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It is generally recognized that the use of physical and digital information-based solutions for tracking plastic materials along a value chain can favour the transition to a circular economy and help to overcome obstacles. In the near future, traceability and information exchange between
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It is generally recognized that the use of physical and digital information-based solutions for tracking plastic materials along a value chain can favour the transition to a circular economy and help to overcome obstacles. In the near future, traceability and information exchange between all actors in the value chain of the plastics industry will be crucial to establishing more effective recycling systems. Recycling plastics is a complex process that is particularly complicated in the case of acrylonitrile butadiene styrene (ABS) plastic because of its versatility and use in many applications. This literature study is part of a larger EU-funded project with the acronym ABSolEU (Paving the way for an ABS recycling revolution in the EU). One of its goals is to propose a suitable traceability system for ABS products through physical marking with a digital connection to a suitable data-management system to facilitate the circular use of ABS. The aim of this paper is therefore to review and assess the current and future techniques for traceability with a particular focus on their use for ABS plastics as a basis for this proposal. The scientific literature and initiatives are discussed within three technological areas, viz., labelling and traceability systems currently in use, digital data sharing systems and physical marking. The first section includes some examples of systems used commonly today. For data sharing, three digital technologies are discussed, viz., Digital Product Passports, blockchain solutions and certification systems, which identify a product through information that is attached to it and store, share and analyse data throughout the product’s life cycle. Finally, several different methods for physical marking are described and evaluated, including different labels on a product’s surface and the addition of a specific material to a polymer matrix that can be identified at any point in time with the use of a special light source or device. The conclusion from this study is that the most promising data management technology for the near future is blockchain technology, which could be shared by all ABS products. Regarding physical marking, producers must evaluate different options for individual products, using the most appropriate and economical technology for each specific product. It is also important to evaluate what information should be attached to a specific product to meet the needs of all actors in the value chain.
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A fixed-time adaptive guaranteed performance tracking control is investigated for a category of nonholonomic mobile robots (NMRs) under asymmetric state constraints. For the sake of favorable transient and steady-state properties of the system, a prescribed performance function (PPF) is introduced and a transform
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A fixed-time adaptive guaranteed performance tracking control is investigated for a category of nonholonomic mobile robots (NMRs) under asymmetric state constraints. For the sake of favorable transient and steady-state properties of the system, a prescribed performance function (PPF) is introduced and a transform function is further constructed. Based on the backstepping technique, an asymmetric barrier Lyapunov function is formulated to ensure the tracking errors converge within a human-specified time. On the foundation of this, the occupation of communication channel is effectively reduced by assigning an event-triggered mechanism (ETM) with relative threshold to the process of controller design. By utilizing the proposed control strategy, the NMR is capable of implementing the enemy dislodging mission while the enemy can always be caught by the NMR and the collision would never be presented. Finally, two simulation experiments are given to verify the effectiveness of the proposed scheme.
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Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However,
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Dam is an essential structure in hydraulic engineering, and its surface cracks pose significant threats to its integrity, impermeability, and durability. Automated crack detection methods based on computer vision offer substantial advantages over manual approaches with regard to efficiency, objectivity and precision. However, current methods face challenges such as misidentification, discontinuity, and loss of details when analyzing real-world dam crack images. These images often exhibit characteristics such as low contrast, complex backgrounds, and diverse crack morphologies. To address the above challenges, this paper presents a pure Vision Transformer (ViT)-based dam crack segmentation network (DCST-net). The DCST-net utilizes an improved Swin Transformer (SwinT) block as the fundamental block for enhancing the long-range dependencies within a SegNet-like encoder–decoder structure. Additionally, we employ a weighted attention block to facilitate side fusion between the symmetric pair of encoder and decoder in each stage to sharpen the edge of crack. To demonstrate the superior performance of our proposed method, six semantic segmentation models have been trained and tested on both a self-built dam crack dataset and two publicly available datasets. Comparison results indicate that our proposed model outperforms the mainstream methods in terms of visualization and most evaluation metrics, highlighting its potential for practical application in dam safety inspection and maintenance.
