Parallel Method Research Articles

Physical and biological characteristics of eco‐friendly films added with Cinnamomum zeylanicum extract for packaging materials

SummaryThe effects of Cinnamomum zeylanicum extract (CZE) at different rations (0%, 1%, 3% and 5%) on gelatin (GL)–polyvinyl alcohol (PVA) blend films were evaluated in relation to the structural, tensile strength, physical, antimicrobial and antioxidant properties of the films in this study. The results showed that water barriers of the film samples decreased, but the UV‐blocking property increased and became opaque at the highest GLPVA5 film. The tensile strengths of GLPVA, GLPVA1, GLPVA3 and GLPVA5 films were 1.21, 1.78, 1.62 and 1.95 MPa, respectively. The antimicrobial effect of these films was determined against Escherichia coli, Enterococcus faecalis, Salmonella typhimurium and Bacillus cereus according to the parallel stick method. Antioxidant properties of cinnamon extract were investigated using 2,2‐diphenyl‐1‐picryl‐hydrazil (DPPH) methods. The % inhibition of DPPH was found to be 82.00. No antibacterial effect was detected for B.cereus and E. faecalis at 0%, 1%, 3% and 5%. While no activity was observed in 0%, 1% and 3% cinnamon, 7.5 and 3.0 mm zones were detected in S. typhimirium and E. coli, respectively, in 5% cinnamon. The results suggest that the film samples could be used in a potential packaging with antioxidant and antibacterial properties.

Increased Confidence and Deeper Understanding of Fatigue Following Participation in Fatigue Education and Management Education in Systemic Sclerosis: A Mixed Methods Evaluation of a Virtual Intervention.

No fatigue-specific programs exist for people with systemic sclerosis (SSc) despite the burden of fatigue and negative impact on daily activities. This study used a convergent parallel mixed methods design to evaluate the impact of an adapted virtual intervention, Fatigue and Activity Management Education in Systemic Sclerosis (FAME-iSS), in the United States. Eighteen people with SSc participated in three separate six-week FAME-iSS programs. Participants completed the modified Fatigue Impact Scale (m-FIS), the Self-Efficacy for Performing Energy Conservation Strategies Assessment (SEPESCA), the Patient-Reported Outcomes Measurement Information System (PROMIS) Self-Efficacy for Managing Symptoms, and the Hospital Anxiety and Depression Scale (HADS) before, immediately after, and three months post intervention. Data were analyzed using descriptive and nonparametric inferential statistics. Participants' perceptions of the program and their use of fatigue management strategies were qualitatively analyzed using content analysis. Eighty-nine percent of participants were women with a mean ± SD age of 52.0 ± 11.6 years and a mean ± SD disease duration of 13.7 ± 14.5 years, and more than 70% had a college degree. Significant improvements were observed for self-efficacy on the PROMIS Self-Efficacy for Managing Symptoms (P = 0.002) and SEPESCA (P = 0.016) immediately post intervention, which continued to significantly improve up to the three-month follow-up (P = 0.006 and 0.035, respectively). Significant improvements were also observed for the m-FIS between baseline and the three-month follow-up (P = 0.029). Participants reported a deeper understanding of fatigue and that they liked sharing strategies and experiences with each other along with the facilitator, citing that "there was a power in our group because we had a common condition." FAME-iSS resulted in improvements in the impact of fatigue and self-efficacy for managing symptoms and performing energy conservation strategies. Feedback was positive, and the virtual format allowed for greater accessibility and sharing of strategies.

