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Cloud-Edge Collaboration-Based Data Processing Method for Distribution Terminal Unit Edge Clusters
No full textDistribution terminal units (DTUs) play critical roles in smart grid for supporting data acquisition, remote monitoring, and fault management. A single DTU generates continuous data streams, imposing new challenges on data processing. To tackle these issues, a cloud-edge collaboration-based data processing method is introduced for DTU edge clusters. First, considering the load imbalance degree of DTU data queues, a cloud-edge integrated data processing architecture is designed. It optimizes edge server selection, the offloading splitting ratio, and edge-cloud computing resource allocation in a collaboration mechanism. Second, an optimization problem is formulated to maximize the weighted difference between the total data processing volume and the load imbalance degree. Next, a cloud-edge collaboration-based data processing method is proposed. In the first stage, cloud-edge collaborative data offloading based on the load imbalance degree, and a data volume-aware deep Q-network (DQN) is developed. A penalty function based on load fluctuations and the data volume deficit is incorporated. It drives the DQN to evolve toward suppressing the fluctuation of load imbalance degree while ensuring differentiated long-term data volume constraints. In the second stage, cloud-edge computing resource allocation based on adaptive differential evolution is designed. An adaptive mutation scaling factor is introduced to overcome the gene overlapping issues of traditional heuristic approaches, enabling deeper exploration of the solution space and accelerating global optimum identification. Finally, the simulation results demonstrate that the proposed method effectively improves the data processing efficiency of DTUs while reducing the load imbalance degree
Application of Vis–NIR Spectroscopy and Machine Learning for Assessing Soil Organic Carbon in the Sierra Nevada de Santa Marta, Colombia
No full textSoil organic carbon (SOC) is an essential indicator of soil fertility, health, and carbon sequestration capacity. Its proper management improves soil structure, productivity, and resilience to climate change, making rapid and reliable SOC assessment essential for sustainable agriculture. Visible and near-infrared (Vis–NIR) spectroscopy offers a non-destructive and cost-effective alternative to conventional laboratory analyses, allowing for the simultaneous estimation of multiple soil properties from a single spectrum. This study aimed to predict SOC content using machine learning techniques applied to Vis–NIR spectra of 860 soil samples collected in the Sierra Nevada de Santa Marta, Colombia. The spectra (400–2500 nm) were acquired using a NIR spectrophotometer, and the soil organic carbon (SOC) content was quantified using a wet oxidation method that employs dichromate in an acidic medium. A hybrid modeling framework combining Random Forest (RF) with support vector regression (SVR) and XGBoost was implemented. Spectral pretreatments (Savitzky–Golay first derivative, MSC, and SNV) were compared, and spectral bands were selected every 10 nm. The 30 most relevant wavelengths were identified using RF importance analysis. Data were divided into training (80%) and test (20%) subsets using stratified random sampling, and five-fold cross-validation was applied for parameter optimization and overfitting control. The RF–XGBoost (R2 = 0.86) and RF–SVR (R2 = 0.85) models outperformed the individual RF and SVR models (R2 < 0.7). The proposed hybrid approach, optimized through features, and advanced spectral preprocessing demonstrate a robust and scalable framework for rapid prediction of SOC and sustainable soil monitoring
Elucidating Scent and Color Variation in White and Pink-Flowered Hydrangea arborescens ‘Annabelle’ Through Multi-Omics Profiling
