Enterococcus faecium B13, selected from fermentation chili, has been proven to promote animal growth by previous studies, but it belongs to opportunistic pathogens, so a comprehensive evaluation of its probiotic properties and safety is necessary. In this study, the probiotic properties and safety of B13 were evaluated at the genetic and phenotype levels in vitro and then confirmed in vivo. The genome of B13 contains one chromosome and two plasmids. The average nucleotide identity indicated that B13 was most closely related to the fermentation-plant-derived strain. The strain does not carry the major virulence genes of the clinical E. faecium strains but contains aac(6′)-Ii, ant (6)-Ia, msrC genes. The strain had a higher tolerance to acid at pH 3.0, 4.0, and 0.3% bile salt and a 32.83% free radical DPPH clearance rate. It can adhere to Caco-2 cells and reduce the adhesion of E. coli to Caco-2 cells. The safety assessment revealed that the strain showed no hemolysis and did not exhibit gelatinase, ornithine decarboxylase, lysine decarboxylase, or tryptophanase activity. It was sensitive to twelve antibiotics but was resistant to erythromycin, rifampicin, tetracycline, doxycycline, and minocycline. Experiments in vivo have shown that B13 can be located in the ileum and colon and has no adverse effects on experiment animals. After 28 days of feeding, B13 did not remarkable change the α-diversity of the gut flora or increase the virulence genes. Our study demonstrated that E. faecium B13 may be used as a probiotic candidate. Full article

The transition to higher education at University is a critical moment for young adults to acquire unhealthy habits regarding physical activity (PA) and adherence to a healthy diet. Negative behaviors might be maintained in the years to come with a major risk of suffering from a Non-Communicable Disease. This study aims to determine the relationship between diet and PA in the student community of University of Milano-Bicocca. Students between 18 and 30 years old completed an online survey (6949 students). Two analyses of covariance (ANCOVA), chi-square tests of independence, and a binomial logistic regression were performed to examine the relationship between adequacy of food consumption and PA, in association also with sociodemographic characteristics. Data show a strong correlation between behaviors analyzed, with a proportional positive association between PA and healthy diet. Nevertheless, a third of the sample students incur in incorrect habits for both diet and PA. Further, students performing intensive PA have the healthiest food consumption in general but the worst red meat and pork intake. Accordingly, men practice more PA but have a less adequate diet, exactly contrary to women. In conclusion, policies promoting consciousness of well-being would transform Universities into healthy hubs for virtuous habits. Full article

Newborn screening (NBS) is hailed as a public health success, but little is known about the long-term outcomes following a positive newborn screen. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort. Six programs developed and tested a core set of minimal LTFU data elements. After an iterative data collection process and the development of a data collection tool, the group agreed on the minimal LTFU data elements. The denominator captured all infants with an NBS diagnosis, accounting for children who moved or died prior to the follow-up year. They also agreed on three LTFU outcomes: if the child was still alive, had contact with a specialist, and received appropriate care specific to their diagnosis within the year. The six programs representing NBS public health programs, clinical providers, and research programs provided data across multiple NBS disorders. In 2022, 83.8% (563/672) of the children identified by the LTFU programs were alive and living in the jurisdiction; of those, 92.0% (518/563) saw a specialist, and 87.7% (494/563) received appropriate care. The core LTFU data elements can be applied as a foundation to address the impact of early diagnosis by NBS within and across jurisdictions. Full article

Piecewise fractional-order differential operators have received more attention in recent years because they can be used to describe various evolutionary dynamical problems to investigate crossover behaviors. In this manuscript, we use the aforementioned operators to investigate a mathematical model of COVID-19. By utilizing fractional calculus, our approach aims to capture the crossover dynamics of disease spread, considering heterogeneity and transitions between epidemic phases. This research seeks to develop a framework using specialized mathematical techniques, such as the Caputo fractional derivative, with the potential to investigate the crossover dynamical behaviors of the considered epidemic model. The anticipated contribution lies in bridging fractional calculus and epidemiology, offering insights for both theoretical advancements and practical public health interventions. In order to improve our understanding of epidemic dynamics and support, we used MATLAB to simulate numerical results for a visual representation of our findings. For this interpretation, we used various fractional-order values. In addition, we also compare our simulated results with some reported results for infected and death classes to demonstrate the efficiency of our numerical method. Full article