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The labor force is a crucial factor in conducting economic activities, especially in labor-surplus countries like Pakistan. In this study, we explore the impact of labor force participation (LF) on economic growth (EG), with an emphasis on how this impact depends on the
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The labor force is a crucial factor in conducting economic activities, especially in labor-surplus countries like Pakistan. In this study, we explore the impact of labor force participation (LF) on economic growth (EG), with an emphasis on how this impact depends on the levels of health and education expenditures. We analyze time series data from Pakistan spanning from 1980 to 2022, using ARDL (Autoregressive Distributed Lag), ECM (Error Correction Model) and Granger causality techniques for empirical analysis. The ARDL results indicate that LF significantly boosts EG, both in the short and long run. Furthermore, the estimations reveal that better facilities for health and education strengthen the positive effects of LF on EG. This suggests a complementary relationship between health, education, and LF in driving EG. Moreover, our findings highlight the temporal significance of health and education: Health plays a more crucial role in the short run, while education’s impact is more substantial in the long run. Furthermore, the Granger causality results indicate that LF, health, and education significantly contribute to EG. It is advisable for the government to prioritize investments in the health and education sectors. This approach can empower individuals to actively and effectively participate in economic activities, eventually contributing to the overall economic output of the nation.
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Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions.
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Current research on automated driving systems focuses on Level 4 automated driving (AD) in specific operational design Domains (ODD). Measurement data from customer fleet operation are commonly used to extract scenarios and ODD features (road infrastructure, etc.) for the testing of AD functions. To ensure data relevance for the vehicle use case, driving domain classification of the data is required. Generally, classification into urban, extra-urban and highway domains provides data with similar ODD features. Highway classification can be implemented using global navigation satellite system coordinates of the driving route, map-matching algorithms, and road classes stored in digital maps. However, the distinction between urban and extra-urban driving domains is more complex, as settlement taxonomies and administrative-level hierarchies are not globally consistent. Therefore, this paper presents a map-based method for driving domain classification. First, potential urban areas (PUA) are identified based on urban land-use density, which is determined based on land-use categories from OpenStreetMap (OSM) and then spatially smoothed by kernel density estimation. Subsequently, two road network scaling models are used to distinguish between urban and extra-urban domains for the PUA. Finally, statistics of ODD feature distribution are analysed for the classified urban and extra-urban areas.
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In this study, digital light processing (DLP) was utilized to generate 3D-printed blends composed of photosensitive acrylate-modified polylactic acid (PLA) resin mixed with varying weight ratios of lignin extracted from softwood, typically ranging from 5 wt% to 30 wt%. The microstructure of these
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In this study, digital light processing (DLP) was utilized to generate 3D-printed blends composed of photosensitive acrylate-modified polylactic acid (PLA) resin mixed with varying weight ratios of lignin extracted from softwood, typically ranging from 5 wt% to 30 wt%. The microstructure of these 3D-printed blends was examined through X-ray microtomography. Additionally, the tensile mechanical properties of all blends were assessed in relation to the weight ratio and post-curing treatment. The results suggest that post-curing significantly influences the tensile properties of the 3D-printed composites, especially in modulating the brittleness of the prints. Furthermore, an optimal weight ratio was identified to be around 5 wt%, beyond which UV light photopolymerization experiences compromises. These findings regarding acrylate-modified PLA/lignin blends offer a cost-effective alternative for producing 3D-printed bio-sourced components, maintaining technical performance in reasonable-cost, low-temperature 3D printing, and with a low environmental footprint.
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This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the
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This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.
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Curcumin, a polyphenol with a rich history spanning two centuries, has emerged as a promising therapeutic agent targeting multiple signaling pathways and exhibiting cellular-level activities that contribute to its diverse health benefits. Extensive preclinical and clinical studies have demonstrated its ability to enhance
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Curcumin, a polyphenol with a rich history spanning two centuries, has emerged as a promising therapeutic agent targeting multiple signaling pathways and exhibiting cellular-level activities that contribute to its diverse health benefits. Extensive preclinical and clinical studies have demonstrated its ability to enhance the therapeutic potential of various bioactive compounds. While its reported therapeutic advantages are manifold, predominantly attributed to its antioxidant and anti-inflammatory properties, its efficacy is hindered by poor bioavailability stemming from inadequate absorption, rapid metabolism, and elimination. To address this challenge, nanodelivery systems have emerged as a promising approach, offering enhanced solubility, biocompatibility, and therapeutic effects for curcumin. We have analyzed the knowledge on curcumin nanoencapsulation and its synergistic effects with other compounds, extracted from electronic databases. We discuss the pharmacokinetic profile of curcumin, current advancements in nanoencapsulation techniques, and the combined effects of curcumin with other agents across various disorders. By unifying existing knowledge, this analysis intends to provide insights into the potential of nanoencapsulation technologies to overcome constraints associated with curcumin treatments, emphasizing the importance of combinatorial approaches in improving therapeutic efficacy. Finally, this compilation of study data aims to inform and inspire future research into encapsulating drugs with poor pharmacokinetic characteristics and investigating innovative drug combinations to improve bioavailability and therapeutic outcomes.
