Multiple Time Research Articles

Modified MF-DFA Model Based on LSSVM Fitting

This paper proposes a multifractal least squares support vector machine detrended fluctuation analysis (MF-LSSVM-DFA) model. The system is an extension of the traditional MF-DFA model. To address potential overfitting or underfitting caused by the fixed-order polynomial fitting in MF-DFA, LSSVM is employed as a superior alternative for fitting. This approach enhances model accuracy and adaptability, ensuring more reliable analysis results. We utilize the p model to construct a multiplicative cascade time series to evaluate the performance of MF-LSSVM-DFA, MF-DFA, and two other models that improve upon MF-DFA from recent studies. The results demonstrate that our proposed modified model yields generalized Hurst exponents h(q) and scaling exponents τ(q) that align more closely with the analytical solutions, indicating superior correction effectiveness. In addition, we explore the sensitivity of MF-LSSVM-DFA to the overlapping window size s. We find that the sensitivity of our proposed model is less than that of MF-DFA. We find that when s exceeds the limited range of the traditional MF-DFA, h(q) and τ(q) are closer than those obtained in MF-DFA when s is in a limited range. Meanwhile, we analyze the performances of the fitting of the two models and the results imply that MF-LSSVM-DFA achieves a better outstanding performance. In addition, we put the proposed MF-LSSVM-DFA into practice for applications in the medical field, and we found that MF-LSSVM-DFA improves the accuracy of ECG signal classification and the stability and robustness of the algorithm compared with MF-DFA. Finally, numerous image segmentation experiments are adopted to verify the effectiveness and robustness of our proposed method.

From Kyoto to Paris and beyond: A deep dive into the green shift

This study investigates the impact of the Kyoto Protocol and its associated regime, the Paris Agreement, on environmental outcomes and the green transition. We employ a difference-in-differences (DD) specification for country-level panel data from 1995 to 2018. The DD estimates suggest that the Kyoto Protocol of 2005 significantly reduced CO2 emissions and natural resources rents by around 20 percent and decreased the quantity of non-renewable electricity generated by about 36 percent, relative to the pre-Kyoto period. The protocol also led to increases in renewable energy consumption and generation of 18 percent and 60 percent, respectively. Robustness checks using system GMM estimates confirm that the effects of the Kyoto Protocol are likely not biased because of dynamic endogeneity from simultaneity problems or confounding trends. For the Paris Agreement, given documented issues in interpreting the average treatment effects using standard two-way fixed effects (TWFE), we leverage a DD specification with multiple treatment timings. We find no statistically significant effect of the Paris Agreement 2016 on the green transition except that it contributes to reducing CO2 emissions. The absence of a significant impact from the ad hoc Paris Agreement on most outcomes points to the need for greater effort in implementing environmental treaties.

PreVR: Variable-Distance Previews for Higher-Order Disocclusion in VR.

The paper introduces PreVR, a method for allowing the user of a VR application to preview a virtual environment (VE) around any number of corners. This way the user can gain line of sight to any part of the VE, no matter how distant or how heavily occluded it is. PreVR relies on a multiperspective visualization that implements a higher-order disocclusion effect with piecewise linear rays that bend multiple times as needed to reach the visualization target. PreVR was evaluated in a user study ($\mathrm{N}=88$) that investigates four points on the VR interface design continuum defined by the maximum disocclusion order $\delta$. In a first control condition (CC0), $\delta=0$, corresponds to conventional VR exploration with no preview capability. In a second control condition (CC1), $\delta=1$, corresponds to the prior art approach of giving the user a preview around the first corner. In a first experimental condition (EC3), $\delta=3$, so PreVR provided up to third-order disocclusion. In a second experimental condition (ECN), $\delta$ was not capped, so PreVR could provide a disocclusion effect of any order, as needed to reach any location in the VE. Participants searched for a stationary target, for a dynamic target moving on a random continuous trajectory, and for a transient dynamic target that appeared at random locations in the maze and disappeared 5s later. The study quantified VE exploration efficiency with four metrics: viewpoint translation, view direction rotation, number of teleportations, and task completion time. Results show that the previews afforded by PreVR bring a significant VE exploration efficiency advantage. ECN outperforms EC3, CC1, and CC0 for all metrics and all tasks, and EC3 frequently outperforms CC1 and CC0.