The SARS-CoV-2 virus, responsible for COVID-19, often manifests symptoms akin to viral pneumonia, complicating early detection and potentially leading to severe COVID pneumonia and long-term effects. Particularly affecting young individuals, the elderly, and those with weakened immune systems, the accurate classification of COVID-19 poses challenges, especially with highly dimensional image data. Past studies have faced limitations due to simplistic algorithms and small, biased datasets, yielding inaccurate results. In response, our study introduces a novel classification model that integrates advanced texture feature extraction methods, including GLCM, GLDM, and wavelet transform, within a deep learning framework. This innovative approach enables the effective classification of chest X-ray images into normal, COVID-19, and viral pneumonia categories, overcoming the limitations encountered in previous studies. Leveraging the unique textures inherent to each dataset class, our model achieves superior classification performance, even amidst the complexity and diversity of the data. Moreover, we present comprehensive numerical findings demonstrating the superiority of our approach over traditional methods. The numerical results highlight the accuracy (random forest (RF): 0.85; SVM (support vector machine): 0.70; deep learning neural network (DLNN): 0.92), recall (RF: 0.85, SVM: 0.74, DLNN: 0.93), precision (RF: 0.86, SVM: 0.71, DLNN: 0.87), and F1-Score (RF: 0.86, SVM: 0.72, DLNN: 0.89) of our proposed model. Our study represents a significant advancement in AI-based diagnostic systems for COVID-19 and pneumonia, promising improved patient outcomes and healthcare management strategies. Full article

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560–680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer. Full article

Elastic spherical polishing tools effectively conform to the polishing surface and exhibit high efficiency in the removal of materials, so they are extensively used in the sub-aperture polishing stages of optical components. However, their processing is often accompanied by significant mid-spatial frequency (MSF) errors, which critically degrade the performance of optical systems. To suppress the MSF errors generated during polishing with spherical tools, this study investigates the influence factor of MSF errors during the polishing process through an analysis of the convolution effect in material removal. A material removal profile model is established, and a uniform removal simulation is conducted to assess the influence of different shape material removal profiles on MSF errors. Simulation and experimental results show that a Gaussian-like shape material removal profile is more effective in suppressing the MSF errors during polishing compared to the “W” and trapezoidal shape material removal profiles. In addition, based on the characteristics of the RMS decreasing in a serrated trend with the decrease in path spacing, a path spacing optimization method considering the polishing efficiency is proposed to improve the polishing efficiency while controlling the MSF errors, and the effectiveness of the path spacing optimization method is verified by comparing the MSF error at the maximum theoretical path spacing and the path spacing that is less than this. Finally, the path spacing optimization method is used to polish single-crystal silicon to further illustrate its practicality. Full article

Weaning stress imposes considerable physiological challenges on piglets, often manifesting in intestinal disturbances, such as inflammation and compromised barrier function, ultimately affecting growth and health outcomes. While conventional interventions, including antimicrobials, have effectively mitigated these sequelae, concerns surrounding antimicrobial resistance necessitate the exploration of alternatives. Fucoidan, derived from brown seaweed, offers promise due to its antioxidant and anti-inflammatory effects. Previous research has been limited to the in-feed supplementation of partially purified fucoidan extracted from brown seaweed. The focus of the present study is assessing the effect of a preweaning drench with highly purified (85%) fucoidan on piglet growth, immune response, and intestinal morphology post-weaning. Forty-eight male piglets at 17 ± 3 days of age (5.67 ± 0.16 kg) were assigned to a saline (control), fucoidan, or antimicrobial group, receiving treatment as a single 18 mL oral drench three days before weaning. Monitoring for seven days post-weaning included body weight measurements, blood sample collection for the inflammatory protein assay, and small intestine morphological analysis. The findings revealed that the preweaning fucoidan drench did not elicit adverse effects on piglets. However, neither fucoidan nor antimicrobial drenches significantly enhanced growth parameters, immune markers, or intestinal morphology compared to that of the control-treated piglets (p > 0.05). The lack of response may be attributed to the high health status of the experimental cohort and the limitation of a single dosage. Future research should consider a more challenging production setting to evaluate the viability and optimal application of fucoidan as an antimicrobial alternative in the pig industry. Full article