No full textThe color and scent of flowers are vital ornamental attributes influenced by a complex interaction of metabolic and transcriptional mechanisms. Comparative analyses were performed to determine the molecular rationale for these features in Hydrangea arborescens, between the white-flowered variety ‘Annabelle’ (An) and its pink-flowered variant ‘Pink Annabelle’ (PA). Gas chromatography–mass spectrometry (GC–MS) detected 25 volatile organic compounds (VOCs) in ‘An’ and 21 in ‘PA’, with 18 chemicals common to both types. ‘An’ exhibited somewhat higher VOC diversity, whereas ‘PA’ emitted much bigger quantities of benzenoid and phenylpropanoid volatiles, including benzaldehyde, benzyl alcohol, and phenylethyl alcohol, resulting in a more pronounced floral scent. UPLC–MS/MS metabolomic analysis demonstrated obvious clustering of the two varieties and underscored the enrichment of phenylpropanoid biosynthesis pathways in ‘PA’. Transcriptomic analysis revealed 11,653 differentially expressed genes (DEGs), of which 7633 were elevated and linked to secondary metabolism. Key biosynthetic genes, including PAL, 4CL, CHS, DFR, and ANS, alongside transcription factors such as MYB—specifically TRINITY_DN5277_c0_g1, which is downregulated in ‘PA’ (homologous to AtMYB4, a negative regulator of flavonoid biosynthesis)—and TRINITY_DN23167_c0_g1, which is significantly upregulated in ‘PA’ (homologous to AtMYB90, a positive regulator of anthocyanin synthesis), as well as bHLH, ERF, and WRKY (notably TRINITY_DN25903_c0_g1, highly upregulated in ‘PA’ and homologous to AtWRKY75, associated with jasmonate pathway), demonstrating a coordinated activation of color and fragrance pathways. The integration of metabolomic and transcriptome data indicates that the pink-flowered ‘PA’ variety attains its superior coloring and aroma via the synchronized transcriptional regulation of the phenylpropanoid and flavonoid pathways. These findings offer novel molecular insights into the genetic and metabolic interplay of floral characteristics in Hydrangea
Influence of Initial Stress on Wave Propagation in Microelongated Thermo-Elastic Media Under the Refined Fractional Dual Phase Lag Model
No full textThis paper focuses on analyzing how initial stress influences wave propagation phenomena in a microelongated thermoelastic medium described within the framework of fractional conformable derivative, considering both the dual phase lag (DPL) and refined dual phase lag (RDPL) theories. The fundamental governing equations for heat transfer, mechanical motion, and microelongation are established to incorporate finite thermal wave speed and microelongation effects. Through an appropriate non-dimensionalization procedure and the application of the normal mode analysis technique, the coupled partial differential system is transformed into a form that admits explicit analytical solutions. These solutions provide expressions for displacement, microelongation, temperature distribution, and stress components, allowing a comprehensive examination of the thermomechanical wave behavior within the medium. To better comprehend the theoretical results, numerical evaluations are performed to emphasize the comparison of DPL and RDPL in the presence and absence of initial stress, as well as the influence of the fractional-order parameter and different times on wave properties. The results show that initial stress has a considerable effect on wave propagation characteristics such as amplitude modulation, propagation speed, and attenuation rate. Furthermore, the use of fractional conformable derivatives and the RDPL formulation allows for more precise modeling and control of the thermal relaxation dynamics. The current study contributes to a better understanding of the linked microelongated and thermal effects in thermoelastic media, as well as significant insights for designing and modeling advanced microscale thermoelastic systems
Cannabinoid Signaling and Autophagy in Oral Disease: Molecular Mechanisms and Therapeutic Implications
No full textAutophagy is a well-preserved biological mechanism that is essential for sustaining homeostasis by degradation and recycling damaged organelles, misfolded proteins, and other cytoplasmic detritus. Cannabinoid signaling has emerged as a prospective regulator of diverse cellular functions, including immunological modulation, oxidative stress response, apoptosis, and autophagy. Dysregulation of autophagy contributes to pathogenesis and treatment resistance of several oral diseases, including oral squamous cell carcinoma (OSCC), periodontitis, and gingival inflammation. This review delineates the molecular crosstalk between cannabinoid receptor type I (CB1) and type II (CB2) activation and autophagic pathways across oral tissues. Cannabinoids, including cannabidiol (CBD) and tetrahydrocannabinol (THC), modulate key regulators like mTOR, AMPK, and Beclin-1, thereby influencing autophagic flux, inflammation, and apoptosis. Experimental studies indicate that cannabinoids inhibit the PI3K/AKT/mTOR pathway, promote reactive oxygen species (ROS)-induced autophagy, and modulate cytokine