Time Consumption Research Articles

A fast laser ultrasonic SAFT method with sparse scanning data and F-K based interpolation

ABSTRACT Laser ultrasonic SAFT can be applied for noncontact testing and imaging of defects in structures. However, due to the large demand for signal data by laser scanning, the time consumption of this method would be relatively long. To address this problem, this paper proposes a fast LU-SAFT method with sparse scanning data and F-K based interpolation. It aims to reduce the inspection time but without decreasing the imaging resolution. First, the method determines the minimum number of scanning sparse points using simulation. Then the F-K interpolation method is utilised to recover the full-field unscanned point signal from the sparse scan data. Finally, the reconstructed signal is used for SAFT image reconstruction of the defect. The experimental results show that, for the same scanning area, the proposed method in this paper reduces the number of scanning points by 87.5% and shortens the scanning time by 86.0% compared to conventional LU-SAFT point-by-point scanning. Additionally, the signal-to-noise ratio of the defective SAFT imaging decreases by only about 7.9%.

Is intensive training with atime interval between instruction and planning CT necessary for deep inspiration breath-hold radiotherapy in breast cancer?

Breathing instruction and exercises and atime gap between training and planning CT scans (pCT) is recommended as part of deep inspiration breath-hold (DIBH) assisted radiotherapy (RT). However, this is associated with additional time expenditure. In two of the authors' treatment centers (TC), patient training took place before the planning CT of DIBH-assisted therapy. In TC1, afurther appointment was made with aminimum interval of 2days to perform the planning CT. At TC2, the planning CT was performed immediately after the first patient instruction. Aretrospective evaluation of the clinical parameters of the therapy was carried out to investigate the relevance of the time gap between DIBH exercises and pCT. Atotal of 72patients were included, 35 of whom were treated in TC1 and37 in TC2. In TC1, an average interval of ~4 days was observed between patient training and planning CT, while in TC2, training and CT were performed immediately after each other. No significant differences in radiation dose exposure of the lung on the treated side, the whole lung, or the heart were found between the two centers. Furthermore, there was no significant difference in the application of the daily RT fraction. The requirement for daily positioning checks was also the same at both treatment centers. This study does not show any advantages for atime gap between instruction/training and pCT. Skipping the time break does not deteriorate any clinically relevant endpoints.

Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning.

Adaptive video streaming relies on the construction of efficient bitrate ladders to deliver the best possible visual quality to viewers under bandwidth constraints. The traditional method of content dependent bitrate ladder selection requires a video shot to be pre-encoded with multiple encoding parameters to find the optimal operating points given by the convex hull of the resulting rate-quality curves. However, this pre-encoding step is equivalent to an exhaustive search process over the space of possible encoding parameters, which causes significant overhead in terms of both computation and time expenditure. To reduce this overhead, we propose a deep learning based method of content aware convex hull prediction. We employ a recurrent convolutional network (RCN) to implicitly analyze the spatiotemporal complexity of video shots in order to predict their convex hulls. A two-step transfer learning scheme is adopted to train our proposed RCN-Hull model, which ensures sufficient content diversity to analyze scene complexity, while also making it possible to capture the scene statistics of pristine source videos. Our experimental results reveal that our proposed model yields better approximations of the optimal convex hulls, and offers competitive time savings as compared to existing approaches. On average, the pre-encoding time was reduced by 53.8% by our method, while the average Bjontegaard delta bitrate (BD-rate) of the predicted convex hulls against ground truth was 0.26%, and the mean absolute deviation of the BD-rate distribution was 0.57%.

Applying neural network model to real-time frosting detection and intelligent defrosting control for air source heat pump