Gap junctions (GJs) are important in the regulation of cell growth, morphology, differentiation and migration. However, recently, more attention has been paid to their role in the pathogenesis of different diseases as well as tumorigenesis, invasion and metastases. The expression pattern and possible role of connexins (Cxs), as major GJ proteins, under both physiological and pathological conditions in the adrenal gland, were evaluated in this review. The databases Web of Science, PubMed and Scopus were searched. Studies were evaluated if they provided data regarding the connexin expression pattern in the adrenal gland, despite current knowledge of this topic not being widely investigated. Connexin expression in the adrenal gland differs according to different parts of the gland and depends on ACTH release. Cx43 is the most studied connexin expressed in the adrenal gland cortex. In addition, Cx26, Cx32 and Cx50 were also investigated in the human adrenal gland. Cx50 as the most widespread connexin, along with Cx26, Cx29, Cx32, Cx36 and Cx43, has been expressed in the adrenal medulla with distinct cellular distribution. Considerable effort has recently been directed toward connexins as therapeutically targeted molecules. At present, there exist several viable strategies in the development of potential connexin-based therapeutics. The differential and hormone-dependent distribution of gap junctions within adrenal glands, the relatively large gap junction within this gland and the increase in the gap junction size and number following hormonal treatment would indicate that gap junctions play a pivotal role in cell functioning in the adrenal gland. Full article

Hydrogen, as a clean, safe, and efficient energy carrier, is one of the hot energy sources that have attracted much attention. Mo₂C, due to the introduction of C atoms, makes the atomic spacing of the Mo lattice decrease and changes the width of the d-band, which makes the electronic properties of Mo₂C similar to that of Pt noble metals, exhibiting excellent electrochemical hydrogen precipitation performance. MoS₂, due to its special crystal structure and tunable electronic structure, has been widely studied. In this paper, Mo₂C nanoparticles were prepared by high-temperature carbonization, and then two-dimensional layered MoS₂ were be loaded on Mo₂C nanoparticles by the hydrothermal method to synthesize Mo₂C/MoS₂ composite catalysts. Their electrochemical hydrogen precipitation (HER) performance under acidic conditions was tested. The above catalysts were also characterized by modern material testing methods such as XRD, SEM, TEM, and XPS. The results showed that the composite catalysts exhibited the most excellent electrochemical hydrogen precipitation performance at Mo₂C/MoS₂-3, with the lowest overpotential at a current density of 10 mA cm⁻², Tafel slope, and electrochemical impedance. At the same time, the electrochemically active area was dramatically enhanced, with good stability under prolonged testing. The catalytic activity was significantly improved compared with that of Mo₂C and MoS₂. The characterization and experimental results indicate that the heterogeneous structure of Mo₂C and MoS₂ formed a built-in electric field between the two, which accelerated the electron transfer efficiency and provided more active sites. The Mo₂C/MoS₂ composite catalyst is a low-cost, easy-to-prepare, and high-efficiency electrochemical hydrogen precipitation catalyst, providing a new idea for developing green and clean energy. Full article

Enteroviruses (EV) are important pathogens causing human disease with various clinical manifestations. To date, treatment of enteroviral infections is mainly supportive since no vaccination or antiviral drugs are approved for their prevention or treatment. Here, we describe the antiviral properties and mechanisms of action of leucoverdazyls—novel heterocyclic compounds with antioxidant potential. The lead compound, 1a, demonstrated low cytotoxicity along with high antioxidant and virus-inhibiting activity. A viral strain resistant to 1a was selected, and the development of resistance was shown to be accompanied by mutation of virus-specific non-structural protein 2C. This resistant virus had lower fitness when grown in cell culture. Taken together, our results demonstrate high antiviral potential of leucoverdazyls as novel inhibitors of enterovirus replication and support previous evidence of an important role of 2C proteins in EV replication. Full article