Knowledge, perceptions and practices towards blood donation among undergraduate medical students in an upcountry Ugandan university: A mixed methods study

ABSTRACT Background Due to the different factors affecting the maintenance of a constant supply of human blood in health facilities, this study aimed at exploring the knowledge, perceptions, and practices towards blood donation among under graduate medical students at Busitema University Faculty of Health Sciences (BUFHS). Methods Using a convergent parallel mixed methods study design, 384 students were recruited into the study. Questionnaires and focused group discussions were used to collect the data. Stata version 15.0 and thematic analysis were used to analyze quantitative and qualitative data respectively. Results Of the respondents, 151 (39.4%) had ever donated blood and 50.4% had good knowledge about blood donation. The commonest motivating factor towards blood donation was “to save someone’s life”. Factors associated with practices towards blood donation included: religion, year 2 of study and prior blood donation history. Being Jehovah’s Witness reduced the willingness to donate blood by 88% compared to other religions. Thematic analysis revealed four themes namely; students’ experiences and thoughts, driving forces to donate blood, fears and misconceptions, and suggestions to increase blood donors. Conclusion The proportion of undergraduate medical students who had adequate level of knowledge about blood donation was moderately high, however, some of their perceptions towards it were unconventional. To ensure safe and adequate blood supply, design of strategies and tailored programs that promote blood donation is highly recommended..

Family members’ and friends’ reports after standard and intensive group treatment for social communication difficulties following acquired brain injury – a mixed method approach

ABSTRACT Objective To examine family members/friends’ perception of change following standard and intensive group interactive structured treatment (GIST) for persons with social communication difficulties (SCDs). Methods A parallel mixed methods design combining data from questionnaires, the Goal Attainment Scale, and interviews. Participants Forty-nine adult (>18 years) family members/friends (69.4% female) of persons with SCDs and acquired brain injury (ABI) (cohabitating partner, 53.1%; other relatives, 30.6%; friends, 16.3%). Intervention Standard GIST consisted of 12 weekly outpatient sessions (2.5 h). Intensive GIST consisted of 4 weeks of inpatient rehabilitation (2 × 3 days/week, 2 × 4 days/week). Both treatments included 3- and 6-month follow-ups. Main outcome measures Quantitative measures (n = 49) included the following informant reports: Latrobe Communication Questionnaire (LCQ), Social Communication Skills Questionnaire-Adapted (SCSQ-A), Behavior Rating Inventory for Executive Functions-Adult version (BRIEF-A), and Goal Attainment Scale. The qualitative measure (n=9) was an interview performed at 6-month follow-up. Results Converging results were found, suggesting a perceived positive change over time for both standard and intensive GIST, with no substantial differences reported between the treatments. Conclusions Family members/friends reported improved social communication skills of the persons with SCDs following standard and intensive GIST. However, the integration of data showed the complexity of assessing SCDs and the need for further investigation.

Exploring the Educational Value of an Immersive Virtual Reality Method Within a Continuing Education Module in Nursing: A Mixed Methods Study.

Few studies have explored the potential educational value of immersive 360° video in continuing education. This study explored the potential value of immersive 360° video as an acceptable educational method in a continuing education module in nursing. A convergent parallel mixed methods design was adopted. The setting was a nursing and midwifery school at a university. The 11 participants were RNs. Data were collected at three time points with surveys and focus groups. Participants found educational value in the triggering of a deep reflective process, supported by a subsequent classroom discussion. Further, there were nuances and complexities to be considered, with a need to tailor material toward high-acuity, low-frequency, or challenging clinical events when considering content. Immersive 360° videos are a potentially useful method for providing continuing education; however, the content must be tailored to students' learning needs. A reflective model may provide a valuable structure for discussions after the use of immersive 360° video. [J Contin Educ Nurs. 2024;55(5):261-268.].

