Search Results (128)

Fanconi anemia (FA) is a rare inherited disorder classically defined by defective DNA interstrand crosslink repair, leading to bone marrow failure and cancer predisposition. Increasing evidence indicates that FA pathophysiology extends beyond genomic instability to include mitochondrial dysfunction, oxidative stress, and impaired antioxidant responses. Across multiple cellular models and patient-derived samples, FA cells display altered mitochondrial bioenergetics, increased reactive oxygen species (ROS) production, and defective activation of redox-adaptive pathways, contributing to cumulative damage to DNA, lipids, and proteins. These alterations are particularly relevant in hematopoietic stem and progenitor cells, where metabolic stress and redox imbalance amplify stem cell exhaustion. Current data support a bidirectional interplay in which mitochondrial dysfunction and oxidative stress act mainly as secondary but amplifying factors of the primary DNA repair defect, establishing pathogenic feedback loops. Preclinical studies suggest that modulation of redox balance and mitochondrial function may improve cellular homeostasis, and early clinical investigations of antioxidant strategies indicate acceptable safety and measurable effects on oxidative biomarkers. However, clinical evidence remains limited and heterogeneous, with uncertain impact on long-term disease progression. Moreover, most mechanistic insights derive from in vitro or patient-derived models, while animal models and longitudinal clinical studies remain insufficient. Overall, a more integrated and translational framework is needed to clarify causality, validate biomarkers, and define the therapeutic potential of targeting metabolic and redox pathways in FA. Full article

Quality evaluation in production systems represents a significant challenge in the manufacturing industry, particularly in environments where expert judgment plays a key role in managing the inherent uncertainty of the production system. This study proposes a fuzzy multicriteria decision-making control chart, termed Fuzzy Decision-Making Control Chart based on AHP-Extent and Triangular Fuzzy Numbers (FDMCC-AHPE). The method integrates expert knowledge through triangular fuzzy numbers and a Fuzzy Analytic Hierarchy Process supported by Extent Analysis, to define fuzzy decision intervals for quality assessment and subsequently perform a structured analysis to classify the product within a control chart framework. In this framework, expert judgments expressed through linguistic evaluations are systematically translated into triangular fuzzy numbers and processed using FAHP–Extent Analysis, allowing the aggregation of subjective assessments within a structured mathematical decision model. The proposed method was validated in a tannery company, specifically in the retanning process. The industrial case study considers both qualitative criteria, such as surface defects and color uniformity, and quantitative process variables that include bath pH, treatment duration, and processing temperature. The results were compared with an empirical expert-based evaluation and a structured expert assessment supported by a multicriteria decision-making method. The findings demonstrate that the FDMCC-AHPE exhibits greater sensitivity in discriminating between quality states under uncertain evaluation conditions, particularly when samples involve complex evaluation conditions. Full article

The electrochemical reduction of carbon dioxide (CO₂RR) into value-added chemicals using renewable electricity is a pivotal strategy for achieving a sustainable carbon cycle. However, this process is plagued by intrinsic challenges, including poor product selectivity, competing hydrogen evolution, and catalyst instability. Metal–organic frameworks (MOFs), with their highly designable periodic structures, atomically dispersed active sites, and tunable pore microenvironments, have emerged as a uniquely versatile platform to address these issues. This review articulates a multi-scale design philosophy that enables precise steering of the CO₂RR pathway. We systematically elaborate on hierarchical tuning strategies, beginning with molecular-scale engineering of active sites (metal nodes and organic ligands) to define intrinsic activity and intermediate binding. This is synergistically integrated with the optimization of electronic structure and charge transport to overcome conductivity bottlenecks, meso-scale modulation of crystal morphology and defects to enhance mass transport and site accessibility, and the construction of heterogeneous interfaces for tandem catalysis and synergistic effects. Through this coherent, cross-scale design framework, MOF-based catalysts demonstrate exceptional capability in the precise control of reaction pathways, leading to remarkably selective synthesis of target high-value products, from C₁ compounds (CO, HCOOH, CH₄, CH₃OH) to C₂⁺ species (C₂H₄, C₂H₅OH) and urea. Finally, we outline future directions centered on dynamic mechanistic understanding, electrode engineering for industrial current densities, and stability enhancement, thereby providing a comprehensive material design guideline to advance CO₂RR technology. This work positions MOFs as a quintessential tunable catalytic platform for the sustainable conversion of CO₂. Full article