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Chemical investigation of marine fungus Nigrospora oryzae SYSU-MS0024 cultured on solid-rice medium led to the isolation of three new alkaloids, including a pair of epimers, nigrosporines A (1) and B (2), and a pair of enantiomers, (+)-nigrosporine C (+)-
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Chemical investigation of marine fungus Nigrospora oryzae SYSU-MS0024 cultured on solid-rice medium led to the isolation of three new alkaloids, including a pair of epimers, nigrosporines A (1) and B (2), and a pair of enantiomers, (+)-nigrosporine C (+)-3, and (−)-nigrosporine C (−)-3, together with eight known compounds (4–11). Their structures were elucidated based on extensive mass spectrometry (MS) and 1D/2D nuclear magnetic resonance (NMR) spectroscopic analyses and compared with data in the literature. The absolute configurations of compounds 1–3 were determined by a combination of electronic circular dichroism (ECD) calculations, Mosher’s method, and X-ray single-crystal diffraction technique using Cu Kα radiation. In bioassays, compound 2 exhibited moderate inhibition on NO accumulation induced by lipopolysaccharide (LPS) on BV-2 cells in a dose-dependent manner at 20, 50, and 100 μmol/L and without cytotoxicity in a concentration of 100.0 μmol/L. Moreover, compound 2 also showed moderate acetylcholinesterase (AChE) inhibitory activities with IC50 values of 103.7 μmol/L. Compound 5 exhibited moderate antioxidant activity with EC50 values of 167.0 μmol/L.
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The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy
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The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value.
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Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently,
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Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I–V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I–V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles’ population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I–V dataset and an I–V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.
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White-collar workers around the world are reconfiguring their ways of working. Some have found their way out, performing office work outdoors, through walk-and-talks, outdoor meetings, or reading sessions. Working outdoors has proved both invigorating and challenging. This qualitative interview study aims to develop
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White-collar workers around the world are reconfiguring their ways of working. Some have found their way out, performing office work outdoors, through walk-and-talks, outdoor meetings, or reading sessions. Working outdoors has proved both invigorating and challenging. This qualitative interview study aims to develop a conceptual framework concerning the implications of white-collar workers incorporating the outdoors into their everyday work life. Applying a constructivist grounded theory approach, 27 interviews with a total of 15 participants were systematically analyzed. Findings evolved around the following categories: practicing outdoor office work, challenging the taken-for-granted, enjoying freedom and disconnection, feeling connected and interdependent, promoting health and well-being, enhancing performance, and finally adding a dimension to work. These categories were worked into a conceptual model, building on the dynamic relationship between the practice of working outdoors on one hand, and how this challenges the system in which office work traditionally takes place on the other. Interviews reflected the profound learning process of the employees. Drawing on the concepts of free space and resonance, we demonstrate how performing office work outdoors may unlock a transformative potential by opening up connectedness and interdependence and contribute to a sustainable work life as well as overall sustainable development.
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Among 441 patients screened, 192 patients were included in the final analysis. The multivariate analysis showed that weight loss, necrotic granuloma, normal serum lysozyme level and hypergammaglobulinemia were significantly associated with TB. A risk score of TB was built based on these variables
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Among 441 patients screened, 192 patients were included in the final analysis. The multivariate analysis showed that weight loss, necrotic granuloma, normal serum lysozyme level and hypergammaglobulinemia were significantly associated with TB. A risk score of TB was built based on these variables and was able to discriminate TB versus sarcoidosis with an AUC of 0.85 (95%CI: 0.79–0.91). Using the Youden’s J statistic, its most discriminant value (−0.36) was associated with a sensitivity of 80% and a specificity of 75%. Conclusion: We developed a score based on weight loss, necrotic granuloma, normal serum lysozyme level and hypergammaglobulinemia with an excellent capacity to discriminate TB versus sarcoidosis. This score needs still to be validated in a multicentric prospective study.