Across Time and Space: Senju ’s Approach for Scaling Iterative Stencil Loop Accelerators on Single and Multiple FPGAs

Stencil-based applications play an essential role in high-performance systems as they occur in numerous computational areas, such as partial differential equation solving. In this context, Iterative Stencil Loops (ISLs) represent a prominent and well-known algorithmic class within the stencil domain. Specifically, ISL-based calculations iteratively apply the same stencil to a multi-dimensional point grid multiple times or until convergence. However, due to their iterative and intensive nature, ISLs are highly performance-hungry, demanding specialized solutions. Here, Field Programmable Gate Arrays (FPGAs) represent a valid architectural choice as they enable the design of custom, parallel, and scalable ISL accelerators. Besides, the regular structure of ISLs makes them an ideal candidate for automatic optimization and generation flows. For these reasons, this article introduces Senju , an automation framework for the design of highly parallel ISL accelerators targeting single-/multi-FPGA systems. Given an input description, Senju automates the entire design process and provides accurate performance estimations. The experimental evaluation shows remarkable and scalable results, outperforming single- and multi-FPGA literature approaches under different metrics. Finally, we present a new analysis of temporal and spatial parallelism trade-offs in a real-case scenario and discuss our performance through a single- and novel specialized multi-FPGA formulation of the Roofline Model.

Continuous palm cooling’s effect on heat transfer and physiology

BACKGROUND: Excess heat accrual is perhaps the body’s most dangerous exercise-induced stressor. While palm cooling uses conduction to reduce body temperatures, to date the volume of heat transferred by this treatment resulting from exercise is unknown. OBJECTIVE: Asses continuous palm cooling’s impact on heat transfer and physiology. METHODS: Thirty-one subjects did two workouts; one with, and one without, palm cooling. Workouts entailed consecutive stages of submaximal pedaling against prescribed workloads. Gloves were worn at workouts; for palm cooling 10.6∘C gel packs were inserted into gloves at the workout’s start and conclusion. Heart rate, auditory canal and palm skin temperatures, and heat transfer across the palm were collected. Data were obtained pre-exercise, at the end of a warm-up, and at multiple times during the 25 minutes of pedaling and 30 minutes of recovery. RESULTS: Auditory canal temperatures had a significant treatment effect (palm cooling < non-palm cooling). Palm skin temperature had an interaction, with higher non-palm cooling values at multiple times. Conversely, heat transfer also produced an interaction, but palm cooling had significantly higher values at multiple times. Heat transfer was 32% higher for the palm cooling workout. CONCLUSIONS: Continuous palm cooling produced significantly higher heat transfer from submaximal exercise.

Enhancing the transferability of adversarial attacks with diversified input strategies

Abstract Adversarial examples are often used to test and evaluate the security and robustness of image classification models. Though adversarial attacks under white-box setting can achieve a high attack success rate, due to overfitting, the success rate of black-box attacks is relatively low. To this end, this paper proposes diversified input strategies to improve the transferability of adversarial examples. In this method, various transformation methods are applied to randomly transform the original image multiple times, thereby generating a batch of transformed images. Then, in the process of back-propagation, the loss function gradient of the transformed images is calculated, and a weighted average of the obtained gradient values is performed to generate adversarial perturbation, which is iteratively added to the original image to generate adversarial examples. Meanwhile, by increasing the variety of data augmentation transformation types and the number of input images, the proposed method effectively alleviates overfitting and improves the transferability of adversarial examples. Extensive experiments on the ImageNet dataset indicate that the proposed method can perform black-box attacks better than benchmark methods, with an average of 97.2% success rate attacking multiple models simultaneously.

Evaluating Recovery After Two and Three Repeated Concussions Using Growth Curves.

The results of prior research concerning the effects of repeated concussions have been mixed. The aim of this study was to evaluate how concussion outcomes and presentation changed within patients who were evaluated at a concussion specialty clinic multiple times with a concussion. Subjects included 202 patients (54% male) aged 10-21 years (M = 13.17) who presented to a specialty concussion clinic for two and three concussions (77% sport-related) and were followed through formal clearance. First, growth curve models were estimated to determine recovery time and initial symptom burden across the multiple injuries. Second, covariates were added to these models to evaluate which demographic, risk factor, or injury variables predicted any change that did occur in evaluation or outcome variables. Models indicated that each subsequent concussion linearly resulted in significantly fewer days to recovery (-4.62 days, p < 0.047) across three concussions, and significantly lower (and linear) symptom scores on the post-concussion symptom scale (PCSS) (-2.16, p = 0.05). More severe presentation (i.e., days to recovery; higher symptom score) was significantly associated (-.62, p = 0.005) with greater improvement in recovery time (-.62, p = 0.005) and symptom burden (-.56, p < 0.001) at subsequent injuries. No covariates were significantly associated with improvement (or lack thereof) at subsequent injuries. This study adds to evidence suggesting multiple injuries is not associated with protracted recovery at subsequent injuries, in the context of treatment and full clearance for each injury at a multi-disciplinary clinic.

