Search Results (10,346)

Background/Objectives: Non-Hodgkin lymphoma (NHL) is a group of blood cancers that arise in the lymphatic nodes and other tissues after an injury to the DNA of B/T lineage and NK lymphocytes. Recently, we reported that incomptine A (IA) has in vivo antilymphoma properties. This research aimed to evaluate the effects of IA in the treatment of NHL using antilymphoma activity, Tandem Mass Tag (TMT), and bioinformatics approaches. Methods: The antilymphoma activity of IA was tested on male Balb/c mice inoculated with U-937 cells. Also, TMT, gene ontology enrichment, Reactome pathway, Kyoto Encyclopedia of Gene and Genomes pathway, molecular docking, toxicoinformatic, and pharmaceutical analyses were performed. Results: By TMT analysis of the altered levels of proteins present in the lymph nodes of Balb/c mice with NHL and treated with IA, we identified 106 significantly differentially expressed proteins (DEPs), including Il1rap, Ifi44, Timd4, Apoa4, and Fabp3 as well as Myh3, Eno 2, and H4c11. Among these, the Fhl1 result was the most important cluster altered and a potential core target of IA for the treatment of NHL. Network pharmacology studies have revealed that DEPs are associated with processes such as muscle contraction, glycolysis, hemostasis, epigenetic regulation of gene expression, transport of small molecules, neutrophil extracellular trap formation, adrenergic signaling in cardiomyocytes, systemic lupus erythematosus, alcoholism, and platelet activation, signaling, and aggregation. Computational studies revealed strong binding affinities with six proteins associated with cancer, positive pharmacokinetic properties, and no toxicity. Conclusions: Our contribution suggests that IA may be a compound with potential therapeutic effects against NHL. Full article

Background: The Space Omics and Medical Atlas (SOMA) is an extensive database containing gene expression information from samples collected during the short-duration Inspiration4 spaceflight mission in 2021. Given our prior understanding of the genetic basis for cardiovascular diseases in spaceflight, including orthostatic intolerance and cardiac deconditioning, we aimed to characterize changes in differential gene expression among astronauts using SOMA-derived data and curated cardiovascular pathways. Methods: Using the KEGG 2021 database, we curated a list of genes related to cardiovascular adaptations in spaceflight, focusing on pathways such as fluid shear stress and atherosclerosis, lipid metabolism, arrhythmogenic ventricular hypertrophy, and cardiac muscle contraction. Genes were cross-matched to spaceflight-relevant datasets from the Open Science Data Repository (OSDR). Differential expression analysis was performed using DESeq2 (v1.40.2, R) with normalization by median-of-ratios, paired pre-/post-flight covariates, and log2 fold change shrinkage using apeglm. Differentially expressed genes (DEGs) were defined as |log2FC| ≥ 1 and FDR < 0.05 (Benjamini–Hochberg correction). Module score analyses were conducted across SOMA cell types to confirm conserved cardiac adaptation genes. Results: A total of 185 spaceflight-relevant genes were analyzed. Statistically significant changes were observed in immune-related cardiovascular pathways, particularly within monocytes and T cells. Persistent upregulation of arrhythmogenic genes such as GJA1 was noted at post-flight day 82. WikiPathways enrichment revealed additional pathways, including focal adhesion, insulin signaling, and heart development. Conclusions: Short-duration spaceflight induces significant gene expression changes that are relevant to cardiovascular disease risk. These changes are mediated largely through immune signaling and transcriptional regulation in peripheral blood mononuclear cells. Findings highlight the need for tailored countermeasures and longitudinal monitoring in future long-duration missions. Full article

In the ever-evolving world full of technologies, blockchain proves itself to be the most secure way of dealing with tampering of data. This paper proposes an innovative model for tracking employees within facilities using RFID, IoT devices and blockchain technology implemented on the Hyperledger Fabric platform. The blockchain system supports a secure and tamper-proof recording of employee movement because it keeps the data in a decentralized system. Smart contracts automate activities like control access, generate alerts and create audit trails without the need for centralized management. This implementation shows a high level of security and efficiency, making it a good approach to improve monitoring and compliance within organizations. Full article