A comprehensive study of the interactions of human serum albumin (HSA) and α-1-acid glycoprotein (AAG) with two isoquinoline alkaloids, i.e., allocryptopine (ACP) and protopine (PP), was performed. The UV-Vis spectroscopy, molecular docking, competitive binding assays, and circular dichroism (CD) spectroscopy were used for the investigations. The results showed that ACP and PP form spontaneous and stable complexes with HSA and AAG, with ACP displaying a stronger affinity towards both proteins. Molecular docking studies revealed the preferential binding of ACP and PP to specific sites within HSA, with site 2 (IIIA) being identified as the favored location for both alkaloids. This was supported by competitive binding assays using markers specific to HSA’s drug binding sites. Similarly, for AAG, a decrease in fluorescence intensity upon addition of the alkaloids to AAG/quinaldine red (QR) complexes indicated the replacement of the marker by the alkaloids, with ACP showing a greater extent of replacement than PP. CD spectroscopy showed that the proteins’ structures remained largely unchanged, suggesting that the formation of complexes did not significantly perturb the overall spatial configuration of these macromolecules. These findings are crucial for advancing the knowledge on the natural product–protein interactions and the future design of isoquinoline alkaloid-based therapeutics. Full article

Forest fires often pose serious hazards, and the timely monitoring and extinguishing of residual forest fires using unmanned aerial vehicles (UAVs) can prevent re-ignition and mitigate the damage caused. Due to the urgency of forest fires, drones need to respond quickly during firefighting operations, while traditional drone formation deployment requires a significant amount of time. This paper proposes a pure azimuth passive positioning strategy for circular UAV formations and utilizes the Deep Q-Network (DQN) algorithm to effectively adjust the formation within a short timeframe. Initially, a passive positioning model for UAVs based on the relationships between the sides and angles of a triangle is established, with the closest point to the ideal position being selected as the position for the UAV to be located. Subsequently, a multi-target optimization model is developed, considering 10 UAVs as an example, with the objective of minimizing the number of adjustments while minimizing the deviation between the ideal and adjusted UAV positions. The DQN algorithm is employed to solve and design experiments for validation, demonstrating that the deviation between the UAV positions and the ideal positions, as well as the number of adjustments, are within acceptable ranges. In comparison to genetic algorithms, it saves approximately 120 s. Full article

Thrombotic disease has been listed as the third most fatal vascular disease in the world. After decades of development, clinical thrombolytic drugs still cannot avoid the occurrence of adverse reactions such as bleeding. A number of studies have shown that the application of various nano-functional materials in thrombus-targeted drug delivery, combined with external stimuli, such as magnetic, near-infrared light, ultrasound, etc., enrich the drugs in the thrombus site and use the properties of nano-functional materials for collaborative thrombolysis, which can effectively reduce adverse reactions such as bleeding and improve thrombolysis efficiency. In this paper, the research progress of organic nanomaterials, inorganic nanomaterials, and biomimetic nanomaterials for drug delivery is briefly reviewed. Full article

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience. Full article

Sea surface salinity (SSS) variability at mesoscales has become an important research topic in recent decades, thanks to satellite missions enabling observations of SSS with global capacity and mesoscale resolution. Here, we analyze the near-global data of the Aquarius/SAC-D along-track SSS, focusing on the slopes of SSS variance spectra in the mesoscale range from 180 to 430 km. In the vast extratropics, the spectral slope is close to −2, indicating a dynamical regime for the inverse cascade of depth-integrated energy identified by the surface quasi-geostrophic theory. However, the spectral slopes in regions near the mouths of the largest rivers are steeper than −2, reaching −3, possibly due to the large river freshwater flux. In addition, data from high-resolution thermosalinograph are used to validate satellite measurements and show good consistency in terms of SSS variance spectral slopes. Full article