Health Risks Assessment of Heavy Metal Concentration in Cultured Chanos chanos (Bangus) and Scylla serrata (Mudcrab) in Selected Municipalities in Northern Samar

One of the common problem in fishponds is heavy metal contamination. Though there are some heavy metal elements that are naturally occurring, but due to human activities, their concentration goes beyond what is normal. In this study, health risks analysis using Estimated Daily Intake (EDI), Total Hazard Quotient (THQ), Target Cancer Risk (TCR) were done to assess if the quantities of the heavy metals, such as: Arsenic, Cadmium, Chromium, Lead, and Mercury, impose risks to consumer. Arsenic had the highest concentration among all other heavy metals in crab aligue, having 46.83 mg/kg. The consumption of bangus meat may result in an EDI that is greater than PTDI, especially for Arsenic [15.22731-18.10317 μg kg−1 BW d−1]. Similarly, consuming crab aligue may also result to a high EDI for Arsenic [2.48197-5.27841μg kg−1 BW d−1]. THQ was also evaluated as well as the sum of individual heavy metal values which is the Hazard Index (HI) that exceeded to 1 multiple times. In terms of TCR levels, all of the heavy metals exceeded the acceptable limit for cancer risks. Shapiro-Wilk Test had shown non-normal distribution of data for EDI, THQ, and TCR. Spearman’s Correlation Test, meanwhile, suggested that there is a significant relationship between the quantities of heavy metals in bangus meat and crab aligue as well as EDI, THQ, and TCR. In general, based on the health risks assessments (EDI, THQ, and TCR), Arsenic, an established carcinogen, can be the greatest contributor in developing risks and disease, while the varying concentration of Chromium and Cadmium in the samples may also pose risks to consumers. This implies that strict management measures should be implemented to mitigate or lessen the discharge of these heavy metals in the aquatic systems.

Lookup Table-Based Design of Scalar Multiplication for Elliptic Curve Cryptography

This paper is aimed at using a lookup table method to improve the scalar multiplication performance of elliptic curve cryptography. The lookup table must be divided into two polynomials and requires two iterations of point doubling operation, for which negation operations are needed. It is well known that an inversion operation requires a lot of multiplication time. The advantage of this paper is that we are able to reduce one inverse element calculation for this problem and also improve the basic operations of finite fields through segmentation methods. If the normal basis method is used in the design of the inverse element operation, it must be converted to the normal basis through the standard basis. However, the conversion process requires a lot of matrix operations. Though the anti-element operation has good speed performance, it also increases the computational complexity. Using number theory and grouping methods will greatly improve the performance of inverse element operations. With application of the two-time point doubling operation in the hardware implementation, the developed approach reduces the computing time by 48% as compared with the conventional approach. The computational time of the scalar multiplication using the presented method is further improved by 67% over the traditional algorithm with only an area increase of 12%. Finally, the proposed lookup table-based technique can be utilized for software and hardware implementation, as the developed arithmetic operations are simple and are consistent in their execution.

Multi-Think Transformer for Enhancing Emotional Health

The smart healthcare system not only focuses on physical health but also on emotional health. Music therapy, as a non-pharmacological treatment method, has been widely used in clinical treatment, but music selection and generation still require manual intervention. AI music generation technology can assist people in relieving stress and providing more personalized and efficient music therapy support. However, existing AI music generation highly relies on the note generated at the current time to produce the note at the next time. This will lead to disharmonious results. The first reason is the small errors being ignored at the current generated note. This error will accumulate and spread continuously, and finally make the music become random. To solve this problem, we propose a music selection module to filter the errors of generated note. The multi-think mechanism is proposed to filter the result multiple times, so that the generated note is as accurate as possible, eliminating the impact of the results on the next generation process. The second reason is that the results of multiple generation of each music clip are not the same or even do not follow the same music rules. Therefore, in the inference phase, a voting mechanism is proposed in this paper to select the note that follow the music rules that most experimental results follow as the final result. The subjective and objective evaluations demonstrate the superiority of our proposed model in generation of more smooth music that conforms to music rules. This model provides strong support for clinical music therapy, and provides new ideas for the research and practice of emotional health therapy based on the Internet of Things.