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems. Full article

Background/Objectives: Archery is a technical sport involving repetitive and asymmetrical movements that requires trunk stability to enable good performance of the upper extremities. Being an asymmetrical sport, imbalances between sides might appear in the abdominal and back muscles. To assess trunk muscle function and symmetry in young competitive archers. Methods: Analyzing pre-season screening evaluation tests from medical files. This included an ultrasound examination of back and abdominal muscles (transverse abdominus and internal oblique) during rest and contraction and trunk muscle clinical strength tests. Results: Data on 15 elite archery athletes (mean age 17.2 (±2.7) years) were included. No athletes reported low back pain. No differences were found between the dominant and non-dominant sides in all outcome measurements (absolute thickness and percentage difference). Internal oblique muscle thickness during rest and contraction for the dominant side was higher in males compared with females (p < 0.05). The back muscles were more symmetrical than the abdominal muscles. Conclusions: Despite the asymmetrical functional demands of sport archery, young athletes displayed trunk muscle symmetry, particularly in their back muscles. While some variability in abdominal muscle asymmetry was observed, these differences were not statistically significant. Full article

This paper is concerned with the problem of a full formal dynamic linearized model-free adaptive control scheme based on pattern class variable (P-FFDL-MFAC) for a class of unknown non-affine nonlinear discrete-time systems. The concept of pattern class variable is defined as dynamic operating variables rather than state variables or output variables. The pattern classes is utilized as the system output conditions, and the purpose of the control is to ensure that the system output belongs to a certain pattern class or some desired pattern classes. The scheme of P-FFDL-MFAC mainly consists of an improved tracking control law, a bias estimation algorithm, and a pseudo-gradient vector estimation algorithm. Furthermore, based on the contraction mapping theorem, the bounded convergence of tracking error has been proved. Finally, numerical examples and the actual sintering process data are used, respectively, to verify the effectiveness of the proposed design techniques and are compared with the traditional MFAC method. The results are better than the traditional method. Full article

The United States produces approximately 500 million tons of asphalt mixtures annually, while generating vast amounts of waste materials that could be repurposed for sustainable infrastructure. Each year, 1.4 billion gallons of lubricating oils are available for reuse and recycling. Additionally, 280 million tires are discarded, contributing to significant environmental challenges. Given the critical role of the roadway network in economic growth, mobility, and infrastructure sustainability, there is a pressing need for innovative material solutions that integrate recycled materials without compromising performance. This study introduces a Rubberized-Aerogel Composite (RaC), a novel asphalt binder modifier combining crumb rubber, recycled oil, and a silica-based aerogel to enhance the sustainability and durability of asphalt pavements. The research methodology involved blending the RaC with the PG70-10 asphalt binder at a 5:1 ratio and conducting comprehensive laboratory tests on binders and mixtures, including rheology, thermal conductivity (TC), specific heat capacity (Cp), the Hamburg Wheel-Tracking Test (HWTT), and indirect tensile strength (IDT). Pavement performance was simulated using AASHTOWare Pavement ME under hot and cold climates with thin and thick pavement structures. Results showed that RaC-modified binders reduced thermal conductivity by up to 30% and increased specific heat capacity by 15%, improving thermal stability. RaC mixtures exhibited a 50% reduction in rut depth in the HWTT and lower thermal expansion/contraction coefficients. Pavement ME simulations predicted up to 40% less permanent deformation and 60% reduced thermal cracking for RaC mixtures compared to the controls. RaC enhances pavement lifespan, reduces maintenance costs, and promotes environmental sustainability by repurposing waste materials, offering a scalable solution for resilient infrastructure. Full article

Rubella is typically a mild viral illness, but it can lead to severe complications when contracted during pregnancy, such as pregnancy loss or developmental defects in the fetus (congenital rubella syndrome). Therefore, it is crucial to develop and maintain protective immunity in women of childbearing age. In this study, we assessed the transcriptional factors associated with rubella-specific immune outcomes (IgG binding antibody and avidity, neutralizing antibody, and memory B cell ELISpot response) following a third MMR vaccine dose in women of reproductive age to identify key factors/signatures impacting the immune response. We identified baseline (Day 0) and differentially expressed (Day 28–Day 0) genes associated with several RV-specific immune outcomes, including the transferrin receptor 2 (TFR2), which is an important factor regulating iron homeostasis and macrophage functional activity, and a close functional homolog of TFR1, the cellular receptor of the New World hemorrhagic fever arenaviruses. We also identified enriched KEGG pathways, “cell adhesion molecules”, “antigen processing and presentation”, “natural killer cell-mediated cytotoxicity”, and “immune network for IgA production”, relevant to immune response priming and immune activation to be associated with RV-specific immune outcomes. This study provides novel insights into potential biomarkers of rubella-specific immunity in women of childbearing age. Full article

