Search Results (32)

Background/Objectives: Preeclampsia (PE) may be associated with maternal endothelial activation and altered early neonatal inflammation. The primary objective was to compare maternal and early neonatal serum interleukin 2 (IL2), IL6, IL8, tumor necrosis factor α (TNFα), and endothelin 1 (ET1) concentrations between PE and control mother–neonate pairs. Methods: This secondary report from a single-center observational cohort included 31 women with PE (34 neonates) and 45 control women (47 neonates). Biomarker concentrations were log-transformed, and PE–control differences were estimated using linear models, with results expressed as geometric mean ratios (GMRs). Neonatal models used pregnancy-clustered standard errors to account for twins. The Benjamini–Hochberg procedure was applied to control the false discovery rate (FDR). Additional neonatal models adjusted for gestational age at birth served as sensitivity analyses. Selected biomarker–outcome associations were explored post hoc using univariable Firth bias-reduced logistic regression. Results: Maternal ET1 was higher in PE (GMR = 1.79, 95% confidence interval—CI—1.53–2.09; q < 0.001). Neonatal IL2 (GMR = 1.37, 95% CI 1.11–1.71; q = 0.016) and TNFα (GMR = 1.49, 95% CI 1.22–1.82; q < 0.001) were higher in PE-exposed neonates. After gestational age at birth adjustment, the TNFα difference persisted (GMR = 1.33, 95% CI 1.14–1.55; q = 0.003), whereas IL2 was attenuated (GMR = 1.20, 95% CI 0.99–1.45; q = 0.164). None of the exploratory Firth associations remained significant after FDR correction. Thrombocytopenia at birth occurred in 5/33 PE-exposed and 1/47 control neonates; intraventricular hemorrhage occurred in 2/34 and 2/47, respectively. Conclusions: PE was associated with between-group differences in maternal endothelial and early neonatal inflammatory biomarkers, with maternal ET1 and neonatal TNFα as the most robust signals. Complication-related findings remain hypothesis-generating and do not support predictive cut-offs, risk stratification, or causal inference. Full article

This paper presents a traffic-driven scaling framework for a digital twin proxy pool (DTPP) in vehicular edge computing (VEC), designed to eliminate the latency and synchronization issues inherent in conventional digital twin (DT) migration approaches. The core innovation lies in replacing the migration of vehicle DTs between edge servers (ESs) with instantaneous switching within a pre-allocated pool of DT proxies, thereby achieving zero migration latency and continuous synchronization. The proposed architecture differentiates between short-term DTs (SDTs) hosted in edge-side in-memory databases for real-time, low-latency services, and long-term DTs (LDTs) in the cloud for historical data aggregation. A queuing-theoretic model formulates the DTPP as an M/M/c system, deriving a closed-form lower bound for the minimum number of proxies required to satisfy a predefined queuing-delay constraint, thus transforming quality-of-service targets into analytically computable resource allocations. The scaling mechanism operates on a cloud–edge collaborative principle: a cloud-based predictor, employing a TCN-Transformer fusion model, forecasts hourly traffic arrival rates to set a baseline proxy count, while edge-side managers perform monotonic, 5 min scale-ups based on real-time monitoring to absorb sudden traffic bursts without causing service jitter. Extensive evaluations were conducted using the PeMS dataset. The TCN-Transformer predictor significantly outperforms single-model baselines, achieving a mean absolute percentage error (MAPE) of 17.83%. More importantly, dynamic scaling at the ES reduces delay violation rates substantially—for instance, from 13.57% under static provisioning to just 1.35% when the minimum proxy count is 2—confirming the system’s ability to maintain service quality under highly dynamic conditions. These findings shows that the DTPP framework provides a robust solution for resource-efficient and latency-guaranteed DT services in VEC. Full article

Nanocomposites composed of compatibilized polyolefin blends and organically modified montmorillonite (OMMT) nanoparticles were produced through melt mixing using a twin-screw extruder. High-density polyethylene (HDPE) and polypropylene (PP) blends were compatibilized with maleic anhydride-grafted PE compatibilizer (COMP). Blends with a 10/25 (w/w) HDPE/PP content were prepared and were reinforced with 1, 2, and 3 phr OMMT. Characterization of nanocomposites was performed using X-ray diffraction (XRD), Thermogravimetric Analysis (TGA), Differential Scanning Calorimetry (DSC), Tensile Testing, and Melt Flow Index (MFI) measurements. Preparation of polyolefin blend/OMMT nanocomposites with a twin-screw extruder was successful at low clay levels (1 phr). These nanocomposites presented increased onset temperature of thermal degradation, crystallinity, and stiffness, whereas their MFI values were lower than those of the pure matrix. Full article