Interpreting biogeochemical processes through the relationship between total alkalinity and dissolved inorganic carbon: Theoretical basis and limitations

AbstractThe marine carbonate system is influenced by anthropogenic CO2 uptake, biogeochemical processes, and physical changes that involve freshwater input and removal. Two frequently used parameters to quantify seawater carbonate system are total alkalinity (TA) and total dissolved inorganic carbon (DIC). To account for the physical changes, both TA and DIC are usually normalized to a reference salinity (i.e., nTA and nDIC), and then the relationship between nTA and nDIC is used to identify major biogeochemical processes that regulate the carbonate system, based on process‐specific reaction stoichiometry. However, the theoretical basis of this interpretation has not been holistically examined. In this study, we validated this method under idealized conditions and discussed the associated assumptions and limitations. Furthermore, we applied this method to interpret field TA and DIC data from a lagoonal estuary in the northwestern Gulf of Mexico. Our results demonstrated that evaluating field data that encompass multiple stations and time periods could be problematic. In addition, various combinations of biogeochemical processes can lead to the same nTA–nDIC relationship, even though the relative importance of each individual process may vary significantly. Therefore, the stoichiometric relationship relying solely on TA and DIC data is not a definitive approach for uncovering dominant biogeochemical processes. Instead, measurements of process‐specific parameters are necessary.

A multi-timescale optimization method for integrated energy systems with carbon capture and accounting

As the goal of “double carbon”, integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system.

DF-ParPINN: parallel PINN based on velocity potential field division and single time slice focus

Modified Benney-Luke equation (mBL equation) is a three-dimensional temporal-spatial equation with complex structures, that is a high-dimensional partial differential equation (PDE), it is also a new equation of the physical ocean field, and its solution is important for studying the internal wave-wave interaction of inclined seafloor. For conventional PDE solvers such as the pseudo-spectral method, it is difficult to solve mBL equation with both accuracy and speed. Physics-informed neural network (PINN) incorporates physical prior knowledge in deep neural networks, which can solve PDE with relative accuracy and speed. However, PINN is only suitable for solving low-dimensional PDE with simple structures, and not suitable for solving high-dimensional PDE with complex structures. This is mainly because high-dimensional PDEs usually have complex structures and high-order derivatives and are likely to be high-dimensional non-convex functions, and the high-dimensional non-convex optimization problem is an NP-hard problem, resulting in the PINN easily falling into inaccurate local optimal solutions when solving high-dimensional PDEs. Therefore, we improve the PINN for the characteristics of mBL equation and propose “DF-ParPINN: parallel PINN based on velocity potential field division and single time slice focus” to solve mBL equation with large amounts of data. DF-ParPINN consists of three modules: temporal-spatial division module of overall velocity potential field, data rational selection module of multiple time slices, and parallel computation module of high-velocity fields and low-velocity fields. The experimental results show that the solution time of DF-ParPINN is no more than 0.5s, and its accuracy is much higher than that of PINN, PIRNN, cPINN, and DeepONet. Moreover, the relative error of DF-ParPINN after deep training 1000000 epochs can be reduced to less than 0.1. The validity of DF-ParPINN proves that the improved PINN also can solve high dimensional PDE with complex structures and large amounts of data quickly and accurately, which is of great significance to the deep learning of the physical ocean field.

Individuals' experiences in genetic counseling and predictive testing for familial amyotrophic lateral sclerosis.

As clinical genetic testing in the amyotrophic lateral sclerosis (ALS) diagnostic setting increases, the identification of at-risk family members has also expanded. No practice guidelines specifically for predictive genetic testing exist, and few studies about the psychological impacts of testing in this subgroup have occurred, limiting the ability to tailor recommendations and counseling in this community. We surveyed asymptomatic individuals at risk for inheriting an ALS-associated gene mutation. The 80-question survey was designed using a combination of validated measures (General Anxiety Disorder; FACToR; Decision Regret Scale) and original items. Ninety participants completed the survey, including those who completed predictive genetic testing (N = 42) and those who did not (N = 48). Gene positive individuals experienced greater negativity, uncertainty, and overall psychological impairment (p = 0.002; p < 0.001; p = 0.001). Individuals who had not undergone testing reported thinking about their risk multiple times per day and experiencing more decisional regret than those who tested (p = 0.006). In terms of decision-making, being prepared for potential clinical drug trials was a more important potential benefit among those who underwent testing (p = 0.026). Participants valuing preparedness for clinical drug trials supports the concept that genetic testing for ALS will increase as research in gene-targeted therapeutics progresses. This study describes factors relevant to the genetic testing decision-making process and adaptation to results from the perspective of at-risk individuals, which can ultimately guide genetic counseling practice in this population.

