Search Results (5,715)

Four-dimensional (4D) printing integrates additive manufacturing with stimuli-responsive materials to fabricate biomedical implants and functional healthcare devices that undergo programmed, time-dependent changes in shape or function. Unlike static 3D-printed constructs, 4D-printed systems can respond to clinically relevant stimuli such as temperature, hydration, pH, light (including near-infrared), magnetic fields, or electrical inputs. These triggers drive defined actuation mechanisms, most commonly thermomechanical shape-memory recovery, swelling-induced morphing, and magnetothermal activation. This review synthesizes the principal material platforms used for biomedical 4D printing, including shape-memory polymers and alloys, hydrogels, liquid-crystal elastomers, and responsive composites, and links material choice to device behavior and translational feasibility. Applications are discussed across self-expanding stents, cardiac occluders, tissue-engineered constructs, implantable drug delivery systems, and adaptive wearables. Key translational challenges include sterilization compatibility, manufacturing reproducibility and quality control, safe stimulus delivery, predictable biodegradation and long-term biocompatibility, and regulatory pathway definition. Full article

Background/Objectives: Prior research suggests that it is possible to improve health outcomes in children with adverse childhood experiences (ACEs) through multi-component interventions that promote protective factors. We designed the Families Implementing Resilient Systems Together (FIRST) study to address the gaps in research on the potential effectiveness of screening for specific ACEs through pediatric practice. Methods: As part of our clinical quality improvement efforts to improve patient care for children impacted by ACEs, we trained a random sample of pediatricians on strategies to promote protective factors and encouraged them to make referrals to community health workers (CHWs) and parenting education resources. This manuscript describes our clinic data on practice changes associated with the FIRST physician training, and our data collection plan for our research study. Results: Physician training resulted in attitudinal shifts and measurable behavioral changes. Trained providers made referrals to CHWs for approximately 5–10% of well-child care visits. The majority (84%) of referrals were for multiple risk factors, most commonly ACEs and socioeconomic concerns. The most common ACEs were parental divorce/separation, parent–child verbal abuse, and caregiver mental health problems. Conclusions: FIRST training improves counseling, education and referrals for children exposed to ACEs. Our research study will evaluate the impact of the FIRST intervention and address important questions about associations between specific ACEs, protective factors, and biomarkers of toxic stress. Full article

Accumulating evidence indicates that a hypomagnetic field (HMF, <5 μT) has a significant impact on various organ systems in animals. However, the cellular and molecular mechanisms underlying these biological effects remain unclear. Understanding the molecular mechanisms underlying mammalian responses to a HMF is crucial for addressing health and safety concerns associated with HMF exposure. In this study, we investigated the changes in intracellular protein phosphorylation under HMF conditions and validated the functional mechanisms by which HMF-induced protein phosphorylation affects cell behavior. We found that U2OS cells can rapidly sense changes in magnetic fields, leading to alterations in protein phosphorylation levels within the cell. The quantitative phosphoproteomics results revealed that the exposure of U2OS cells to the HMF environment for 0.5 h and 3 days resulted in the alteration of 1101 and 1543 phosphosites, respectively. Notably, HMF exposure enhanced the phosphorylation of β-Catenin at Ser552, and this increased phosphorylation-promoted U2OS proliferation and migration. Furthermore, quantitative proteomics showed that exposure to a HMF for 3 days upregulated the expression of LOX and FN1, while the knockdown of LOX or FN1 suppressed the proliferation and migration of the U2OS cells. These results suggest that a HMF enhances U2OS cell proliferation and migration by promoting β-Catenin phosphorylation and upregulating FN1 and LOX expression. Full article