This paper presents a novel, robust, and reliable control strategy for renewable energy production systems, leveraging artificial neural networks (ANNs) to optimize performance and efficiency. Unlike conventional ANN approaches that rely on perturbation-based methods, we develop a fundamentally different ANN model incorporating equilibrium points (EPs) that achieve superior regulation of photovoltaic (PV) systems. The efficacy of the proposed approach is evaluated through comparative analysis against the conventional control strategy based on perturb and observe (MPPT/PO), demonstrating a 3.3% improvement in system efficiency (98.3% vs. 95%), a five times faster response time (6 s vs. 30 s), and six-fold reduction in voltage ripple (1% vs. 5.95%). A critical aspect of ANN-based controller design is the learning phase, which is addressed through the integration of deep reinforcement learning (DRL) for primary PV system control. Specifically, a hybrid control architecture combining the Artificial Neural Network based on Equilibrium Points (ANN/EP) model with DRL (ANN/PE-RL) is introduced, utilizing a synergistic integration of two reinforcement learning agents: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG). The TD3-based hybrid approach achieves an average reward value of 434.78 compared to 422.767 for DDPG, representing a 2.84% performance improvement in tracking maximum power points under imbalanced conditions. This hybrid approach demonstrates significant potential for improving the overall performance of grid-connected PV systems, reducing energy losses from 1.95% to below 1%, offering a promising solution for advanced renewable energy management. Full article

This paper presents TwinP2G, a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling, including electricity, green hydrogen, natural gas, and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles, including data scientists and energy experts, using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally, TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid. Full article

This study aims to improve the performance of wood–plastic composites (WPCs) composed of cotton stalk powder and residual film particles. Additionally, it aims to promote the efficient utilization of cotton stalk biochar. The composites were prepared using modified cotton stalk biochar and xylem powder as the matrix, maleic anhydride grafted high-density polyethylene (MA-HDPE) as the coupling agent, and polyethylene (PE) residual film particles as the filler. The WPCs were fabricated through melt blending using a twin-screw extruder. Mechanical properties were evaluated using a universal testing machine and texture analyzer, Shore D hardness was measured using a durometer, and microstructure was analyzed using a high-resolution digital optical microscope. A systematic investigation was conducted on the effect of biochar content on material properties. The results indicated that modified biochar significantly enhanced the mechanical and thermal properties of the WPCs. At a biochar content of 80%, the material achieved optimal performance, with a hardness of 57.625 HD, a bending strength of 463.159 MPa, and a tensile strength of 13.288 MPa. Additionally, thermal conductivity and thermal diffusivity decreased to 0.174 W/(m·K) and 0.220 mm²/s, respectively, indicating improved thermal insulation properties. This research provides a novel approach for the high-value utilization of cotton stalks and residual films, offering a potential solution to reduce agricultural waste pollution in Xinjiang and contributing to the development of low-cost and high-performance WPCs with wide-ranging applications. Full article

This study uses the melt compounding method to recycle polypropylene-based car bumper waste (PP-CBW) in order to produce nanocomposite films for mulch application. The nanocomposite films were compounded by mixing virgin linear low-density polyethylene (LLDPE) with PP-CBW at a constant ratio of 4:1 in the presence of different percentages of nanofillers. Nanocomposites reinforced with nanoclays were compatibilized with an anhydride grafted polyethylene (PE-g-MAH), at a constant compatibilizer-to-clay ratio equal to 3, to improve the adherence between the nonpolar matrix and the hydrophilic nanoclay and acrylic paint present in the car bumper. An extruder with a corotating twin screw was used to produce blends of different compositions. To create nanocomposite films, the mixtures were further processed in a blown film extruder. The effect of the presence of nanoclays on the barrier, thermal, and mechanical properties of the nanocomposite films was investigated. The dispersion of clay layers in the matrix was examined by atomic force microscopy (AFM). The results indicate that 3 wt% of clay loading maximized the tensile strength in the transverse direction (TD) and machine direction (MD). A 1 wt% clay loading increased the MD tear resistance by 66% and manifested an optimum dart impact strength. Significant improvements in thermal and barrier properties were also achieved in the presence of 3 wt% clay loading. Full article