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Food-producing animals are exposed to mycotoxins through ingestion, inhalation, or dermal contact with contaminated materials. This exposure can lead to serious consequences for animal health, affects the cost and quality of livestock production, and can even impact human health through foods of animal
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Food-producing animals are exposed to mycotoxins through ingestion, inhalation, or dermal contact with contaminated materials. This exposure can lead to serious consequences for animal health, affects the cost and quality of livestock production, and can even impact human health through foods of animal origin. Therefore, controlling mycotoxin exposure in animals is of utmost importance. A systematic literature search was conducted in this study to retrieve the results of monitoring exposure to mycotoxins in food-producing animals over the last five years (2019–2023), considering both external exposure (analysis of feed) and internal exposure (analysis of biomarkers in biological matrices). The most commonly used analytical technique for both approaches is LC-MS/MS due to its capability for multidetection. Several mycotoxins, especially those that are regulated (ochratoxin A, zearalenone, deoxynivalenol, aflatoxins, fumonisins, T-2, and HT-2), along with some emerging mycotoxins (sterigmatocystin, nivalenol, beauvericin, enniantins among others), were studied in 13,818 feed samples worldwide and were typically detected at low levels, although they occasionally exceeded regulatory levels. The occurrence of multiple exposure is widespread. Regarding animal biomonitoring, the primary objective of the studies retrieved was to study mycotoxin metabolism after toxin administration. Some compounds have been suggested as biomarkers of exposure in the plasma, urine, and feces of animal species such as pigs and poultry. However, further research is required, including many other mycotoxins and animal species, such as cattle and sheep.
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C/S1 basic leucine zipper (bZIP) transcription factors are essential for plant survival under energy deficiency. However, studies on the responses of C/S1-bZIPs to low energy in woody plants have not yet been reported. In this study, members of C/S1-bZIP subfamilies in Populus tomentosa [...] Read more.
C/S1 basic leucine zipper (bZIP) transcription factors are essential for plant survival under energy deficiency. However, studies on the responses of C/S1-bZIPs to low energy in woody plants have not yet been reported. In this study, members of C/S1-bZIP subfamilies in Populus tomentosa were systematically analyzed using bioinformatic approaches. Four C-bZIPs and 10 S1-bZIPs were identified, and their protein properties, phylogenetic relationships, gene structures, conserved motifs, and uORFs were systematically investigated. In yeast two-hybrid assays, direct physical interactions between C-bZIP and S1-bZIP members were observed, highlighting their potential functional synergy. Moreover, expression profile analyses revealed that low energy induced transcription levels of most C/S1-bZIP members, with bZIP55 and bZIP21 (a homolog of bZIP55) exhibiting particularly significant upregulation. When the expression of bZIP55 and bZIP21 was co-suppressed using artificial microRNA mediated gene silencing in transgenic poplars, root growth was promoted. Further analyses revealed that bZIP55/21 negatively regulated the root development of P. tomentosa in response to low energy. These findings provide insights into the molecular mechanisms by which C/S1-bZIPs regulate poplar growth and development in response to energy deprivation.
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Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for
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Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros.
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Microglia are key players in the brain’s innate immune response, contributing to homeostatic and reparative functions but also to inflammatory and underlying mechanisms of neurodegeneration. Targeting microglia and modulating their function may have therapeutic potential for mitigating neuroinflammation and neurodegeneration. The anti-inflammatory properties
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Microglia are key players in the brain’s innate immune response, contributing to homeostatic and reparative functions but also to inflammatory and underlying mechanisms of neurodegeneration. Targeting microglia and modulating their function may have therapeutic potential for mitigating neuroinflammation and neurodegeneration. The anti-inflammatory properties of essential oils suggest that some of their components may be useful in regulating microglial function and microglial-associated neuroinflammation. This study, starting from the ethnopharmacological premises of the therapeutic benefits of aromatic plants, assessed the evidence for the essential oil modulation of microglia, investigating their potential pharmacological mechanisms. Current knowledge of the phytoconstituents, safety of essential oil components, and anti-inflammatory and potential neuroprotective effects were reviewed. This review encompasses essential oils of Thymus spp., Artemisia spp., Ziziphora clinopodioides, Valeriana jatamansi, Acorus spp., and others as well as some of their components including 1,8-cineole, β-caryophyllene, β-patchoulene, carvacrol, β-ionone, eugenol, geraniol, menthol, linalool, thymol, α-asarone, and α-thujone. Essential oils that target PPAR/PI3K-Akt/MAPK signalling pathways could supplement other approaches to modulate microglial-associated inflammation to treat neurodegenerative diseases, particularly in cases where reactive microglia play a part in the pathophysiological mechanisms underlying neurodegeneration.
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Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact
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Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.