Periodic defrosting is essential for efficient operation of air source heat pump to avoid the attenuation of performance caused by frosting. However, the mal-defrosting phenomena in practice, which are no defrosting progress when necessary and unnecessary defrosting operation under no frosting, happen occasionally. To improve the accuracy of defrosting, a convolutional neural network model based on deep learning with YOLOv5 is introduced for frosting detection in this paper. Firstly, the evaluation method of frosting degree combining fractal dimension and heating capacity attenuation is constructed. Then, an image dataset is built based on frosting degree evaluation. The optimized YOLOv5-frost network is constructed, which improves the accuracy of frosting detection by iterative training. Thirdly, a novel defrosting control method based on YOLOv5-frost network is proposed, in which the defrosting initiation and termination points are investigated. Finally, comparative experiments are conducted between the proposed defrosting control method and traditional methods. The results indicate that the mal-defrosting phenomena are effectively avoided under the proposed method. The COP and total heating capacity are increased by 11.83 % and 7.42 % respectively, as well as the defrosting frequency and energy consumption are decreased by 75.00 % and 88.24 %, respectively. Additionally, the time consumption, memory overhead, and central processing unit overhead of the proposed method are 2.72 %, 71.58 %, and 74.07 % of those for the traditional methods, respectively. The performance of air source heat pump unit in real-time frosting detection and on-demand defrosting in complex conditions can be effectively improved with the proposed method.

Efficiency of computerized clinical decision support systems involving anticoagulants: A flashmob study in Dutch hospital pharmacies.

Computerized decision support systems (CDSSs) aim to prevent adverse drug events. However, these systems generate an overload of alerts that are not always clinically relevant. Anticoagulants are frequently involved in these alerts. The aim of this study was to investigate the efficiency of CDSS alerts on anticoagulants in Dutch hospital pharmacies. A multicentre, single-day, cross-sectional study was conducted using a flashmob design in Dutch hospital pharmacies, which have CDSSs that operate on both a national medication surveillance database and on self-developed clinical rules. Hospital pharmacists and pharmacy technicians collected data on the number and type of alerts and time needed for assessing these alerts. The primary outcome was the CDSS efficiency on anticoagulants, defined as the percentage of alerts on anticoagulants that led to an intervention. Secondary outcomes where among other CDSSs efficiency related to any medications and the time expenditure. Descriptive data-analysis was used. Of the 69 hospital pharmacies invited, 42 (61%) participated. The efficiency of CDSS alerts on anticoagulants was 4.0% (interquartile range [IQR] 14.0%) for the national medication surveillance database alerts and 14.3% (IQR 40.0%) for alerts from clinical rules. For any medication, the efficiency was lower: 1.8% (IQR 7.5%) and 13.4% (IQR 21.5%) respectively. The median time for assessing the relevance of all alerts was 2 (IQR 1:21) h/day for pharmacists and 6 (IQR 5:01) h/day for pharmacy technicians. CDSS efficiency is generally low, both for anticoagulants and any medication, while the time investment is high. Optimization of CDSSs is needed.

Convergence Acceleration of Favre-Averaged Non-Linear Harmonic Method

Abstract This paper develops a numerical procedure to accelerate the convergence of the Favre-averaged non-linear harmonic (FNLH) method. The scheme provides a unified mathematical framework for solving the sparse linear systems formed by the mean flow and the time-linearized harmonic flows of FNLH in an explicit or implicit fashion. The approach explores the similarity of the sparse linear systems of FNLH and leads to a memory-efficient procedure, so that its memory consumption does not depend on the number of harmonics to compute. The proposed method has been implemented in the industrial computational fluid dynamics solver Hydra. Three test cases are used to conduct a comparative study of explicit and implicit schemes in terms of convergence, computational efficiency, and memory consumption. Comparisons show that the implicit scheme yields better convergence than the explicit scheme and is also roughly 7–10 times more computationally efficient than the explicit scheme with four levels of multigrid. Furthermore, the implicit scheme consumes only approximately 50% of the memory required by the explicit scheme with four levels of multigrid. Compared with the full-annulus unsteady Reynolds-averaged Navier–Stokes simulations, the implicit scheme produces comparable results to URANS with computational time and memory consumption that are two orders of magnitude smaller.