The need for renewable and biodegradable materials for packaging applications has grown significantly in recent years. Growing environmental worries over the widespread use of synthetic and non-biodegradable polymeric packaging, particularly polyethylene, are linked to this increase in demand. This study investigated the degradation properties of low-density polyethylene (LDPE), a material commonly used in packaging, after incorporating various natural fillers that are sustainable, compatible, and biodegradable. The LDPE was mixed with 2.5, 5, and 10 wt.% of sawdust, cellulose powder, and Nanocrystalline cellulose (CNC). The composites were melted and mixed using a twin-screw extruder machine with a screw speed of 50 rpm at 190 °C to produce sheets using a specific die. These sheets were used to prepare samples for rheological tests that measured the viscosity curve, the flow curve, and a non-Newtonian mathematical model using a capillary rheometer at 170, 190, and 210 °C. X-ray diffraction analysis was carried out on the 5 wt.% samples, and a short-term degradation test was conducted in soil with a pH of 6.5, 50% humidity, and a temperature of 27 °C. The results revealed that the composite melts exhibited non-Newtonian behavior, with shear thinning being the dominant characteristic in the viscosity curves. The shear viscosity increased as the different cellulose additives increased. The 5% ratio had a higher viscosity for all composite melts, and the LDPE/CNC melts showed higher viscosities at different temperatures. The curve fitting results confirmed that the power-law model best described the flow behavior of all composite melts. The LDPE/sawdust and cellulose powder melts showed higher flow index (n) and lower viscosity consistency (k) values compared with LDPE/CNC melted at different temperatures. The sawdust and powder composites had greater weight loss compared with the LD vbbPE/CNC composites; digital images supported these results after 30 days. The degradation test and weight loss illustrated stronger relations with the viscosity values at low shear rates. The higher the shear viscosity, the lower the degradation and vice versa. Full article

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications. Full article

Background: It is well established in the literature that pregnancy-associated plasma protein-A (PAPP-A) is linked to several adverse pregnancy outcomes, including pre-eclampsia (PE), fetal growth restriction (FGR), and preterm birth (PTB) in singleton pregnancies. However, data regarding such an association in twin pregnancies are lacking. The primary goal of this systematic review and meta-analysis was to assess the potential value of low PAPP-A levels in the prediction of the subsequent development of hypertensive disorders of pregnancy (HDPs), PTB, and small for gestational age (SGA)/FGR fetuses in twin pregnancies and investigate its association with the development of gestational diabetes, intrauterine death (IUD) of at least one twin, and birth weight discordance (BWD) among the fetuses. Methods: Medline, Scopus, CENTRAL, Clinicaltrials.gov, and Google Scholar databases were systematically searched from inception until 31 July 2024. All observational studies reporting low PAPP-A levels after the performance of the first-trimester combined test as part of the screening for chromosomal abnormalities with reported adverse pregnancy outcomes were included. Results: The current systematic review encompassed a total of 11 studies (among which 6 were included in the current meta-analysis) that enrolled a total of 3741 patients. Low PAPP-A levels were not associated with HDPs (OR 1.25, 95% CI 0.78, 2.02, I-square test: 13%). Low PAPP-A levels were positively associated with both the development of preterm birth prior to 32 (OR 2.85, 95% CI 1.70, 4.77, I-square test: 0%) and 34 weeks of gestational age (OR 2.09, 95% CI 1.34, 3.28, I-square test: 0%). Furthermore, low PAPP-A levels were positively associated with SGA/FGR (OR 1.58, 95% CI 1.04, 2.41, I-square test: 0%). Prediction intervals indicated that the sample size that was used did not suffice to support these findings in future studies. Conclusions: Our study indicated that low PAPP-A levels are correlated with an increased incidence of adverse perinatal outcomes in twin pregnancies. Identifying women at elevated risk for such adversities in twin pregnancies may facilitate appropriate management and potential interventions, but additional studies are required to identify the underlying mechanism linking PAPP-A with those obstetrical complications. Full article

This study concerns the optimization of an industrial recycling line; in other terms, this paper aims to find the optimal processing parameters that allow for a decrease in the loss of stress crack resistance (SCR) using a notched crack ligament stress (NCLS) test and an increase in the gain of the elongation at break, flexural modulus, and Izod impact strength of a polyethylene (PE) blend before and after recycling. The recycling line is composed mainly of a mono- and twin-screw extruder and a filtration system. Hence, the research question is as follows: How can we optimize the recycling process, without compromising the mechanical properties of recycled polyethylene (PE) blends? To answer the research question, Taguchi’s design of experiment and grey relational analysis (GRA) for multiobjective optimization was applied. Experiments were performed according to $L_{16}$ standard orthogonal array based on five process parameters: mono-screw design, screw speed of the mono- and twin-screw extruder, melt pump pressure, and filter mesh size. Based on grey relational analysis (GRA), the optimal setting of process parameters was identified, and a barrier screw and a higher screw speed for both extruders were allowed to have optimal mechanical properties. Furthermore, the analysis of variance (ANOVA) indicated that the mono-screw design and screw speed of the mono- and twin-screw extruder significantly impact the mechanical properties of recycled polyethylene (PE) blends. Full article