Additive manufacturing enables the production of lattice structures, which have been proven to be a superior class of lightweight mechanical metamaterials whose specific stiffness can reach the theoretical limit of the upper Hashin–Shtrikman bound for isotropic cellular materials. To achieve isotropy, complex structures are required, which can be challenging in powder bed additive manufacturing, especially with regard to subsequent powder removal. The present study focuses on the Finite Element Method simulation of 2,5D anisotropic plate lattice metamaterials and the investigation of their lightweight potential. The intentional use of anisotropic structures allows the production of a cell architecture that is easily manufacturable via Laser Powder Bed Fusion (LPBF) while also enabling straightforward optimization for specific load cases. The work demonstrates that the considered anisotropic plate lattices exhibit high weight-specific stiffnesses, superior to those of honeycomb structures, and, simultaneously, a good de-powdering capability. A significant increase in stiffness and the associated surpassing of the upper Hashin–Shtrikman bound due to anisotropy is achievable by optimizing wall thicknesses depending on specific load cases. A stability analysis reveals that, in all lattice structures, plastic deformation is initiated before linear buckling occurs. An analysis of stress concentrations indicates that the introduction of radii at the plate intersections reduces stress peaks and simultaneously increases the weight-specific stiffnesses and thus the lightweight potential. Exemplary samples illustrate the feasibility of manufacturing the analyzed metamaterials within the LPBF process. Full article

To address the challenges in processing and identifying mine acoustic emission signals, as well as the inefficiency and inaccuracy issues prevalent in existing methods, an enhanced CELMD approach is adopted for preprocessing the acoustic emission signals. This method leverages correlation coefficient filtering to extract the primary components, followed by classification and recognition using the Swin Transformer neural network. The results demonstrate that the improved CELMD method effectively extracts the main features of the acoustic emission signals with higher decomposition accuracy and reduced occurrences of mode mixing and end effects. Furthermore, the Swin Transformer neural network exhibits outstanding performance in classifying acoustic emission signals, surpassing both convolutional neural networks and ViT neural networks in terms of accuracy and convergence speed. Moreover, utilizing preprocessed data from the improved CELMD enhances the performance of the Swin Transformer neural network. With an increase in data volume, the accuracy, stability, and convergence speed of the Swin Transformer neural network continuously improve, and using preprocessed data from the enhanced CELMD yields superior training results compared to those obtained without preprocessing. Full article

Vasa previa is a pregnancy complication that occurs when unprotected fetal blood vessels traverse the cervical os, placing the fetus at high risk of exsanguination and fetal death. These fetal vessels may be compromised by fetal movement and compression, leading to poor oxygen distribution and asphyxiation. Diagnostic tools for vasa previa management and preterm labor (PTL) include transvaginal ultrasound, cervical length (CL) surveillance and use of fetal fibronectin (FFN) testing. These tools can prove to be quite useful as they allow for lead time in the prediction of PTL and spontaneous rupture of membranes which can result in devastating outcomes for pregnancies affected by vasa previa. We conducted a literature review on vasa previa management and the usefulness of FFN and CL surveillance in predicting PTL and found 36 related papers. Although there is limited research available to show the impact of FFN and CL surveillance in the management of vasa previa, there is sufficient evidence to support FFN and CL surveillance in predicting the onset of PTL, which can have devastating consequences for the pregnancies affected. It can be extrapolated that these tools, by helping to determine pregnancies at risk for PTL, could improve management and outcomes in patients with vasa previa. Future studies investigating the management of vasa previa with FFN and CL surveillance to reduce the burden of PTL and its associated comorbidities are warranted. Full article

