIP) of 4 normal human PBMC samples was performed using anti-AIRE monoclonal antibody. Quantitative-Real-Time PCR (qRT-PCR) was used to detect AIRE-binding at promoters of known autoantigens, including thyroidrelated thyroglobulin, thyroperoxidase, thyrotropin-receptor, Type 1 diabetes-related autoantigens, i.e. insulin and zinc transporter 8, and to confirm their expression in PBMC. ChIP evidenced amplicons of promoter regions of mentioned autoantigens by qRT-PCR. Expression of AIRE and of autoantigens was confirmed in the same human PBMC samples. This study provides the first evidence that AIRE binds promoters of known autoantigens in human PBMC, supporting its expression and potential role in modulating peripheral self-antigen expression.

Abstract: Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artifi- cial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 615 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and 6 prognostic applica- tions, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E) stained images, while the remainder relied on diverse modalities, including IHC stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary appli- cation was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progres- sion (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the "black box" nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments.

Abstract: Background: persistent strength deficits and psychological impairments may compro-mise return to sport (RTS) after anterior cruciate ligament reconstruction (ACLR). Ob-jective: to investigate the relationship between thigh muscle isokinetic strength recovery at six months after ACLR and long-term psychological outcomes related to RTS in competitive male soccer players. Methods: sixty male soccer players who underwent primary ACLR with bone–patellar tendon–bone autograft were retrospectively analyzed. Isokinetic testing of quadriceps and hamstrings was performed one week before surgery and six months post-surgery at 90°/s and 180°/s. Limb symmetry index (LSI) was calcu-lated both pre- and post-operatively. At long-term follow-up (mean ≈4 years after RTS), athletes completed questionnaires assessing RTS status, ACL re-injuries, sport-related perceptions, and kinesiophobia using the Tampa Scale for Kinesiophobia (TSK). Results: absolute quadriceps and hamstring peak torque values significantly increased from pre- to post-surgery, with quadriceps strength deficits persisting only in the operated limb. However, quadriceps LSI significantly decreased post-operatively, while hamstring LSI remained stable. Overall, RTS rate was 91.7%, but a second ACL injury occurred in 18.2% of athletes. High kinesiophobia (TSK ≥ 37) was present in 56.7% of the cohort at long-term follow-up. Conclusions: despite significant strength gains, quadriceps limb symmetry worsened six months after ACLR, with deficits confined to the operated limb, suggesting persistent neuromuscular inhibition. These physical deficits coexist with long-term ki-nesiophobia despite high RTS rates. Findings highlight the limitations of time-based and strength-only RTS criteria and support the need for an integrated physical, psychological, and neuro-cognitive approach to rehabilitation and RTS decision-making.

Abstract: Accurate prediction of pedestrian intention and future paths is essential for traffic safety, urban planning, and autonomous navigation. This study develops a multimodal prediction model that combines meaning-based image-text features, motion trajectories, and social interactions. We extract visual-language information from RGB sequences using a CLIP-based encoder and represent group behavior using a Social-GRU network. To improve the reliability of predictions, we apply Bayesian modeling to manage uncertainty. We tested the method on the Waymo and ETH/UCY datasets. On the ETH dataset, the model achieved a 14.2% reduction in average displacement error and a 17.6% reduction in final displacement error, compared with leading baseline methods. The model remained effective in crowded spaces, unclear visual conditions, and sudden motion changes. The results confirm that combining visual-language and motion data improves prediction accuracy. This method offers a practical solution for real-world pedestrian analysis in intelligent transport systems.

Abstract: Porous eco-materials—such as perlite, pumice composites, foamed concretes, and bio-derived cellular solids—are increasingly used in sustainable construction due to their low density, thermal insulation capacity, and reduced environmental impact. However, their mechanical characterization remains incomplete, particularly with respect to transverse shear behavior. Classical formulas for the shear correction factor ks, typically derived for homogeneous continua, are unsuitable for porous media exhibiting local density gradients, irregular pore morphologies, and spatially varying stiffness. This paper presents a generalized analytical–numerical methodology for evaluating the shear correction factor in a wide class of porous eco-materials. The approach is based on the strain-energy equivalence principle and uses a continuous stiffness model that reflects density-dependent elastic properties. A voxel-based microstructural representation is employed to validate the analytical predictions and to quantify the influence of heterogeneity on the shear stress distribution. Perlite is used as a representative case study, demonstrating how classical homogeneous formulas may produce errors exceeding 40%, while the proposed method provides significantly improved agreement with numerical benchmarks. The framework is applicable to a broad range of porous materials and offers a consistent basis for predicting transverse shear stiffness in lightweight fillers, thermal barriers, and fire-protective building components where shear deformation is critical.