Relationship between Idealized Influence (II) and Performance Contracts (PC) in Road Agencies in Kenya

Purpose: This study entailed a diagnostic analysis of the relationship between idealized influence and performance contracts in road agencies in Kenya. The objective of this research was to investigate the relationship between idealized influence and performance contracts in road agencies in Kenya. Operationalization of variables included idealized influence as an independent variable and performance contracts as a dependent variable. Theories that guided the study included transformational leadership theory and public management theory.
 Materials and Methods: Descriptive research design was used for the study and pragmatism was adopted as the philosophical framework for the research. Convergent parallel mixed method approach was adopted where data was collected through questionnaires for quantative data and structured interviews for qualitative data. The target population for the research was all staff members at top management and other officers in job group 6 (six) and above for the three road agencies in Kenya. These road agencies included Kenya National Highway Authority (KeNHA), Kenya Urban Roads Authority (KURA) and Kenya Rural Roads Authority (KeRRA). Data collected was analyzed through descriptive statistics, inferential analysis and hypothesis testing. The collected data was presented through tables and figures. Findings from the above analysis indicated that the performance contracts process and its outcomes in road agencies in Kenya could be predicted by idealized influence. The null hypothesis was rejected as p value was less than p<0.05 threshold of significance level and the study concluded that idealized influence has a significant relationship with performance contracts in road agencies in Kenya.
 Findings: The study rejected the null hypothesis that there is no significant relationship between idealized influence and performance contracts in road agencies in Kenya. The foregoing was supported by both results and findings from descriptive statistics, hypothesis tested and structured interviews conducted in the three road agencies. Leaders who were interviewed in these road agencies indicated that they also practiced integrity and leadership behaviors that could be emulated by their followers.
 Implications to Theory, Practice and Policy: The study thus supported the transformational leadership theory that was adopted for the study. Further, the study has bridged and addressed gaps on motivation, training and lack of leadership as identified in the statement of problem. This study has recommended that policy makers in road agencies should include idealized influence in their leadership and also align it with their strategies as detailed in strategic plans, performance contracts and individual workplans to enhance motivation, training and capacity building among employees of the studied organizations. The study also contributed to the transformational leadership theory as the was supported by the findings that leaders in these organizations should be role models and practice integrity in the process of performance contracting in road agencies. The study further recommended that policy makers in road agencies should entrench appropriate leadership (transformational leadership) during the process of performance contracting in road agencies.

Methods Used in YouTube for Teaching and Learning English Language: A Systematic Literature Review

Challenges await in giving and receiving English instructions. One way globalization proposes to create a fun, engaging, and fulfilling setting for English language education is the appropriate use of YouTube. This paper is a systematic review of literature on the use of YouTube in teaching and learning the English language. It also followed thematic content analysis via manual coding. Eligibility criteria were prepared before going to Google Scholar and ERIC databases to look for studies that could be used. PRISMA Flowchart was used to show how the researcher only had 46 articles for this study. PICO Framework was utilized to summarize the data gathered and aid the researcher in asking the question, “How was YouTube used for teaching and learning the English language? and “What specific skills are developed in the use of YouTube for teaching and learning English?”. The results constructed Horizontal, Intersecting, Vertical, Parallel, and Spiral methods for English instruction. It also revealed that speaking, pronunciation, listening, self-learning, comprehension, grammar, reading and writing improved. Future researchers can work on guidelines for selecting content or using YouTube for teaching and learning the English language.

Cell Patterning Using Magnetic-Archimedes Strategy.

Cell patterning, allowing precise control of cell positioning, presents a unique advantage in the study of cell behavior. In this protocol, a cell patterning strategy based on the Magnetic-Archimedes (Mag-Arch) effect is introduced. This approach enables precise control of cell distribution without the use of ink materials or labeling particles. By introducing a paramagnetic reagent to enhance the magnetic susceptibility of the cell culture medium, cells are repelled by magnets and arrange themselves into a pattern complementary to the magnet sets positioned beneath the microfluidic substrate. In this article, detailed procedures for cell patterning using the Mag-Arch-based strategy are provided. Methods for patterning single-cell types as well as multiple cell types for co-culture experiments are offered. Additionally, comprehensive instructions for fabricating microfluidic devices containing channels for cell patterning are provided. Achieving this feature using parallel methods is challenging but can be done in a simplified and cost-effective manner. Employing Mag-Arch-based cell patterning equips researchers with a powerful tool for in vitro research.