A Gnn-Enhanced Ant Colony Optimization for Security Strategy Orchestration

The expansion of Internet of Things (IoT) technology and the rapid increase in data in smart grid business scenarios have led to a need for more dynamic and adaptive security strategies. Traditional static security measures struggle to meet the evolving low-voltage security requirements of state grid systems under this new IoT-driven environment. By incorporating symmetry in metaheuristic algorithms, we can further improve performance and robustness. Symmetrical properties have the potential to lead to more efficient and balanced solutions, improving the overall stability of the grid. We propose a gnn-enhanced ant colony optimization method for orchestrating grid security strategies, which trains across combinatorial optimization problems (COPs) that are representative scenarios in the state grid business scenarios, to learn specific mappings from instances to their heuristic measures. The learned heuristic metrics are embedded into the ant colony optimization (ACO) to generate the optimal security policy adapted to the current security situation. Compared to the ACO and adaptive elite ACO, our method reduces the average time consumption of finding a path within a limited time in the capacitated vehicle routing problem by 67.09% and 66.98%, respectively. Additionally, ablation experiments verify the effectiveness and necessity of the individual functional modules.

Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study

Port structures face the constant threat of deterioration due to exposure to dynamic and seismic forces, as well as harsh environmental condition prevalent in a maritime setting. These challenges lead to induced stress concentrations within the structures, resulting in potential catastrophic failures. Despite significant effort, expenses, and time consumption associated with traditional structural health monitoring systems (SHM), regular inspections remain the norm. This study introduces innovative methods to replace traditional approaches, leveraging Artificial Neural Networks (ANNs) as surrogates for real-time finite element (FE) modelling. The goal is to develop FE-based real-time digital twins for port structure by using sensor data, visualizing structural behaviour, and enabling a comprehensive monitoring of its structural response. An ANN model is trained using a validated FE model of the structure, generating immediate deformations based on sensor readings. By implementing this approach, stress variations are efficiently obtained and visualized throughout the structure. Unlike traditional methods that follow an inverse approach in estimating the entire structural response based on sensor values, the ANNs demonstrate high efficiency in addressing ill-conditioning issues inherent in such processes. This integrated methodology showcases the effectiveness of ANNs in providing real-time insights into the structural response of port infrastructure, offering a viable addition to the conventional SHM practices. The trained ANN model generates results in a high accuracy where the testing error is in the order of 10−5 and it generates data within 15 milli seconds demonstrating the near real-time conditions. The development of digital twins, facilitated by ANNs, demonstrates a promising solution for continuous monitoring, predictive maintenance, and risk mitigation in the face of dynamic operational and environmental challenges. The study contributes to the advancement of smart infrastructure by harnessing the capabilities of artificial intelligence and digital twin technology.

Experimental correlations to predict the cooling performance and compressor energy consumption of domestic refrigerator with phase change material

ABSTRACT The phase change material technology is continuously evolving in commercial market. In order to develop/design PCM modules of different capacity and different PCM material, it is necessary to develop the equation which will provide required information for development of PCM module. This paper establishes correlations to predict backup time and equivalent energy consumption of domestic refrigerator based on experimental data of testing of PCMs, potassium chloride (KCl), sodium chloride (NaCl), and sodium fluoride (NaF) dissolved in water for PCM masses varying between 0 and 1.75 kg in both closed door and door opening conditions. This paper investigates the impact of incorporating a phase-change material (PCM) on the cooling performance and energy consumption of a domestic refrigerator. The PCM module, filled with potassium chloride (KCl), sodium chloride (NaCl), and sodium fluoride (NaF) dissolved in water, is strategically placed on the contact surface of the refrigerator’s evaporator coil. The utilization of PCM aims to mitigate cabin temperature fluctuations and reduce overall energy consumption. Through experimental evaluation, the study assesses the effectiveness of different phase-change materials in enhancing the cooling performance and energy efficiency of the domestic refrigerator. The findings reveal that the incorporation of phase-change material significantly improves the cooling performance, as evidenced by the extended backup time during power cut-off and a reduction in compressor energy consumption. The tested phase-change materials, including KCl, NaCl, and NaF, demonstrate varying impacts on performance parameters. The backup time for closed-door tests is 1.83 – 1.03 times longer than that for door opening tests, respectively. The backup time of door opening tests is shortened due to heat entered to the cabin for all masses, between 45.45% and 3.23%. The backup time is increased by the masses of PCM KCl between 14% and 155%, the masses of PCM NaCl between 18% and 159%, and the masses of PCM NaF between 27% and 182%. The equivalent energy consumption for door opening tests is 1.12–1.02 times larger than that for the closed-door tests. The correlation equation established based on the existing experimental data can be used to accurately predict the backup time and equivalent energy consumption of refrigerator for tested PCM of any mass within the interval of 0 Kg to 1.75 Kg with ± 10% accuracy.

Comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set: a diagnostic study

BackgroundIn the recent years, National Early Warning Score2 (NEWS2) is utilized to predict early on, the worsening of clinical status in patients. To this date the predictive accuracy of National Early Warning Score (NEWS2), Revised Trauma Score (RTS), and Trauma and injury severity score (TRISS) regarding the trauma patients’ mortality rate have not been compared. Therefore, the objective of this study is comparing NEWS2, TRISS, and RTS in predicting mortality rate in trauma patients based on prehospital data set.MethodsThis cross-sectional retrospective diagnostic study performed on 6905 trauma patients, of which 4191 were found eligible, referred to the largest trauma center in southern Iran, Shiraz, during 2022–2023 based on their prehospital data set in order to compare the prognostic power of NEWS2, RTS, and TRISS in predicting in-hospital mortality rate. Patients are divided into deceased and survived groups. Demographic data, vital signs, and GCS were obtained from the patients and scoring systems were calculated and compared between the two groups. TRISS and ISS are calculated with in-hospital data set; others are based on prehospital data set.ResultsA total of 129 patients have deceased. Age, cause of injury, length of hospital stay, SBP, RR, HR, temperature, SpO2, and GCS were associated with mortality (p-value < 0.001). TRISS and RTS had the highest sensitivity and specificity respectively (77.52, CI 95% [69.3–84.4] and 93.99, CI 95% [93.2–94.7]). TRISS had the highest area under the ROC curve (0.934) followed by NEWS2 (0.879), GCS (0.815), RTS (0.812), and ISS (0.774). TRISS and NEWS were superior to RTS, GCS, and ISS (p-value < 0.0001).ConclusionThis novel study compares the accuracy of NEWS2, TRISS, and RTS scoring systems in predicting mortality rate based on prehospital data. The findings suggest that all the scoring systems can predict mortality, with TRISS being the most accurate of them, followed by NEWS2. Considering the time consumption and ease of use, NEWS2 seems to be accurate and quick in predicting mortality based on prehospital data set.

Metal‐Supported Heterogeneous Catalysts for the Synthesis of Nitrogen‐Containing Heterocycles: A Comprehensive Review

AbstractThe use of metals in the preparation of heterocycles has become increasingly popular due to several key advantages of metals as catalysts. These include recycling capability, eco‐friendliness, simplicity of catalyst preparation and separation, longer lifespan, abundance, and higher yields. As a result of their novel applications in organic, inorganic, medicinal, industrial, polymer chemistry, material sciences, nanotechnology, and biological processes, the utilization of nitrogen‐containing heterocycles has rapidly increased in recent years. Various nitrogen‐containing organic moieties are used in therapeutic processes to enhance effectiveness against several bacterial and viral diseases. This review covers the preparation of imidazoles, triazoles, tetrazoles, pyridine derivatives, azo compounds, pyrimidines, quinoxalines, and quinolines using metals as heterogeneous catalysts. It provides an overview of current methodologies for metal‐assisted heterogeneous catalytic reactions, including product yield, operating temperature, and solvents, among other factors. Heterogeneous catalyst‐mediated organic synthetic reactions offer several advantages, particularly in medicinal chemistry, pharmaceuticals, and industrial applications, by reducing time and energy consumption. This review emphasizes recent advancements in the fabrication of nitrogen heterocycles utilizing heterogeneous catalysts, taking into account various economic, eco‐friendly, and user‐friendly considerations. Additionally, it discusses the benefits, reaction conditions, and yields reported in the literature over the past six years.

Feature-Based Dataset Fingerprinting for Clustered Federated Learning on Medical Image Data

ABSTRACT Federated Learning (FL) allows multiple clients to train a common model without sharing their private training data. In practice, federated optimization struggles with sub-optimal model utility because data is not independent and identically distributed (non-IID). Recent work has proposed to cluster clients according to dataset fingerprints to improve model utility in such situations. These fingerprints aim to capture the key characteristics of clients’ local data distributions. Recently, a mechanism was proposed to calculate dataset fingerprints from raw client data. We find that this fingerprinting mechanism comes with substantial time and memory consumption, limiting its practical use to small datasets. Additionally, shared raw data fingerprints can directly leak sensitive visual information, in certain cases even resembling the original client training data. To alleviate these problems, we propose a Feature-based dataset FingerPrinting mechanism (FFP). We use the MedMNIST database to develop a highly realistic case study for FL on medical image data. Compared to existing methods, our proposed FFP reduces the computational overhead of fingerprint calculation while achieving similar model utility. Furthermore, FFP mitigates the risk of raw data leakage from fingerprints by design.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.