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by heterogeneous behavioral symptoms and systemic comorbidities, including immune and gastrointestinal dysfunctions. Emerging studies suggest that glycosylation—a fundamental post-translational modification regulating cellular communication and immune responses—may play a role in ASD pathophysiology, yet its contribution remains underexplored. Methods: In this study, we developed an integrative transcriptomic and network analysis framework to investigate glycosylation-related gene expression changes and their functional associations in ASD. Using publicly available datasets from bulk and single-cell RNA sequencing of brain and blood tissues, we focused on four prior-knowledge gene subsets: glycogenes, extracellular matrix glycoproteins, immune response genes, and autism risk genes. Results: Differential expression and pathway enrichment analyses revealed consistent dysregulation of glycosylation pathways, including mucin-type O-glycan biosynthesis, glycosaminoglycan metabolism, GPI-anchor formation, and sialylation, across ASD tissues. These transcriptional changes were functionally linked to altered immune signaling (e.g., IL-17, Toll-like receptor, and complement pathways) and synaptic development pathways, forming a distinct glyco-immune axis. Network analysis identified key glycogenes such as GALNT10, NEU1, LMAN2L, and CHST1 as central molecular nodes, interacting with immune and neuronal regulators. Linkage disequilibrium analysis further revealed ASD-associated SNPs influencing the expression of these glycogenes in both blood and brain tissues. Conclusions: Together, these findings support a model in which disrupted glycosylation contributes to ASD pathophysiology by mediating immune dysregulation and altered neuronal connectivity. This study offers a systems-level framework to understand the molecular complexity of ASD and highlights glycogenes as potential biomarkers and targets for future therapeutic exploration. Full article

To meet the high-fidelity testing environment requirements for autonomous driving system development, driving simulators are gradually evolving from tools that “only provide scenes and interaction interfaces” into integrated verification platforms for autonomous driving capabilities. These simulators, in particular, need to feature testable and scalable decision-making modules. However, the autonomous driving functions in existing driving simulators mostly rely on rule-based or simplified model approaches, which are inadequate for depicting the complex interactions in real-world traffic and fail to meet the personalized decision-making needs under various driving styles. To address these challenges, this paper designs and implements a trajectory data-driven personalized autonomous driving decision system, using drone aerial imagery as the core data source to provide realistic background traffic flow and human-like decision-making capabilities. The proposed system can be interpreted as an integrated decision–planning–control framework deployed within a high-fidelity driving simulation platform. It consists of a driving style classification module based on drone trajectory data, a personalized decision module integrating inverse reinforcement learning and dynamic game theory, and a planning and control module. First, a natural driving database is built using 4997 real vehicle trajectories, and prior features of different driving styles are extracted through trajectory feature engineering and an improved K-means++ method. Based on this, a personalized decision-making framework that combines dynamic game theory and maximum entropy inverse reinforcement learning is proposed, aiming to learn the preference weights of different driving styles in terms of safety, comfort, and efficiency. Furthermore, the Dueling Network Architecture (DuDQN) is used to generate human-like lane-changing strategies. Subsequently, a real-time closed-loop execution of personalized decisions in the simulation platform is achieved through fifth-order polynomial trajectory planning, lateral Linear Quadratic Regulator (LQR) control, and longitudinal cascade Proportional–Integral–Derivative (PID) control. Experimental results show that the personalized decision model trained with drone data can realistically reproduce vehicle decision-making behaviors in natural traffic flows within the simulation environment and generate autonomous driving strategies that are highly consistent with different driving styles. This significantly enhances the humanization and personalization capabilities of the autonomous driving module in the driving simulator. Full article

Energy efficiency improvement represents a central strategic objective of the European Union (EU), essential for mitigating climate change and facilitating the transition toward a sustainable energy system. In 2023, renewable energy sources generated approximately 46% of the electricity produced in the EU, becoming the dominant component of the regional energy mix. This progress has been supported by coherent public policies, dedicated investment programs, and regulatory mechanisms aimed at accelerating the adoption of sustainable technologies. However, the existing literature highlights a research gap regarding the relationship between the dynamics of the European energy transition, the operational challenges generated by the rapid increase in the share of renewable energy sources, and the potential for energy savings in the residential sector through non-technological interventions. This paper analyzes the structural transformations of the European energy mix, the limitations of energy systems in the context of accelerated renewable energy integration, and the role of behavioral interventions in supporting the stability of the energy system. The study examines the dynamics of residential energy consumption, behavioral determinants of energy use, and the effectiveness of instruments such as information campaigns, real-time feedback, dynamic pricing, and demand response programs. The results indicate that these interventions can reduce peak loads, increase consumption flexibility, and alleviate pressure on energy networks under conditions of variable renewable energy generation. The integration of energy storage systems and the implementation of low-cost behavioral measures can act as complementary instruments for maintaining the dynamic stability of the energy system and for achieving the EU’s sustainability and climate neutrality objectives. Full article