The corrosion behavior and mechanism of Q235 steel during a two-year exposure to the subtropical marine atmospheric environment on an offshore platform in the East China Sea were investigated in this study. Methods including corrosion weight loss measurement, macro/micro-morphological observation (using a digital camera, SEM, and 3D-CLSM), composition analysis (XRD and XPS), and electrochemical tests (EIS and Tafel polarization curves) were employed to systematically examine corrosion kinetics, rust layer evolution, and electrochemical performance. The results indicated that the corrosion rate of Q235 steel initially increased and subsequently decreased with prolonged exposure, with the atmospheric corrosivity reaching CX level as defined (according to the ISO 9223 standard). The corrosion products transitioned from an early-stage rust layer predominantly consisting of γ-FeOOH to a later-stage layer primarily composed of α-FeOOH and Fe₃O₄. XPS analyses revealed that both the α*/γ* ratio and the Fe(II)/Fe(III) ratio increased over time, demonstrating a progressive improvement in the protective properties of the rust layer. The polarization resistance of the rust layer gradually rose, while the corrosion current density declined significantly, further confirming the enhanced stability and protective performance of the rust layer following long-term exposure. Chloride ions accumulated at defects within the rust layer, inducing local acidification, which played a key role in promoting the initiation and propagation of pitting corrosion. This study elucidated the corrosion behavior and mechanism of Q235 steel in the marine atmospheric environment of the East China Sea. Despite the increase in exposure time from 6 to 24 months, during which the electrochemical stability of the rust layer enhanced over time, it failed to prevent the initiation and propagation of severe localized corrosion—an issue of critical importance for load-bearing structures. The findings provide important theoretical and data support for service-life assessment and corrosion protection design of offshore photovoltaic steel structures. Full article

Background: Bone tissue engineering has emerged as a promising technique for treating bone defects in large bones. Recent methods have enabled scaffold designs based on predefined microstructures or mechanical behavior patterns, including porosity-graded scaffolds adaptable to heterogeneous load states. However, there is no consensus on the optimal scaffold design strategy, which is sometimes chosen based on the intact bone or results from computational or in vivo experiments. Objective: This work proposes the design of graded-porosity triply periodic minimal surface (TPMS) scaffolds that mimic the mechanical environment within a bone transport callus at the peak of bone tissue production, according to in vivo load measurements. Methods: Finite element models based on computational tomography scans were used to define the strain field of the callus at the peak of bone tissue production. The developed scaffold models were evaluated through finite element simulation. Results: The callus simulations reported that the period in which maximum woven bone tissue production was achieved corresponds to the period of maximum axial strain. The graded-porosity scaffolds simulated demonstrated their ability to replicate this strain field along the callus. The microstructural parameters and strain environment of the proposed graded-porosity scaffolds were consistent with finding from studies assessing the influence of different microstructural parameters or strain conditions on bone ingrown within scaffolds. Conclusions: The proposed approach—designing graded-porosity scaffolds based on the callus strain field at the peak of bone tissue production—proved to be appropriate and may help improve future clinical applications. Full article

The geometric variability of industrial components represents a persistent challenge in robotic arc welding, particularly in high-volume manufacturing environments where parts are positioned in fixtures based on nominal CAD assumptions. Even moderate deviations in dimensions or seating conditions can lead to weld defects, rework, and reduced process capability when conventional offline programming is employed. This paper presents an applied industrial workflow for adaptive robotic welding trajectory correction that integrates full-field 3D optical metrology with a data-driven deep reinforcement learning (DRL) model. Prior to welding, each component is scanned using a structured-light 3D system, and critical geometric deviations are extracted relative to the nominal CAD model. These deviations define a compact state representation that is mapped, via a trained DRL agent, to corrective translational and rotational adjustments of the welding trajectory. Importantly, all trajectory corrections are computed offline, ensuring compatibility with standard industrial robot controllers and avoiding real-time computational overheads. The proposed approach is validated using real production data from an industrial batch of 5000 components characterized by significant dimensional variability and limited process capability. Experimental results demonstrate a reduction in welding defects exceeding 90%, elimination of rework associated with improper part positioning, and an improvement of the overall process performance to a sigma level of 5.219. The results show that combining 3D optical metrology with learning-based trajectory adaptation enables robust compensation of part-level geometric deviations without mechanical fixture modifications. The proposed method provides a practical and scalable solution for improving welding quality in manufacturing environments affected by upstream variability and imperfect part positioning. Full article