An active learning strategy of reliability-based design and optimization by parallel adaptive sequential importance candidate region method

An active learning strategy of reliability-based design and optimization by parallel adaptive sequential importance candidate region method

Parallel Calibration Method for Phased Array Based on Multiple-Period Time Modulation

Parallel Calibration Method for Phased Array Based on Multiple-Period Time Modulation

Large-Scale Molecular Dynamics Simulation Based on Heterogeneous Many-Core Architecture.

As the application of molecular dynamics (MD) simulations continues to evolve, the demand for accelerating large-scale simulation systems and handling of enormous simulation tasks is steadily increasing. We propose a parallel acceleration method for large-scale MD simulations based on Sunway heterogeneous many-core processors. This method integrates task scheduling, simulation calculations, and data storage, effectively tackling issues related to large-scale simulations and numerous simulation tasks. The task scheduling strategy flexibly handles tasks on various scales and enables parallel execution of multiple tasks. During the simulation calculations, we ported GROMACS to the Sunway architecture and accelerated the calculation of short-range forces through a heterogeneous processor. Our method achieves approximately 10-fold acceleration and 90% scalability when executing a single simulation task. When handling numerous simulation tasks, our method achieves parallel execution of all of the tasks with 90% scalability. By employing our method, we carried out 50 ns simulations on over 3000 distinct conotoxin structures individually within just 5 h. Additionally, we evaluated more than 200 protein-ligand complexes, and the simulation efficiency significantly exceeded that of midsized to small GPU clusters.

Performance optimization of machine learning-based image recognition algorithms for mobile devices based on the iOS operating system

Today, mobile devices play an important role in everyone's daily life, and one of the key technologies leading to significant benefits for mobile applications is machine learning. Optimization of machine learning algorithms for mobile devices is an urgent and important task, it is aimed at developing and applying methods that will effectively use the limited computing resources of mobile devices. The paper discusses various ways to optimize image recognition algorithms on mobile devices, such as quantization and compression of models, optimization of initial calculations. In addition to ways to optimize the machine learning model itself, various libraries and tools for using this technology on mobile devices are also being considered. Each of the described methods has its advantages and disadvantages, and therefore, in the results of the work, it is proposed to use not only a combination of the described options, but also an additional method of parallelization of image processing processes. The article discusses examples of specific tools and frameworks available for optimizing machine learning performance on iOS, and conducted its own experiments to test the effectiveness of various optimization methods. An analysis of the results obtained and a comparison of the performance of the algorithms are also provided. The practical significance of this article is as follows: Improving the performance of machine learning algorithms on iOS mobile devices will lead to more efficient use of computing resources and increase system performance, which is very important in the context of limited computing power and energy resources of mobile devices. Optimization of machine learning performance on the iOS platform contributes to the development of faster and more responsive applications, which will also improve the user experience and allow developers to create new and innovative features and capabilities. Expanding the applicability of machine learning on iOS mobile devices opens up new opportunities for application development in various fields such as pattern recognition, natural language processing, data analysis, and others.

Parallel accumulation-serial fragmentation method for in-depth proteomic analysis of bronchoalveolar lavage fluid collected from patients with nonsmall cell lung cancer.

Advances in mass spectrometry-based quantitative proteomic analysis have successfully demonstrated the in-depth detection of protein biomarkers in bronchoalveolar lavage fluid (BALF) from patients with lung cancers. Recently, ion mobility technology was incorporated into the mass spectrometers escalating the sensitivity and throughput. Utilizing these advantages, herein, we employed the parallel accumulation-serial fragmentation (PASEF) implanted in a timsTOF Pro mass spectrometer to examine the alteration of BALF proteomes in patients with nonsmall cell lung cancers (NSCLCs). BALF proteins were processed from patients with NSCLC and analyzed in a timsTOF Pro mass spectrometer with the PASEF method using a peptide input of 100 ng. Label-free mass spectrometry data were analyzed in the FragPipe platform. We quantitated over 1400 proteins from a single injection of 100 ng of peptides per sample with a median of ∼2000 proteins. We were able to find a few potential biomarker proteins upregulated in NSCLC. The alterations of the BALF proteome landscape vary among patients with NSCLC as previously observed in patients with small-cell lung cancers. The PASEF method has significantly enhanced the sensitivity and throughput, demonstrating its effectiveness in clinical research and application.