Thermal management is critical for maintaining the safety and performance of lithium-ion batteries. Phase change materials (PCMs) have been widely studied as passive cooling media due to their high latent heat capacity, but major technical challenges remain due to their relatively low thermal conductivity and nanoparticle sedimentation in composite systems. In this work, a composite phase change material (PCM) consisting of paraffin wax, a microencapsulated phase change material (MicroPCM 28D), and nano carbon black is developed to enhance thermal stability and suppress particle sedimentation through increased viscosity of the PCM matrix. Five capsule geometries fabricated by fused filament fabrication (FFF) 3D printing are experimentally investigated under airflow velocities ranging from 0 to 10 m s⁻¹. Wind tunnel experiments with infrared thermography are used to evaluate the thermal response of the PCM capsules. The results show that airflow velocity and capsule geometry strongly influence heat dissipation behavior. Compared with conventional wax composites, the MicroPCM 28D composite capsules reduce peak temperature by approximately 2–4 °C under airflow velocities of 0–10 m/s. These findings provide insights into geometry-regulated convection and stable composite PCM design for lithium-ion battery thermal management systems. Full article

Background: Epilepsy is a chronic neurological disorder characterized by recurrent seizures, complex neurochemical, and metabolic disturbances. Parishin B, a major bioactive component of Gastrodia elata, has shown neuroprotective potential, but its systemic mechanisms remain unclear. Methods: A pentylenetetrazol (PTZ)-induced seizure model in zebrafish larvae was developed and used to evaluate the anti-seizure effects of Parishin B. Behavioral analysis, ELISA-based biochemical assays, integrated untargeted metabolomics with DIA-based proteomics, and qPCR were performed to decipher underlying molecular mechanisms. Results: Parishin B (0.0625–0.25 mg/mL) significantly alleviated PTZ-induced hyperactivity without developmental toxicity. Parishin B restored neurotransmitter balance by increasing GABA, dopamine, and norepinephrine levels while reducing 5-HT. In addition, it suppressed neuroinflammation and enhanced antioxidant capacity. Integrated multi-omics analysis revealed that Parishin B modulated key metabolic pathways, particularly the TCA cycle and lipid metabolism, and reversed the downregulation of ATP-citrate lyase (ACLY). Parishin B was also associated with the regulation of ferroptosis-related pathways, supported by changes in acsl4a and fth1a expression. qPCR results further confirmed the regulation of aclya, unc13c, and GABAergic signaling genes. Conclusions: Parishin B exerts anti-seizure effects through coordinated regulation of neurotransmitter homeostasis, neuroinflammation, and ACLY-associated energy–lipid metabolism, with potential involvement in ferroptosis-related processes. These findings provide molecular insights supporting Parishin B as a promising candidate for epilepsy therapy. Full article

Metabolically-dysfunction-associated steatotic liver disease (MASLD) represents the most common chronic liver disease in the pediatric population, and its prevalence has doubled over the past decade. The etiology is multifactorial, including genomic risk factors, perinatal and developmental or behavioral factors. Still, many cases of MASLD are associated with being overweight and obesity, particularly in children who have poor dietary habits and sedentary lifestyles that contribute to excessive weight gain. Given the progressive and heterogeneous nature of MASLD, early identification of high-risk patients before the development of severe liver disease is a major clinical priority. Recent studies indicate that disorders of amino acid metabolism are closely linked to both obesity and MASLD, reflecting profound alterations in systemic metabolic homeostasis. The reported data sustain significant changes in circulating amino acid profiles, particularly elevated levels of branched-chain amino acids (BCAAs) and aromatic amino acids. These alterations are thought to reflect fundamental metabolic disturbances, including insulin resistance, compromised mitochondrial activity, and altered hepatic lipid metabolism. Consequently, alterations in amino acid metabolism have been proposed as potential biomarkers for disease progression and metabolic dysfunction in MASLD. This review aims to evaluate the correlation between the amino acid profile and histological changes in pediatric MASLD, including steatosis, steatohepatitis, and fibrosis. Full article