Efficient Headline Generation with Hybrid Attention for Long Texts

Headline generation aims to condense key information from an article or a document into a concise one-sentence summary. The Transformer structure is in general effective for such tasks, yet it suffers from a dramatic increase in training time and GPU consumption as the input text length grows. To address this problem, a hybrid attention mechanism is proposed. Both local and global semantic information among words are modeled in a way that significantly improves training efficiency, especially for long text. Effectiveness is not sacrificed; in fact, fluency and semantic coherence of the generated headlines are enhanced. Experimental results on an open benchmark dataset show that, compared to the baseline model’s best performance, the proposed model obtains a 14.7%, 16.7%, 14.4% and 9.1% increase in the F1 values of the ROUGE-1, the ROUGE-2, the ROUGE-L and the ROUGE-WE metrics, respectively. The semantic coherence of the generated text is also improved, as shown by a 2.8% improvement in the BERTScore’s F1 value. These results show that the effectiveness of the proposed headline generation model with the hybrid attention mechanism is also improved. The hybrid attention mechanism could provide references for relevant text generation tasks.

Efficient Implementation of Multilayer Perceptrons: Reducing Execution Time and Memory Consumption

A technique is presented that reduces the required memory of neural networks through improving weight storage. In contrast to traditional methods, which have an exponential memory overhead with the increase in network size, the proposed method stores only the number of connections between neurons. The proposed method is evaluated on feedforward networks and demonstrates memory saving capabilities of up to almost 80% while also being more efficient, especially with larger architectures.

Investigation of functional constipation in elderly inpatients and analysis of its influencing factors: A cross-sectional study.

To investigate the prevalence of functional constipation (FC) in elderly hospitalized patients and analyze the influencing factors. This was a cross-sectional study in which 506 patients hospitalized in general surgery from February to June 2020 were selected. Information on patients' age, gender, ethnicity, body mass index, intake of vegetables, fruits, meat, and spicy foods, sleep, smoking, alcohol consumption, time of defecation, and mode of defecation was collected through questionnaires, and the factors affecting functional constipation were analyzed using binary logistic regression models; among 506 patients, 254 had FC, with a prevalence of 50.19%. Among the clinical symptoms of FC, the most common ones were straining to defecate (83.85%) and lumpy or hard stools (81.80%). Univariate analysis revealed statistically significant differences in family history of constipation (P = .033), sedentary (P = .004), self-care ability (P = .001), body mass index (P = .013), defecation time (P < .0001), spicy food intake in dietary preference (P = .001), age (P = .004), and education level (P = .016), and binary logistic regression analysis showed that defecation time and spicy food consumption were independent influencing factors of FC. For hospitalized elderly people, regular morning defecation and not eating spicy foods can more helpful to slow the occurrence of functional constipation.

Gender and Race/Ethnicity dynamics in anesthesiology mentorship: results of a European survey