secretion, mechanisms that underline their dual anti-inflammatory and anti-cancer capabilities. In addition, cannabinoid-induced autophagy has been shown to enhance stem cell survival and differentiation, offering promise for dental pulp regeneration. Despite these promising prospects, several challenges remain, including receptor selectivity, dose-dependent variability, limited oral bioavailability, and ongoing regulatory constraints. A deeper understanding of the context-dependent regulation of autophagy by cannabinoid signaling could pave the way for innovative therapeutic interventions in dentistry. Tailored cannabinoid-based formulations, engineered for receptor specificity, tissue selectivity, and optimized delivery, hold significant potential to revolutionize oral healthcare by modulating autophagy-related molecular pathways involved in disease resolution and tissue regeneration
Impact of Zirconia and Titanium Implant Surfaces of Different Roughness on Oral Epithelial Cells
No full textBackground/Objectives: Formation of tight contacts between oral soft tissue and dental implants is a significant challenge in contemporary implantology. An essential role in this process is played by oral epithelial cells. In the present study, we investigated how titanium and zirconia surfaces with different roughness influence various parameters of oral epithelial cells in vitro. Methods: We used the human oral squamous carcinoma Ca9-22 cell line and cultured them on the following surfaces: machined smooth titanium (TiM) and zirconia (ZrM) surfaces, as well as sandblasted and acid-etched titanium moderately rough (SLA) and zirconia (ZLA) surfaces. Cell proliferation/viability was measured by CCK-8 assay, and cell morphology was analyzed by fluorescent microscopy. The gene expression of interleukin (IL)-8, intercellular adhesion molecule (ICAM)-1, E-cadherin, integrin (ITG)-α6, and ITG-β4 was measured by qPCR, and the content of IL-8 in conditioned media by ELISA. Results: At the initial culture phase, cell proliferation was promoted by rougher surfaces. Differences in cell attachment were observed between machined and moderately rough surfaces. Machined surfaces were associated with slightly higher IL-8 levels (p < 0.05). Furthermore, both ZLA and SLA surfaces promoted the expression of (ITG)-α, ITG-β4, and ICAM-1 in Ca9-22 cells (p < 0.05). Surface material had no impact on the investigated parameters. Conclusions: Under the limitations of this in vitro study, some properties of oral epithelial cells, particularly the immunological and barrier function, are moderately modified by roughness but not by material. Hence, the roughness of the implant surface might play a role in the quality of the peri-implant epithelium
AgentRed: Towards an Agent-Based Approach to Automated Network Attack Traffic Generation
No full textNetwork security tools are indispensable in testing and evaluating the security of computer networks. Existing tools, such as Hping3, however, offer a limited set of options and attack-specific configurations, which restrict their use solely to well-known attack patterns. Although highly parameterizable libraries, such as Scapy, provide more options and scripting capabilities, they require extensive manual setup and often a steep learning curve. The development of powerful AI models, capitalizing on the transformer architecture, has enabled cybersecurity researchers to develop or incorporate these models into existing cyber-defense systems and red-team assessments. Prominent models such as NetGPT, TrafficFormer, and TrafficGPT can be effective, but require extensive computational resources for fine-tuning and a complex setup to adapt to proprietary networking environments and protocols. In this work, we propose AgentRed, a lightweight tool for generating network attack traffic with minimal human configuration and setup. Our tool integrates an AI agent and a large language model with fewer than a billion parameters into the network traffic generation process. Our method creates lightweight Low-Rank Adaptation (LoRA) adapters that can learn specific traffic patterns in a particular network environment. Our agent can autonomously train the LoRA adapters, search online documentation for attack patterns and parameters, and select appropriate adapters to generate network traffic specific to the user’s needs. It utilizes the LoRA adapters to create an intermediate traffic representation that can be parsed and executed by tools such as Scapy to generate malicious traffic in a virtualized test environment. We assess the performance of the proposed approach on six popular network attacks, including flooding attacks, Smurf, Ping-of-Death, and normal ICMP ping traffic. Our results validate the ability of the proposed tool to efficiently generate network packets with 97.9% accuracy using the LoRA adapters, compared to 95.4% accuracy using the base pre-trained Qwen3 0.6B model. When the AI agent performs online searches to enrich the LoRA adapters’ context during traffic generation, our method maintains an accuracy of 96.0% across all tested traffic patterns