The stability of Indonesia’s banking sector is closely linked to the effectiveness of capital regulations, particularly as a country that aligns its policies with Basel III standards. Ensuring that banks have adequate capital buffers is crucial for mitigating systemic risk. However, the interaction between regulatory requirements and actual banking behavior in developing countries remains poorly understood. This study aims to evaluate the impact of Indonesia’s capital requirement instruments, including the countercyclical capital buffer (CCyB), the capital conservation buffer (CCB), and the capital surcharge, on credit performance and financial stability across various bank categories. Using a quantitative approach, the analysis utilizes panel data from commercial banks, state-owned banks and regional development banks over several periods, using the panel regression method and Difference-in-Differences (DID) to assess how changes in buffer levels affect credit growth, Non-Performing Loans (NPLs), and the Capital Adequacy Ratio (CAR). The results show that capital buffers have a statistically significant effect on lending behavior: a 1% increase in buffer levels is associated with a measurable decrease in credit expansion across several bank groups, while CCBs exhibit a stronger stabilizing effect than CCyBs. Although these instruments do not eliminate financial uncertainty, they contribute to more prudent risk-taking. This study also revealed that the CCyB rate increases when the financial cycle is in an expansionary phase. Conversely, if the economy slows (as during the pandemic), the CCyB rate can be lowered back to 0% to encourage bank intermediation, thus shaping the bank’s responses to regulation. Several implications of implementing a capital buffer in Indonesia include the benefits of resilience and bank behavior during credit expansion. Overall, this study concludes that aligning regulatory frameworks with real-world banking behavior is crucial for enhancing financial stability in developing countries, such as Indonesia. Full article

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking. Full article

Parallel, data-intensive applications are now commonly executed on infrastructures that combine Cloud, Fog, and Edge resources. In these environments, execution takes place on devices with markedly different computational power and over networks whose latency and bandwidth can fluctuate over time. Under these conditions, overall performance is influenced not only by processing speed but also by communication delays arising from data dependencies between tasks. This leads to a basic issue: whether scheduling strategies developed under computation-focused assumptions continue to perform well once communication costs are made explicit. This work examines the behavior of simple and widely adopted scheduling heuristics when network effects are modeled directly within the system. No new scheduling algorithms are introduced. Instead, the analysis focuses on how execution time and monetary cost change for deterministic parallel workloads deployed on hierarchical Cloud–Edge infrastructures exposed to stochastic latency and bandwidth variations. For this purpose, we introduce CLOWNSim, a lightweight discrete-event simulation framework that supports large-scale Monte Carlo experiments on fixed task graphs, allowing infrastructural and scheduling effects to be examined independently of workload variability. The experimental analysis covers fully centralized Cloud deployments, intermediate Fog configurations, and resource-constrained IoT scenarios. Scheduling policies based on computational speed, execution cost, or random device selection are evaluated across these settings. In Cloud and Fog environments, communication latency and data transfers represent a substantial portion of the overall makespan, weakening the impact of scheduling decisions driven primarily by computation. In IoT scenarios, limited processing capacity becomes the main limiting factor, while communication overhead remains present but less influential in comparison. The results indicate that performance trends across the Cloud–Edge continuum cannot be attributed to scheduler choice alone. Execution behavior arises from the combined effects of workload structure, placement decisions, and network properties, with different elements becoming dominant depending on the deployment context. The proposed simulation framework offers a practical way to study these interactions and to assess cost–performance trade-offs under communication conditions that reflect realistic operating environments. Full article