With the rapid development of artificial intelligence, multi-party collaboration based on data sharing has become an inevitable trend. However, in practical applications, shared data often originate from multiple providers. Therefore, achieving secure and efficient data sharing while protecting the rights and interests of each data provider is a key challenge currently faced. Existing access control methods have the following shortcomings in multi-owner data scenarios. Most methods rely on centralized management, which makes it difficult to solve conflicts caused by inconsistent permission policies among multiple owners. There are problems such as poor consistency of permission management, low security, and lack of protection for the autonomous will of each owner. To this end, our paper proposes a fine-grained decentralized autonomous access control scheme based on blockchain, which includes three core stages: formulation, deployment, and execution of access control policies. In the access control policy formulation stage, the scheme constructs a multi-owner data policy matrix and introduces a benefit function based on a Stackelberg game to balance conflicting attributes to form a unified access policy. Secondly, in the access control policy deployment stage based on smart contracts, all data owners vote on the access control policy by calculating their own benefits to achieve a consensus on joint decision-making on the policy. Finally, in the policy execution and joint authorization phase, a decentralized authorization method based on threshold passwords is used to distribute access keys to each owner, ensuring that data is only granted after receiving authorization from a sufficient number of owners, thereby ensuring the ultimate control of each owner and the fine-grained access control. Finally, we verified the feasibility of the solution through case analysis and experiments. Full article

Synergizing soil quality improvement and greening for increased yields are essential to ensuring grain security and developing sustainable agriculture, which has become a key issue in agricultural cultivation. This study considers a contract farming supply chain composed of a risk-averse farmer and a risk-neutral firm making green and efficient technology (GET) investments, which refers to the use of technology monitoring to achieve fertilizer reduction and yield increases with yield uncertainty. Based on the CvaR (Conditional value at Risk) criterion, the Stackelberg game method is applied to construct a two-level supply chain model and analyze different cooperation mechanisms. The results show that when the wholesale price is moderate, both sides will choose the cooperative mechanism of cost sharing to invest in technology; the uncertainty of yield and the degree of risk aversion have a negative impact on the agricultural inputs and GET investment, and when yield fluctuates greatly, the farmer invests in GET to make higher utility but lowers profits for the firm and supply chain. This study provides a theoretical basis for GET investment decisions in agricultural supply chains under yield uncertainty and has important practical value for promoting sustainable agricultural development and optimizing supply chain cooperation mechanisms. Full article

Welding, as a critical process for achieving permanent material joining through localized heating or pressure, is extensively applied in mechanical manufacturing and transportation industries, significantly enhancing the assembly efficiency of complex structures. However, the associated localized high temperatures and rapid cooling often induce uneven thermal expansion and contraction, leading to complex stress evolution and residual stress distributions that compromise dimensional accuracy and structural integrity. In this study, we propose a combined heat source model based on the geometric characteristics of the weld pool to simulate the arc welding process of large flange shafts made of Fe-C-Mn-Cr low-alloy medium carbon steel. Simulations were performed under different welding durations and shaft diameters, and the model was validated through experimental welding tests. The results demonstrate that the proposed model accurately predicts weld pool geometry (depth error of only 2.2%) and temperature field evolution. Meanwhile, experimental and simulated deformations are presented with 95% confidence intervals (95% CI), showing good agreement. Residual stresses were primarily concentrated in the weld and heat-affected zones, exhibiting a typical “increase–steady peak–decrease” distribution along the welding direction. A welding duration of 90 s effectively reduced residual stress differentials perpendicular to the welding direction by 19%, making it more suitable for medium carbon steel components of this scale. The close agreement between simulation and experimental data verifies the model’s reliability and indicates its potential applicability to the welding simulation of other large-scale critical components, thereby providing theoretical support for process optimization. Full article