Plastic containers, commonly produced from non-biodegradable petroleum-based plastics such as polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), raise significant environmental concerns due to their persistence. The disposal of agricultural waste, specifically rice straw (RS), through burning, further compounds these environmental issues. In response, this study explores the integration of polylactic acid (PLA), a biodegradable material, with RS using a twin-screw extruder and injection process, resulting in the creation of a biodegradable packaging material. The inclusion of RS led to a decrease in the melt flow rate, thermal stability, and tensile strength, while concurrently enhancing the hydrophilic properties of the composite polymers. Additionally, the incorporation of maleic anhydride (MA) contributed to a reduction in the water absorption rate. The optimized formulation underwent migration testing and met the standards for food packaging products. Furthermore, no MA migration was detected from the composite. This approach not only provides a practical solution for the disposal of RS, but also serves as an environmentally-friendly alternative to conventional synthetic plastic waste. Full article

Prime editing is a rapidly developing method of CRISPR/Cas-based genome editing. The increasing number of novel PE applications and improved versions demands constant analysis and evaluation. The present review covers the mechanism of prime editing, the optimization of the method and the possible next step in the evolution of CRISPR/Cas9-associated genome editing. The basic components of a prime editing system are a prime editor fusion protein, consisting of nickase and reverse transcriptase, and prime editing guide RNA, consisting of a protospacer, scaffold, primer binding site and reverse transcription template. Some prime editing systems include other parts, such as additional RNA molecules. All of these components were optimized to achieve better efficiency for different target organisms and/or compactization for viral delivery. Insights into prime editing mechanisms allowed us to increase the efficiency by recruiting mismatch repair inhibitors. However, the next step in prime editing evolution requires the incorporation of new mechanisms. Prime editors combined with integrases allow us to combine the precision of prime editing with the target insertion of large, several-kilobase-long DNA fragments. Full article

In order to improve the problem of overly relying on situational information, high computational power requirements, and weak adaptability of traditional maneuver methods used by hypersonic vehicles (HV), an intelligent maneuver strategy combining deep reinforcement learning (DRL) and deep neural network (DNN) is proposed to solve the hypersonic pursuit–evasion (PE) game problem under tough head-on situations. The twin delayed deep deterministic (TD3) gradient strategy algorithm is utilized to explore potential maneuver instructions, the DNN is used to fit to broaden application scenarios, and the intelligent maneuver strategy is generated with the initial situation of both the pursuit and evasion sides as the input and the maneuver game overload of the HV as the output. In addition, the experience pool classification strategy is proposed to improve the training convergence and rate of the TD3 algorithm. A set of reward functions is designed to achieve adaptive adjustment of evasion miss distance and energy consumption under different initial situations. The simulation results verify the feasibility and effectiveness of the above intelligent maneuver strategy in dealing with the PE game problem of HV under difficult situations, and the proposed improvement strategies are validated as well. Full article

This work describes a systematic method for the analysis of the attrition and residual morphology of natural fibers during the compounding process by twin-screw extrusion. There are several methods for the assessment of fiber lengths and morphology, although they are usually based on the use of non-affordable apparatus or time-consuming methods. In this research, the variation of morphological features such as the length, diameter and aspect ratio of natural fibers were analyzed by affordable optical scanning methods and open-source software. This article presents the different steps to perform image acquisition, refining and measurement in an automated way, achieving statistically representative results, with thousands of fibers analyzed per scanned sample. The use of this technique for the measurement of giant reed fibers in polyethylene (PE) and polylactide (PLA)-based composite materials has proved that there are no significant differences in the output fiber morphology of the compound, regardless of the fiber feed sizes, extruder scale, or the polymer used as matrix. The ratio of fiber introduced for the production of composites also did not significantly affect the final fiber size. The greatest reduction in size was obtained in the first kneading zone during compounding. Pelletizing or injection molding did not significantly modify the fiber size distribution. Full article