Advancing randomized controlled trial methodologies: The place of innovative trial design in eating disorders research.

Randomized controlled trials can be used to generate evidence on the efficacy and safety of new treatments in eating disorders research. Many of the trials previously conducted in this area have been deemed to be of low quality, in part due to a number of practical constraints. This article provides an overview of established and more innovative clinical trial designs, accompanied by pertinent examples, to highlight how design choices can enhance flexibility and improve efficiency of both resource allocation and participant involvement. Trial designs include individually randomized, cluster randomized, and designs with randomizations at multiple time points and/or addressing several research questions (master protocol studies). Design features include the use of adaptations and considerations for pragmatic or registry-based trials. The appropriate choice of trial design, together with rigorous trial conduct, reporting and analysis, can establish high-quality evidence to advance knowledge in the field. It is anticipated that this article will provide a broad and contemporary introduction to trial designs and will help researchers make informed trial design choices for improved testing of new interventions in eating disorders. PUBLIC SIGNIFICANCE: There is a paucity of high quality randomized controlled trials that have been conducted in eating disorders, highlighting the need to identify where efficiency gains in trial design may be possible to advance the eating disorder research field. We provide an overview of some key trial designs and features which may offer solutions to practical constraints and increase trial efficiency.

How does the urban built environment affect dockless bikesharing-metro integration cycling? Analysis from a nonlinear comprehensive perspective

The multi-modal combination of "dockless bikesharing (DBs) + metro" can effectively addresses the "last mile" challenge, and its importance to improve the quality of metro integration and residents' commuting efficiency is becoming increasingly prominent. As one of the important determining factors of transportation, the urban built environment directly affects the integration travel behavior of residents. To better understand this issue, it is necessary to conduct in-depth research on the influence mechanism of urban built environment on DBs-metro integration cycling (DBsMIC), and reveal its complex nonlinear characteristics and spatial heterogeneity. Therefore, taking Shenzhen as a case study, this study first introduced the OLS regression model to detect the significant built environmental impact factors of DBsMIC. Secondly, the random forest regression model was used to analyze its nonlinear influence. Finally, aiming at the problem of spatial non-stationarity of influencing effects, the spatial heterogeneity is analyzed from both global and local perspectives based on the MGWR model. The results show that the significant impact indices of the built environment of the integration cycling volume vary in different time periods. Overall, seven factors such as road and residential density have a significant impact on DBsMIC. Among them, the job-housing balance and the bus line density have a negative impact, while the rest have a positive impact. Meanwhile, the nonlinear effect of the built environment on DBsMIC is obvious, and there is an obvious marginal effect. In addition, the nonlinear impact of the built environment on DBsMIC in different time periods has spatial heterogeneity and spatial marginal effects. For example, the influence of road density shows a global positive correlation effect in multiple time periods, and the promotion effect increases from southwest to northeast. This study can provide auxiliary decision-making support for the construction of an efficient, green and sustainable urban transportation system according to local conditions.

Investigation of anti-adhesion ability of 8-arm PEGNHS-modified porcine pericardium

In post-adhesion surgery, there is a clinical need for anti-adhesion membranes specifically designed for the liver, given the limited efficacy of current commercial products. To address this demand, we present a membrane suitable for liver surgery applications, fabricated through the modification of decellularized porcine pericardium with 20 KDa hexaglycerol octa (succinimidyloxyglutaryl) polyoxyethylene (8-arm PEGNHS). We also developed an optimized modification procedure to produce a high-performance anti-adhesion barrier. The modified membrane significantly inhibited fibroblast cell adherence while maintaining minimal levels of inflammation. By optimizing the modification ratio, we successfully controlled post-adhesion formation. Notably, the 8-arm PEG-modified pericardium with a molar ratio of 5 exhibited the ability to effectively prevent post-adhesion formation on the liver compared to both the control and Seprafilm®, with a low adhesion score of 0.5 out of 3.0. Histological analysis further confirmed its potential for easy separation. Furthermore, the membrane demonstrated regenerative capabilities, as evidenced by the proliferation of mesothelial cells on its surface, endowing anti-adhesion properties between the abdominal wall and liver. These findings highlight the membrane’s potential as a reliable barrier for repeated liver resection procedures that require the removal of the membrane multiple times.