The assessment of upper limb (UL) movement patterns plays a critical role in the rehabilitation of individuals with motor impairments resulting from neuromotor disorders, which significantly affect essential activities of daily living (ADLs) such as drinking and eating. However, conventional clinical evaluation methods often lack objective and quantitative insights into the biomechanics of movement. To enable accurate identification of pathological patterns, it is first necessary to establish normative biomechanical and electrophysiological benchmarks in healthy individuals. In this study, a previously developed, low-cost, wearable, and portable prototype device was employed to objectively assess UL movement. The system, specifically designed for clinical applicability, integrates surface electromyography (EMG) sensors and an inertial measurement unit (IMU) to capture muscle activity and kinematic data, respectively. Thirty healthy participants were recruited to perform standardized drinking and eating tasks. The analysis focused on characterizing muscle activation patterns and joint range of motion during different task phases. Results revealed consistent variations in muscle contraction and joint kinematics, allowing the identification of distinct activation profiles for key shoulder muscles. The findings contribute to the establishment of a normative dataset that can serve as a reference for the assessment of clinical populations. Such data are essential for informing rehabilitation strategies and developing predictive models of UL function during ADLs in individuals with neuromotor disorders. Full article

This research paper delves into the application of optimality results in orthogonal fuzzy metric spaces to demonstrate the existence and uniqueness of solutions of nonlinear differential equations with boundary conditions and nonlinear integral equations, emphasizing the importance of orthogonal fuzzy metric spaces in extending fixed-point theory. Through introducing this innovative concept, the study provides a theoretical framework for analyzing mappings in diverse scenarios. In this study, we introduce the concept of best proximity point (BPP) within the framework of orthogonal fuzzy metric spaces by employing orthogonal fuzzy proximal contractive mappings. Moreover, this research explores the implications of the established results, considering both self-mappings and non-self mappings that share the same parameter set. Additionally, some examples are provided to illustrate the practical relevance of the proven results and consequences in various mathematical contexts. The findings of this study can open up avenues for further exploration and application in solving real-world problems. Full article

This study employs Geographic Information Systems (GIS) combined with multivariate statistical techniques to evaluate soil contamination at two landfill sites in Al-Kharj, Saudi Arabia. A total of 32 soil samples were collected and analyzed for heavy metals and metalloids (HMs) using a range of contamination indices and established soil quality standards. GIS mapping revealed that the Al-Kharj landfill 1 (Kj1) experienced a steady area expansion from 2014 through 2025, while landfill Kj2 expanded from 2014 until 2022, after which its area contracted following the construction of additional facilities. The average values of HMs observed were as follows: Fe (9909 mg/kg), Al (6709 mg/kg), Mn (155.9 mg/kg), Zn (36.4 mg/kg), Cr (24.1 mg/kg), V (22.2 mg/kg), Ni (19.5 mg/kg), Cu (8.20 mg/kg), Pb (7.91 mg/kg), Co (4.32 mg/kg), and As (2.29 mg/kg). Notably, Kj2 exhibited overall higher HM concentrations than Kj1, with particularly elevated levels of Cr, Ni, and Pb. Although most HMs remained within internationally accepted safety limits, only three samples (9.4% of the total) exceeded the WHO threshold for Pb (>30 mg/kg). An analysis using contamination and enrichment factors pointed to increased concentrations of Pb, Zn, and Cr, suggesting localized anthropogenic contributions. Additionally, all samples recorded an ecological risk index (Eri) below 40, and the levels of As, Cr, and Pb consistently stayed under their respective effects range-low (ERL) thresholds, indicating minimal contamination risks. The variations in HM contamination between the sites are likely attributable to differences in the sources of metal inputs and removal processes. These findings highlight the need for continuous monitoring and localized remediation strategies to ensure environmental safety and sustainable landfill management. Full article

Task scheduling in data relay satellite networks (DRSNs) is subject to dynamic disruptions such as resource failures, sudden surges in task demands, and variations in service duration requirements. These disturbances may degrade the performance of pre-established scheduling plans. To improve adaptability and robustness under such uncertainties, this paper presents a dynamic scheduling model for DRSN that integrates comprehensive task constraints and link connectivity requirements. The model aims to maximize overall task utility while minimizing deviations from the original schedule. To efficiently solve this problem, an ensemble heuristic adaptive contract net protocol (EH-ACNP) is developed, which supports dynamic scheduling strategy adaptation and efficient rescheduling through iterative negotiations. Extensive simulation results show that, in scenarios with sudden task surges, the proposed method achieves a 3.1% improvement in yield compared to the state-of-the-art dynamic scheduling algorithm HMCNP, and it also outperforms HMCNP in scenarios involving resource interruptions. Sensitivity analysis further indicates that the algorithm maintains strong robustness when the disposal rate parameter exceeds 0.2. These results highlight the practical potential of the EH-ACNP for dynamic scheduling in complex and uncertain DRSN environments. Full article