Cancer metabolism is a cornerstone of tumor biology, characterized by profound alterations in cellular energy production and biosynthetic pathways that drive malignancy. The seminal discovery of the “Warburg effect”, the preference of cancer cells for aerobic glycolysis even under oxygen-rich conditions, provided the first major insight into this field. Historically, this observation was attributed to defective mitochondria, but modern research has revealed a far more complex picture of metabolic reprogramming that is actively driven by oncogenes, tumor suppressor genes, and the tumor microenvironment (TME). This review advances a unifying framework for understanding cancer metabolism as a dynamic ecosystem defined by three interconnected adaptations: metabolic plasticity, oncometabolite-driven epigenetic remodeling, and immune-metabolic crosstalk. These adaptations extend beyond glycolysis to encompass glutamine metabolism, lipid synthesis, amino acid utilization, and mitochondrial dynamics, all coordinated to fuel rapid proliferation, promote survival, and enable metastasis. By examining the drivers, consequences, and therapeutic barriers within this framework, we highlight emerging strategies for precision intervention. Although understanding the mechanistic basis of these pathways has unveiled new therapeutic avenues, clinical translation has been limited by metabolic redundancy, microenvironmental buffering, and patient heterogeneity. Strategies such as metabolic inhibitors, dietary interventions, and immuno-metabolic combinations offer promising prospects for disrupting tumor growth when guided by biomarker-driven patient selection and emerging technologies, including spatial metabolomics and AI-driven network modeling. Full article

β-thalassemia and sickle cell disease are two inherited hematological diseases due to defective hemoglobin synthesis or to the production of hemoglobin with altered properties. These two conditions have prolonged survival with modern support therapies, albeit life-long, complex, expensive and resource-consuming. Studies carried out in the last three decades have shown that allogeneic hematopoietic stem cell transplantation (allo-HSCT) and gene therapy may offer a curative approach for these diseases. Allo-HSCT should be performed early in life to reduce disease-related complications like irreversible tissue damage due to iron overload in patients with transfusion-dependent β-thalassemia (TDT) and systemic vasculopathy in patients with sickle cell disease (SCD). HSCTs from a matched-sibling donor or a matched-unrelated donor represent the best therapeutic option; however, haploidentical HSCT in both TDT and SCD is now increasingly performed as a valuable and viable option for a larger number of these patients. An alternative curative strategy is based on gene therapy. These curative approaches, particularly those of gene therapy, are available only in a part of the world. Gene therapy diffusion is strongly limited by its high technological and infrastructure requirements and its very high cost. Criteria must be defined for the optimal selection of TDT and SCD patients for allo-HSCT or gene therapy. Full article

The field of developing effective catalysts for heterogeneous catalysis has recently focused on controlling the structures of catalysts themselves to optimise the density and energy of crystal lattice defects. This can significantly influence catalytic activity in terms of both reaction rates and reaction mechanisms, and thus the selective production of desired substances as well. In many cases, these crystal lattice defects manifest themselves as so-called electron traps (ETs) and thus significantly influence charge transfer between the catalyst and reactants. ETs provide the missing electronic link between atomic-scale defects and macroscopic performance in heterogeneous catalysis. Therefore, the importance of ETs for catalysis is particularly evident in areas where charge transfer plays a fundamental role in the reaction mechanism, such as photocatalysis and electrocatalysis. In the field of thermally initiated reactions, the importance of ETs in heterogeneous catalysis has not yet been fully appreciated. However, several studies have already addressed the importance of ETs for this type of reaction. This review consolidates and extends the concept of ETs to purely thermal-initiated reactions, with a focus on CO₂ hydrogenation using typical transition metal catalysts. Firstly, in this review, ETs are defined as band gap states associated with internal and external defects, and their depth, density, spatial location, and dynamics are then coupled with key steps in thermocatalytic cycles, including charge storage/release, reactant activation, intermediate stabilisation, and redox turnover. Secondly, electron trap detection is reviewed based on advanced spectroscopic techniques, including reversed double-beam photoacoustic spectroscopy (RDB-PAS), thermally stimulated current (TSC), deep-level transient spectroscopy (DLTS), thermoluminescence (TL), electron paramagnetic resonance (EPR), and photoluminescence (PL), highlighting how each method describes trap energetics and populations under realistic operating conditions. Finally, case studies on the application of metal oxides and supported metals are discussed, as these are typical catalysts for the reaction mentioned above. This review highlights how oxygen vacancies (OVs), polarons, and metal–support interfacial sites act as robust electron reservoirs, lowering the barriers for CO₂ activation and hydrogenation. By reframing thermocatalysts through the lens of ET chemistry, this review identifies ETs as actionable targets for the rational design of next-generation materials for CO₂ hydrogenation and related high-temperature transformations. Full article