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Large-caliber and long-barrel weapons are important experimental devices for exploring the impact resistance and reliability of warheads. The force of impact of the muzzle jet has a significant influence on the overload resistance of the warhead and surrounding devices. The mechanism of motion
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Large-caliber and long-barrel weapons are important experimental devices for exploring the impact resistance and reliability of warheads. The force of impact of the muzzle jet has a significant influence on the overload resistance of the warhead and surrounding devices. The mechanism of motion of the body inside the tube cannot be ignored owing to the high kinetic energy at the muzzle. In this study, we designed the relevant experiment and a simulation model to analyze the structural characteristics and mechanism of evolution of the shock wave and the vortex structure in a muzzle jet. The aim was to examine the evolution of the shock wave with initial jet-induced interference. And we established three other simulation models to compare the similarities and differences between the results of the models. The results showed that, in the original complex model, the initial jet restricted the free expansion of the muzzle jet, and this led to many shock–shock collisions that retarded the development of the shock waves. Multiple reflected shock waves were thus formed under a high local pressure that distorted the shock structure, while the structure of the shock wave in the simplified models was clear and simple. The parameters of motion of the body changed by a little when the initial jet-induced interference was ignored. The difference in values of the strongest impact force measured at monitoring points far from the muzzle was small, with an error of about 2%, such that the simplified model without the initial jet could be used in place of the original complex model. The other simplified models yielded significant differences. Our results provide an important theoretical basis for further research on the muzzle jet and its applications in engineering.
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Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from
[...] Read more.
Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs.
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Many researchers have studied the factors that impact on students’ entrepreneurial intention; however, findings are conflicting. The present study attempts, through an extensive review of the literature, to provide a holistic view and deeper knowledge of the most significant factors that influence university
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Many researchers have studied the factors that impact on students’ entrepreneurial intention; however, findings are conflicting. The present study attempts, through an extensive review of the literature, to provide a holistic view and deeper knowledge of the most significant factors that influence university students’ decisions to be self-employed or to start a business. A systematic review as well as a bibliometric analysis of the literature was implemented, using a three-step literature mapping protocol to search, select, evaluate, and validate the literature by examining and analyzing numerous papers from the scientific community. The process ended up with 677 papers, from which the forty-three most cited were used as our research sample. Findings revealed that there are four primary categories of factors: the contextual factors, such as the economic, social, and political environment, the motivational factors, such as individuals’ personal needs, personality traits, and characteristics, and the factors related with the personal background of individuals such as family, education, and peers. We also examined the countries with the maximum number of papers on university students’ entrepreneurial intentions. These findings can be useful for policy makers and educators and will serve as a basis for future research, while they also contribute to the literature by highlighting the factors that most affect the entrepreneurial intention of university students.
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This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save
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This study’s goals are divided into two categories. The first is to design and build an excavator equipped with parallel electrical linear actuators. The second is to generate and test a PSO-based and a PFM-based path for this excavator in order to save energy by reducing energy consumption, improve the digging accuracy by minimizing the deviation between the desired and dug surfaces of the ground, and prevent colliding with subsurface objects. For this purpose, computer vision was employed to improve monitoring and verification. Five types of experiments were carried out in this investigation. The first two and the other three examined the impact of energy conservation in PSO- and PFM-based path generation, respectively. Finally, the results from these experiments were compared to identify and show the effect of optimal path generation.
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by
Jimmie L. Bullock, Jr., Thomas E. Hickey, Troy J. Kemp, Jordan Metz, Sarah Loftus, Katarzyna Haynesworth, Nicholas Castro, Brian T. Luke, Douglas R. Lowy and Ligia A. Pinto
Vaccines2024, 12(5), 516; https://doi.org/10.3390/vaccines12050516 (registering DOI) - 9 May 2024
SARS-CoV-2 vaccination-induced protection against infection is likely to be affected by functional antibody features. To understand the kinetics of antibody responses in healthy individuals after primary series and third vaccine doses, sera from the recipients of the two licensed SARS-CoV-2 mRNA vaccines were
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SARS-CoV-2 vaccination-induced protection against infection is likely to be affected by functional antibody features. To understand the kinetics of antibody responses in healthy individuals after primary series and third vaccine doses, sera from the recipients of the two licensed SARS-CoV-2 mRNA vaccines were assessed for circulating anti-SARS-CoV-2 spike IgG levels and avidity for up to 6 months post-primary series and 9 months after the third dose. Following primary series vaccination, anti-SARS-CoV-2 spike IgG levels declined from months 1 to 6, while avidity increased through month 6, irrespective of the vaccine received. The third dose of either vaccine increased anti-SARS-CoV-2 spike IgG levels and avidity and appeared to enhance antibody level persistence—generating a slower rate of decline in the 3 months following the third dose compared to the decline seen after the primary series alone. The third dose of both vaccines induced significant avidity increases 1 month after vaccination compared to the avidity response 6 months post-primary series vaccination (p ≤ 0.001). A significant difference in avidity responses between the two vaccines was observed 6 months post-third dose, where the BNT162b2 recipients had higher antibody avidity levels compared to the mRNA-1273 recipients (p = 0.020).
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