BackgroundMentorship is crucial to career advancement, medical education, and psychosocial support, especially for women and minorities. Although anesthesia mentoring programs have shown promise, there are no survey data regarding mentor-mentee relationship dynamics. This study aimed to explore the dynamics of the anesthesia mentor/mentee relationship.MethodsA open cross-sectional web-based survey was distributed by the European Society of Anesthesiology and Intensive Care and European Society of Regional Anesthesia to European anesthesiologists. Participation was anonymous and consent was obtained. The study evaluated responses relating to preferences, facilitators, and barriers to mentorship relationships along with sociodemographic information.ResultsIn total, 543 anesthesiologists responded to the survey, and 406 (111 mentees, 49 mentors, 193 both, 53 neither) responded to questions regarding mentorship. 184 anesthesiologists identified as woman and 22 as other genders (non-binary, transgender, gender-fluid, and self-described gender). Moreover, 250 anesthesiologists identified as white. Both mentors and mentees indicated that personal compatibility was the most important factor for successful mentorship. Barriers to mentorship included time consumption and perceived lack of interest from the mentor and mentee. Both mentors and mentees benefited from this relationship. The former reported feeling helpful, and the latter supported the development of clinical skills. The mentors indicated that their participation was important for protecting against burnout/exhaustion and impostor syndrome. Participants reported a preference for mentorship programs organized at the departmental level, offered at the start of the anesthesiology education curricula. Women were more likely to feel a ‘lack of interest’ in mentoring them as a barrier (OR = 2.49, P = 0.033). Gender was a barrier for mentors of other genders (OR = 23.9, P = 0.0027) and ethnicity (OR = 48.0, P = 0.0023). White mentees found gender (OR = 0.14, P = 0.021) and ethnicity (OR = 0.11, P = 0.048) to be less important barriers to successful mentorship relationship.ConclusionWhen possible, programs should prioritize matching mentors and mentees based on personal compatibility and experience in the mentee’s area of interest. Addressing the perceived lack of interest in mentoring is essential for promoting diversity, equality, and inclusion within anesthesiology, as well as and uplifting women and minorities.Trial registrationClinicaltrials.gov identifier: NCT05968339, First posted (01/08/2023).

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

Synergistic nanocoating with layer-by-layer functionalized PCL membranes enhanced by manuka honey and essential oils for advanced wound healing

Chronic wounds represent a significant global health concern, statistically impacting 1–2% of the population in developed countries throughout their lifetimes. These wounds cause considerable discomfort for patients and necessitate substantial expenditures of time and resources for treatment. Among the emerging therapeutic approaches, medicated dressings incorporating bioactive molecules, including natural compounds, are particularly promising. Hence, the objective of this study was to develop novel antimicrobial dressings for wound treatment. Specifically, polycaprolactone membranes were manufactured using the electrospinning technique and subsequently coated with natural polyelectrolytes (chitosan as a polycation and a mixture of manuka honey with essential oils nanoemulsions as a polyanion) employing the Layer-by-Layer assembly technique. Physico-chemical and morphological characterization was conducted through QCM-D, FTIR-ATR, XPS, and SEM analyses. The results from SEM and QCM-D demonstrated successful layer deposition and coating formation. Furthermore, FTIR-ATR and XPS analyses distinguished among different coating compositions. The coated membranes were tested in the presence of fibroblast cells, demonstrating biocompatibility and expression of genes coding for VEGF, COL1, and TGF-β1, which are associated with the healing process (assessed through RT-qPCR analysis). Finally, the membranes exhibited excellent antibacterial activity against both Staphylococcus aureus and Pseudomonas aeruginosa, with higher bacterial strain inhibition observed when cinnamon essential oil nanoemulsion was incorporated. Taken together, these results demonstrate the potential application of nanocoated membranes for biomedical applications, such as wound healing.

Assessment on the influence of FDM process parameters on the mechanical properties of PLA samples

Abstract The manufacturing of functional parts from the fused deposition modeling 3D printer is limited due to poor mechanical properties, low surface finish, and huge production time. During 3D printing of prototypes, the time consumption of printing and mechanical properties of the model is directly proportional. The fused deposition modeling process parameters significantly impact the quality of printed parts. The prime objective of this research is to optimize the process parameters of a 3D printing machine (wall thickness, infill percentage, infill pattern, and infill speed) using polylactic acid material to enhance the mechanical properties with less printing time of the 3D printing model. The ultimate tensile strength and elongation of the 3D printed samples are used to evaluate the mechanical properties of the various slicing process parameters as per the standards. From the results, it is evident that the mechanical properties of the 3D printed materials increased significantly. The magnitude of ultimate tensile strength is reported as 39.972 MPa with a printing time of 28 min. From close observation, it is evident that lower infill speeds with gyroid infill pattern produce the highest ultimate tensile strength of all combinations. Also, optimal results are derived at higher wall thickness and higher infill percentage. In addition to that, finite element analysis was carried out for different infill percentages of the specimen to validate the actual result.