Chitosan-triphosphate (TPP) nanogels are widely studied drug delivery carrier systems, typically prepared via a simple mixing process. However, the effects of the processing factors on nanogel production have not been extensively explored, despite the importance of understanding and standardising such factors to allow upscaling and commercial usage. This study aims to systematically evaluate the effects of various fabrication and processing factors on the properties of nanogels using a Design of Experiment approach. Hydrodynamic size, polydispersity index (PDI), zeta potential, and encapsulation efficiency were determined as the dependent factors. The temperature, stirring rate, chitosan grade, crosslinker choice, and the interaction term between temperature and chitosan grade were found to have a significant effect on the particle size, whereas the effect of temperature and the addition rate of crosslinker on the PDI was also noteworthy. Moreover, the addition rate of the crosslinker and the volume of the reaction vessel were found to impact the encapsulation efficiency. The zeta potential of the nanogels was found to be governed by the chitosan grade. The optimal fabrication conditions for the development of medium molecular weight chitosan and TPP nanogels included the following: the addition rate for TPP solution was set at 2 mL/min, while the solution was then stirred at a temperature of 50 °C and a stirring speed of 600 rpm. The volume of the glass vial used was 28 mL, while the stirrer size was 20 mm. The second aim of the study was to evaluate the potential for scaling up the nanogels. Size and PDI were found to increase from 128 nm to 151 nm and from 0.232 to 0.267, respectively, when the volume of the reaction mixture was increased from 4 to 20 mL and other processing factors were kept unchanged. These results indicate that caution is required when scaling up as the nanogel properties may be significantly altered with an increasing production scale. Full article

Enamel microcracks (EMCs) arising during the removal of metal orthodontic brackets represent a considerable challenge in dentistry. This in vitro study aims to explore the impacts of thermocycling, the types of orthodontic bonding agent, and curing techniques on the enamel surface of the tooth structure following the debonding of orthodontic metal brackets. It also examines the incidence, number, length, and direction of EMCs on the buccal surface of the tooth. Additionally, the study compares adhesive remnant index (ARI) scores and bracket failure post-debonding. Forty extracted human maxillary canines were divided into ten groups, including intact enamel negative controls (groups 1, 2) and groups (3–8) with metal brackets bonded using two different bonding agents and curing techniques. Following bonding, half of the groups underwent thermocycling testing. EMCs, ARI scores, and bracket failure modes were evaluated. The formation, length, and direction of cracks did not significantly differ among groups, regardless of experimental conditions. Thermocycling had no significant effect on ARI scores or bracket failure modes. However, significant variations were observed among curing technique groups, with seventh-generation bonding agents demonstrating potential effectiveness in achieving complete adhesive removal. The study underscores the importance of considering bonding agent systems and curing protocols to optimize bond outcomes and minimize the risk of metal bracket failure in orthodontic treatment. Full article

Robotic manipulators play a pivotal role in modern intelligent manufacturing and unmanned production systems, often tasked with executing specific paths accurately. However, the input of the robotic manipulators is trajectory which is a path with time information. The resulting core technology is trajectory planning methods which are broadly classified into two categories: maximum velocity curve (MVC) methods and multiphase direct collocation (MPDC) methods. This paper concentrates on addressing challenges associated with the latter methods. In MPDC methods, the solving efficiency and accuracy are greatly influenced by the number of discretization nodes. When dealing with systems with complex dynamics, such as robotic manipulators, striking a balance between solving time and path discretization errors becomes crucial. We use a mesh refinement (MR) algorithm to find a suitable number of nodes under the premise of ensuring the path discretization error. So, the actual device can effectively implement the planned solutions. Nonetheless, the conventional application of the MR algorithm requires solving the original problem in each iteration; these processes are extremely time-consuming and may fail to solve when dealing with a complex dynamic system. As a result, we propose a sequential optimal trajectory planning scheme to solve the problem efficiently by dividing the original optimal control (OC) problem into two stages: path planning (PP) and trajectory planning (TP). In the PP stage, we employ a DC method based on arc length and an MR algorithm to identify key nodes along the specified path. This aims to minimize the approximation error introduced during discretization. In the TP stage, the identified key nodes serve as boundary conditions for an MPDC method based on time. This facilitates the generation of an optimal trajectory that maximizes motion performance, considering constant velocity in Cartesian space and dynamic constraints while keeping the path discretization error. Simulation and experiment are conducted with a six-axis robotic manipulator, ROCR6, and show significant potential for a wide range of applications in robotics. Full article