The article considers issues related to improving the surface characteristics of titanium Gr2 using one of the lightest, cheapest and most ecological methods—electrospark deposition with low pulse energy and with ultradisperse electrodes TiB₂-TiAl with nanosized additives of NbC and ZrO₂. Using profilometric, metallographic, XRD, SEM and EDS methods, the change in the geometric characteristics, composition, structure, micro and nanohardness of the coatings as a function of the electrical parameters of the ESD regime has been studied. The results show that the use of TiB₂-TiAl electrodes and low pulse energy allows the formation of dense, continuous and uniform coatings that demonstrate a significant reduction in roughness, inherent irregularities and structural defects of electrospark coatings. Coatings with minimal defects, with crystalline–amorphous structures, with newly formed intermetallic and wear-resistant double and triple phases of the type AlTi₃, TiAl₃, TiB, TiN_0.3, Al₂O₃, AlB₂, TiC_0.3N_0.7, Ti_3.2B_1.6N_2.4, Al_2.86O_3.45N_0.55 have been obtained. Possibilities have been found for controlling and obtaining specific values for the roughness and thickness of coatings in the ranges Ra = 1.5–3.2 µm and δ = 8–19.5 µm, respectively. The electrical parameters of the modes ensure the production of coatings with previously known thickness and roughness, with increased microhardness up to 13 GPa, with the maximum possible content of deliberately synthesized high-hard phases and with ultra-fine-grained structures have been defined. Full article

Alkali-activated cementitious materials are environmentally friendly alternatives to traditional cement. The structure of their core product, sodium aluminosilicate hydrate (N-A-S-H) gel, is regulated by the silicon-to-aluminum (Si/Al) ratio; however, the atomic-scale mechanism underlying this influence remains unclear. Integrating reactive force field molecular dynamics simulations and experiments, this study systematically reveals the regulation mechanism of the Si/Al ratio (1.0–2.0) on the microstructure and macroscopic properties of N-A-S-H gels. Starting from well-defined PS and PSS oligomers, the simulation results demonstrate that the Si/Al ratio governs the polymerization pathway, aluminum coordination environment (especially the content of pentacoordinate aluminum), and evolution of nanoporosity. When the Si/Al ratio is approximately 1.8, the system exhibits the highest silicate polymerization degree, lowest nanoporosity, and densest three-dimensional (3D) network structure; deviation from this ratio leads to structural degradation due to charge imbalance or excessive polymerization. These computational findings are validated by experiments on fly ash-based geopolymers: the material achieves the highest compressive strength at a Si/Al ratio of 1.8. The consistency between simulations and experiments collectively reveals a cross-scale action mechanism: the Si/Al ratio determines the macroscopic mechanical properties by regulating the nanoscale packing density and defect distribution of the gel. This study provides critical atomic-scale insights for the rational design of high-performance geopolymers. Full article

Multi-material Directed Energy Deposition (DED) Additive Manufacturing (AM) processes enable the integration of different material properties into a single structure, addressing the requirements of various applications and working environments. Laser-based Directed Energy Deposition (DED-LB) has been employed in the past for surface coatings as well as for the repair and repurposing of high-value industrial components, with the goal of extending product lifetime without relying on expensive and time-consuming manufacturing from scratch. While powder DED-LB has traditionally been used for multi-material AM, the more resource-efficient and cost-effective wire DED-LB process is now being explored as a solution for creating hybrid materials. This work focuses on the critical aspects of implementing multi-material DED-LB, specifically defining an optimal operating process window that ensures the best quality and performance of the final parts. By investigating the possibility of combining stainless steel and nickel-based alloys, this study seeks to unlock new possibilities for the repair and optimization of naval components, ultimately improving operational efficiency and reducing downtime for critical naval equipment. The analysis of the experimental results has revealed strong compatibility of stainless steel 316 with Inconel 718 and stainless steel 17-4PH, while the gray cast iron forms acceptable fusion only with stainless steel 17-4PH. Finally, during the experimental phase, substrate pre-heating and process monitoring with thermocouples will be employed to manage and assess heat distribution in the working area, ensuring defect-free material joining. Full article