Various neuromodulatory benefits of the ketogenic diet (KD) have been demonstrated, yet its influence on reward learning and underlying mechanisms remain poorly defined. This study combined proteomics and metabolomics to identify key molecular changes in the hippocampus of KD-fed mice. Our analysis revealed significant upregulation of the “dopaminergic synapse” pathway, with CAMK2A emerging as a central regulator. In vitro, treatment of the hippocampal neuronal cell line HT22 with β-hydroxybutyrate (BHB), a primary KD metabolite, increased the protein expression of CAMK2A and increased the phosphorylation of its downstream target, GluA1. Crucially, Camk2a knockdown completely blocked BHB-induced p-GluA1 enhancement. To determine the behavioral relevance, we stereotaxically delivered AAV-shCamk2a into the hippocampus of KD-fed mice. Knockdown of Camk2a reversed the pro-reward effects of KD, as measured by the sucrose preference test and conditioned place preference test, without impairing general locomotor activity in the open field test. Together, these results suggest a novel BHB–CAMK2A–dopaminergic signaling axis through which KD enhances reward learning, thus bridging systemic metabolism with cognitive function and expanding our understanding of KD-mediated neuromodulation. Full article

The present study investigates the influence of geometric parameters on the vibro-acoustic performance of piezoelectric speakers, with the objective of establishing quantitative design guidelines for resonance tuning and sound pressure level (SPL) enhancement. Understanding the dimension-dependent behavior of such devices is essential for the development of compact and efficient acoustic transducers. To this end, a fully coupled electromechanical–acoustic finite element model is developed in the frequency domain, incorporating linear piezoelectric constitutive relations, structural dynamics, and an external acoustic air domain. The model systematically examines the effects of variations in piezoelectric disc thickness, brass diaphragm thickness, and diaphragm radius. The results demonstrate that increasing the piezoelectric disc thickness leads to a noticeable increase in resonance frequency and a measurable enhancement in SPL due to strengthened electromechanical coupling. In contrast, reducing the brass membrane thickness primarily shifts the resonance frequency to lower values, while producing negligible changes in SPL amplitude. Furthermore, enlarging the diaphragm radius significantly decreases the fundamental resonance frequency, confirming its dominant influence on stiffness-controlled vibration behavior. These findings quantitatively establish the relationship between geometric design parameters and acoustic response, providing a predictive framework for performance optimization. The proposed modeling approach offers an effective and reliable tool for the design and refinement of high-performance piezoelectric speaker systems. Full article

Purpose: To evaluate the 1-year behavior of accommodation and optical quality one year after the implantation of phakic intraocular lenses, specifically the implantable collamer lens (ICL), in myopic patients, comparing outcomes between low- and high-myopia groups. Methods: This comparative longitudinal study included 38 eyes of 38 patients who underwent ICL implantation for myopia correction. Patients were divided into two groups based on preoperative manifest sphere: low myopia (−2.50 D to −6.25 D) and high myopia (>−6.25 D to −12.50 D). The amplitude of accommodation (AA), subjective accommodative response (AR), optical quality parameters including the modulation transfer function (MTF) cut-off, objective scatter index (OSI) and Strehl ratio (SR), and objective accommodative response with a double-pass system (HD Analyzer, Visiometrics) were assessed preoperatively, 1 month, and 1 year postoperatively. Results: Both groups achieved postoperative refractive outcomes close to emmetropia, with high efficacy and safety indices. A statistically significant decrease in the amplitude of accommodation was observed at 1 month and remained stable at 1 year in both groups; however, this change was not clinically meaningful. The optical quality parameters (MTF cut-off, OSI, and Strehl ratio) and objective accommodative response with the HD Analyzer showed no clinically relevant changes over time, with no significant intergroup differences detected (p-value > 0.05). Conclusions: An initial reduction in accommodative amplitude was observed after ICL implantation without recovery over time; however, it was not clinically relevant, as it fell within the test–retest variability in the minus lens technique. Other accommodative parameters and optical quality remained stable at 1 year in both low and high myopia. Full article