Abstract: Volume fraction, or one-point statistics, is commonly used to homogenize composites. However, it contains no geometric information regarding the spatial distribution of the phases. The spatial distribution can be characterized using higher-order statistics. Two-point statistics (f₂) quantify average relative phase positions, and the geometric features encoded in f₂ influence material properties. However, just as a single volume fraction can describe multiple unique microstructures, some f₂ map to multiple distinct microstructures. The existence of multiple microstructures possessing the same f₂ is termed ‘degeneracy’ and is problematic for microstructure-sensitive design because unique microstructures may map to the same f₂ yet exhibit different properties. This study quantifies how pervasive degeneracy is in f₂ through exhaustive enumeration of all 2³⁶ ≈ 6.9¹⁰ possible 6 × 6 binary microstructures, and tests other metrics as ways to uniquely characterize microstructures with degenerate f₂. We determined that using nondirectional f₂ (i.e., orientation-averaged f₂) substantially increases degeneracy, nearly doubling the probability that a randomly selected microstructure will share the same f₂ as some other symmetry-inequivalent microstructure. Notably, the fraction of nontrivially degenerate microstructures does not increase monotonically with system size—a counterintuitive finding that challenges prior theoretical predictions. Finally, for the small microstructures examined, we determined that three-point statistics will fully resolve the degeneracy at a computational cost that scales as n⁴ (where n is side length), while two-point cluster functions resolve the majority of degeneracies with substantially lower computational overhead.

Abstract: In this work, we analyze the reasons for the absence of immortality from the perspective of “genetic information metabolism.” All living organisms synthesize and release genes, including novel and previously unknown genes, into the external environment through the process of genetic information metabolism. As a result, new genes become available for inclusion in the unified complex of genetic information represented by all living and non-living carriers, which has been termed the “Pangenome,” ensuring the maintenance of life on Earth under changing biotic and abiotic conditions. Part of the newly created genetic information remains inaccessible to spreading to other members of the Pangenome during the lifetime of an organism and can only be released after its death. We hypothesize, to our knowledge for the first time, that the absence of immortality is associated with the necessity of releasing novel genes for spread within the Pangenome, which can happen efficiently only after an organism’s death. We define the spread of genes and their integration into the genomes of other organisms as “gene reincarnation.” Within the Pangenome, genes are redistributed, ensuring the further evolution of life. We formulate a new definition of death as “a stage in the metabolism of genetic information during which all genes of an organism become available for reincarnation.” This understanding for the first time views death as a crucial part of the genetic cycle of life. Based on above novel concepts, we propose certain properties that immortal organisms should possess.

Abstract: The human microbiome is increasingly recognized as a key component of women’s reproductive health. This narrative review examines the vaginal, endometrial, and gut microbiota and their roles in the pathogenesis of gynecologic and obstetric disorders, with the aim of integrating current evidence into a clinically relevant framework. We review intrinsic (genetic, hormonal, and immunological) and extrinsic (environmental, lifestyle, and pharmacological) factors shaping microbial composition, with particular focus on dysbiosis and the role of the gut estrobolome in estrogen metabolism. The review synthesizes data on microbiome alterations associated with endometriosis, adenomyosis, uterine fibroids, endometrial polyps and hyperplasia, gynecologic malignancies, pelvic inflammatory disease, bacterial vaginosis, infertility, and adverse obstetric outcomes including preterm birth and fetal growth restriction. Methodological approaches used to characterize the reproductive tract microbiota, such as vaginal swabs, endometrial sampling, and fecal analysis, are critically discussed, alongside limitations related to low-biomass environments and contamination risk. Evidence regarding therapeutic modulation of the microbiome, including antibiotics, probiotics, hormonal therapies, and emerging microbiota-based interventions, is summarized, as are the effects of gynecologic surgery on microbial translocation and long-term microbial balance. Overall, the available literature supports an association between microbiota alterations and multiple reproductive conditions, although causality remains incompletely defined. Further standardized and longitudinal studies are needed to clarify mechanisms and guide microbiome-informed diagnostic and therapeutic strategies.

Abstract:

Background: Formin proteins are crucial regulators of actin filament assembly and elongation in eukaryotic cells, playing important roles in plant development and abiotic stress responses. However, the functional characterization of formins in Brassica rapa remains uncover. Methods: A total of 27 formin family members (BrFHs) were identified through genome-wide alignment with Arabidopsis thaliana. Results: Phylogenetic analysis classified BrFH gene family into two distinct clades, designated Group I and Group II, which exhibit divergent protein architectures. Promoter analysis revealed that BrFHs contain multiple cis-regulatory elements related to growth and development, stress responses, and phytohormone signaling. These findings suggest that BrFHs may have diversified functions. Tissue-specific expression analysis revealed that BrFHs exhibit distinct expression patterns across various tissues. Notably, BrFH15 and BrFH18 are highly expressed in flowers, displaying expression profiles similar to those of floral development genes such as AP3, AGL10 and so on. Additionally, many BrFHs show dynamic expression patterns in response to cold stresses. In particular, BrFH2, BrFH19 and BrFH27 were up-regulated, and their co-expression within the gene network suggests potential roles in regulating cold stress. Conclusions: These results clarify the functional roles of BrFHs and shed light on the molecular mechanisms underlying their regulation of tissue development and responses to abiotic stress in Brassica rapa.