This work presents a data-driven framework for early detection of polymer melt instability in industrial injection moulding using Long Short-Term Memory (LSTM) time-series models. The study uses six months of continuous production data comprising approximately 280,000 injection cycles collected from a fully operational thermoplastic injection line. Because melt behaviour evolves gradually and conventional threshold-based monitoring often fails to capture these transitions, the proposed approach models temporal patterns in torque, pressure, temperature, and rheology to identify drift conditions that precede quality degradation. A physically informed labelling strategy enables supervised learning even with sparse defect annotations by defining volatile zones as short time windows preceding operator-identified non-conforming parts, allowing the model to recognise instability windows minutes before defects emerge. The framework is designed for deployment on standard machine signals without requiring additional sensors, supporting proactive process adjustments, improved stability, and reduced scrap in injection moulding environments. These findings demonstrate the potential of temporal deep-learning models to enhance real-time monitoring and contribute to more robust and adaptive manufacturing operations. Full article

Background/Objectives: Sepsis-associated liver injury (SALI) is a critical and often early complication of sepsis, defined by distinct hyper-inflammatory and immunosuppressive phases that shape patient phenotypes. Methods: Characterizing these phases establishes a foundation for immunomodulation strategies tailored to individual immune responses, as discussed subsequently. Results: The initial inflammatory response activates pathways such as NF-κB and the NLRP3 inflammasome, leading to a cytokine storm that damages hepatocytes and is frequently associated with higher SOFA scores and a higher risk of 28-day mortality. Kupffer cells and infiltrating neutrophils exacerbate hepatic injury by releasing proinflammatory cytokines and reactive oxygen species, thereby causing cellular damage and prolonging ICU stays. During the subsequent immunosuppressive phase, impaired infection control and tissue repair can result in recurrent hospital-acquired infections and a poorer prognosis. Concurrently, hepatocytes undergo significant metabolic disturbances, notably impaired fatty acid oxidation due to downregulation of transcription factors such as PPARα and HNF4α. This metabolic alteration corresponds with worsening liver function tests, which may reflect the severity of liver failure in clinical practice. Mitochondrial dysfunction, driven by oxidative stress and defective autophagic quality control, impairs cellular energy production and induces hepatocyte death, which is closely linked to declining liver function and increased mortality. The gut-liver axis plays a central role in SALI pathogenesis, as sepsis-induced gut dysbiosis and increased intestinal permeability allow bacterial products, including lipopolysaccharides, to enter the portal circulation and further inflame the liver. This process is associated with sepsis-related liver failure and greater reliance on vasopressor support. Protective microbial metabolites, such as indole-3-propionic acid (IPA), decrease significantly during sepsis, removing key anti-inflammatory signals and potentially prolonging recovery. Clinically, SALI most commonly presents as septic cholestasis with elevated bilirubin and mild transaminase changes, although conventional liver function tests are insufficiently sensitive for early detection. Novel biomarkers, including protein panels and non-coding RNAs, as well as dynamic liver function tests such as LiMAx (currently in phase II diagnostics) and ICG-PDR, offer promise for improved diagnosis and prognostication. Specifying the developmental stage of these biomarkers, such as identifying LiMAx as phase II, informs investment priorities and translational readiness. Current management is primarily supportive, emphasizing infection control and organ support. Investigational therapies include immunomodulation tailored to immune phenotypes, metabolic and mitochondrial-targeted agents such as pemafibrate and dichloroacetate, and interventions to restore gut microbiota balance, including probiotics and fecal microbiota transplantation. However, translational challenges remain due to limitations of animal models and patient heterogeneity. Conclusion: Future research should focus on developing representative models, validating biomarkers, and conducting clinical trials to enable personalized therapies that modulate inflammation, restore metabolism, and repair the gut-liver axis, with the goal of improving outcomes in SALI. Full article

Hiring assembly line workers is often time- and resource-demanding. Following the call for more effective hiring practices, this article describes the design, development, and implementation of an ‘AI-empowered recruitment model’, an emerging technology in hiring employees. The raw data for model building were gathered from the assembly line workers and their managers. The dataset comprised two parts. Part-1 data were the occupational codes and personality parameters of the top performers (provided by the performers), whereas Part-2 data were the employability and fitness parameters of the top performers (rated by the managers of the performers). Top performers were defined as the employees who had the highest output of products with the lowest defect rate. Through the use of repetitive data-matching algorithms, the model gradually learned and identified the signs (patterns) of top performers. After cross-validation and external testing, the model became established. The model was then applied to the employee recruitment practice, in which the model achieved its purpose by selecting the best-fit candidates from the pool of applicants within minutes. The AI-empowered recruitment model saved organizational resources and expenses. As there was no use of human labor, administrative delays and errors were minimized, thus improving the efficacy of the hiring practice. Limitations and suggestions for improvement were addressed. Full article