Fabrication of 3D-Printed Hydrocortisone Triple Pulsatile Tablet Using Fused Deposition Modelling Technology.

Hydrocortisone (HC) is the optimal drug for adolescents diagnosed with congenital adrenal hyperplasia (CAH). Because traditional dosage regimens HC are inconvenient, our study used fused deposition modeling (FDM) three-dimensional (3D) printing technology to solve the problems caused by traditional preparations. First, we designed a core-shell structure tablet with an inner instant release component and an outer delayed release shell. The instant release component was Kollicoat IR: glycerol (GLY): HC = 76.5:13.5:10. Then, we used Affinisol® HPMC 15LV to realize delayed release. Furthermore, we investigated the relationship between the thickness of the delayed release shell and the delayed release time, and an equation was derived through binomial regression analysis. Based on that equation, a novel triple pulsatile tablet with an innovative structure was devised. The tablet was divided into three components, and the drug was released multiple times at different times. The dose and release rate of the tablets can be adjusted by modifying the infill rate of the printing model. The results indicated that the triple pulsatile tablet exhibited desirable release behavior in vitro. Moreover, the physicochemical properties of the drug, excipients, filaments, and tablets were characterized. All these results indicate that the FDM 3D printing method is a convenient technique for producing preparations with intricate structures.

Comparison of the effectiveness of project-based 6E learning and problem-based quantum learning: Solomon four-group design

PurposeResearchers have previously utilized the project-based 6E learning model and the problem-based quantum learning model in various courses, such as the instructional principles and methods course and the character and values education course. These models were evaluated for their impact on students in different subjects, including developing skills, values, democracy perceptions, attitudes towards cooperative learning, metacognitive thinking skills and teacher self-efficacy perceptions. In 2023, Ökmen, Sahin and Kiliç reported positive outcomes, while Sahin and Kiliç reported similar findings in 2023a, 2023b and 2023c. There has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future. Specifically, this research aims to compare the effects of the project-based 6E learning model and problem-based quantum learning model on critical thinking and academic literacy.Design/methodology/approachThis research employed the Solomon four-group experimental design to assess the efficacy of the applications. Prior knowledge and experience of the participants were evaluated through pretests. However, it should be noted that pretests may impact posttest scores either positively or negatively. For instance, participants taking the test multiple times may become more interested or attentive to the subject matter. The Solomon four-group design was deemed appropriate to analyze the influence of pretesting. This design enables the investigation of the application effect, pretest effect and interactive effect of pretest and application (van Engelenburg, 1999).FindingsIt was concluded that the project-based 6E learning model was effective in developing critical thinking in students, but not significantly. It was concluded that the problem-based quantum learning model significantly improved students' critical thinking skills. It was concluded at the end of the study that the project-based 6E learning model notably enhanced students' academic literacy. It was concluded that the problem-based quantum learning model had a significant positive impact on students' academic literacy. According to research, it has been determined that the problem-based quantum learning model is superior in enhancing critical thinking abilities compared to the project-based 6E learning model. Nevertheless, there seems to be no detectable disparity in the academic literacy advancement of pupils between the problem-based quantum learning model and the project-based 6E learning model.Originality/valueThere has been no investigation into how the models affect students' critical thinking and academic literacy. This study seeks to determine the impact of both models on these skills, gain more insight into their effectiveness and determine which is more beneficial. The results will guide the decision-making process for the character and values education course and other courses in the future.

Abundant soliton solution for the time-fractional stochastic Gray-Scot model under the influence of noise and M-truncated derivative

In this study, we investigate the abundant soliton solutions for the time-fractional stochastic Gray-Scot (TFSGS) model analytically. The Gray-Scot model is considered under the influence of M-truncated derivative and multiplicative time noise. This is a reaction–diffusion chemical concentration model that explains the irreversible chemical reaction process. The M-truncated derivative is applied for the fractional version while Brownian motion is taken in the sense of time noise. The novel mathematical technique is used to obtain the abundant families of soliton solutions. These solutions are explored in the form of shock, complicated solitary-shock, shock-singular, and periodic-singular types of single and combination wave structures. During the derivation, the rational solutions also appear. Moreover, we use MATHEMATICA 11.1 tools to plot our solutions and exhibit several three-dimensional, two-dimensional, and their corresponding contour graphs to show the fractional derivative and Brownian motion impact on the soliton solutions of the TFSGS model. We show that the TFDGS model solutions are stabilized at around zero by the multiplicative Brownian motion. These wave solutions represent the chemical concentrations of the reactants.