Phosphomimetic Thrombospondin-1 Modulates Integrin β1-FAK Signaling and Vascular Cell Functions
No full textThrombospondin-1 (TSP1) is a multifunctional glycoprotein that plays a crucial role in angiogenesis and vascular remodeling. Ser93 of TSP1 has recently been identified as a novel phosphorylation site, influencing angiogenic properties; however, the underlying signaling mechanism remains unclear. Here, we investigated the functional impact of Ser93 phosphorylation using phosphomimetic (TSP1S93D) and phosphonull (TSP1S93A) mutants. Endothelial cell (EC) migration was analyzed using scratch assay and electric cell-substrate impedance sensing. Activation of key pathways (Akt, p38, ERK, and FAK) was analyzed by immunoblotting. TSP1 secretion was quantified by ELISA. Downstream effects on smooth muscle cells were examined by Western blot using conditioned media of endothelial cells. Expression of TSP1S93D significantly impaired endothelial migration and wound closure, associated with reduced phosphorylation of FAK and paxillin. Upstream of FAK signaling, TSP1S93D showed enhanced binding to integrin β1 and promoted its clustering. In contrast, TSP1S93D stimulated smooth muscle cell proliferation, migration, cytoskeletal remodeling, and phenotypic switching toward a synthetic, pro-inflammatory state characterized by elevated marker protein expression. Together, these findings demonstrate that the impaired angiogenic properties induced by TSP1S93D result from the modulation of integrin β1-FAK pathways in ECs, suppressing endothelial motility while promoting smooth muscle activation, suggesting a role in early vascular remodeling and inflammation
Endowment Inequality in Common Pool Resource Games: An Experimental Analysis
No full textThis work addresses whether heterogeneity in player endowments influences investment decisions in common pool resource (CPR) games, shedding light on the relationship between inequality and economic decision making. We explore two theoretical avenues from behavioral economics—linear other-regarding preferences and inequity aversion—and examine the predictions of each with a laboratory experiment. Our experimental results roundly reject the majority of these explanations: in treatments with endowment inequality, high endowment individuals invest more in the common pool resource than low endowment individuals. We discuss these results in the context of the literature on psychological entitlement and positional preferences
Antioxidant N-Acetylcysteine Facilitates Breast Cancer Metas-Tasis via Immunosuppressive Reprogramming of Neutrophils
No full textN-acetylcysteine (NAC) is a widely used antioxidant. It has also attracted significant research interest with regard to its role in cancer progression, although the mechanisms involved remain controversial and poorly understood. Here, using murine models of breast cancer metastasis, we found that systemic NAC administration significantly enhanced pulmonary metastasis without altering primary tumor growth in immunocompetent mice, whereas this metastasis-promoting property of NAC was abrogated in T cell-deficient mice. This phenomenon was not due to the direct effects of NAC on T cells or tumor cells, since in vitro studies indicated that NAC exhibited no impact on the effector functions of T cells or the malignant behavior of breast cancer cells. Mechanistically, we demonstrated that NAC endows neutrophils with an immunosuppressive phenotype, which is characterized by the upregulation of immunosuppressive genes, and these NAC-educated neutrophils potently suppress the activation and effector functions of T cells. Collectively, our study reveals a previously unrecognized role played by NAC in regulating breast cancer lung metastasis by orchestrating the myeloid-dependent suppression of anti-tumor T cell immunity and suggests a need to consider immune-mediated mechanisms when evaluating the systemic impact of antioxidant agents in cancer patients