Parallel Physics-Informed Neural Networks Method with Regularization Strategies for the Forward-Inverse Problems of the Variable Coefficient Modified KdV Equation

Parallel Physics-Informed Neural Networks Method with Regularization Strategies for the Forward-Inverse Problems of the Variable Coefficient Modified KdV Equation

Robust PRESB Preconditioning of a 3-Dimensional Space-Time Finite Element Method for Parabolic Problems

Abstract We present a recently developed preconditioning of square block matrices (PRESB) to be used within a parallel method to solve linear systems of equations arising from tensor-product discretizations of initial boundary-value problems for parabolic partial differential equations. We consider weak formulations in Bochner–Sobolev spaces and tensor-product finite element approximations for the heat and eddy current equations. The fast diagonalization method is employed to decouple the arising linear system of equations into auxiliary spatial complex-valued linear systems that can be solved concurrently. We prove that the real part of the system matrix is positive definite, which allows us to accelerate the flexible generalized minimal residual method (FGMRES) by the PRESB preconditioner. The action of PRESB on a vector includes two solutions with positive definite matrices. The spectrum of the preconditioned system lies between 1/2 and 1. Finally, we combine the PRESB-FGMRES method with multigrid-CG iterations and illustrate the numerical efficiency and the robustness for spatial discretizations up to 12 millions degrees of freedom.

Fracture characteristic and support effect around deep lined tunnels using CGP-FDEM simulation and field investigation analysis

The lining is one of the main methods of support and is chosen as the primary support for composite and reinforced roadways during mining excavation. Systematic investigations into the supportive effect of lined roadways in deep weak rock masses are highly desirable to improve support design and enhance roadway stability. The fracture characteristics and deformation are more prominent in the lining failure and damage to the surrounding rock. For investigations into the fracture mode and failure characteristics of the lining support, this study proposed a lining element support model in the graphics processing units (GPU) parallel combined finite-discrete element method (FDEM) program using compute unified device architecture (CUDA), referred to as CGP-FDEM. The lining element model and excavation simulation method are implemented in the CGP-FDEM aim of simulation large-scale rock fractures in underground excavations. On this basis, the simulation of deep roadway excavation is performed under various support conditions, including low-strength and high-strength shotcrete, steel arch, and U-profile yieldable steel. Further, a comparative analysis is conducted between the field investigation and simulation results, focusing on fracture-fragmentation and large deformation of the surrounding rock, as well as the failure and fracture mode of the lining elements. Results of individual support effects indicate that the reinforcement conditions mainly affect the excavation damaged zone (EDZ) and the deformation of the surrounding rock. Rock fragmentation and buckling intensify the large deformation and instability of the roadway. Fracture evolution and instability of the surrounding rock can easily occur in weak areas due to severe stress concentration and the accumulation and release of energy. Based on the analysis, the principle of "uniform reinforcement of reactive force throughout the entire section" is proposed to alleviate stress concentrations and maintain the overall stability of the excavation space.

A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction

A parallel feature selection method based on NMI-XGBoost and distance correlation for typhoon trajectory prediction

"At the end of the day, someone done lost their child": A mixed methods analysis of Black families' experiences of the sociopolitical climate.

How Black Americans in the United States (U.S.) make sense of a sociopolitical climate marked by racist imagery, tensions, and police violence is important to understand given the numerously documented detrimental effects of racism-related stress on the well-being of Black parents and children. Informed by Racism-Related Stress Theory, the current study employed a convergent parallel mixed methods design to better understand the ways racism-related stressors in the sociopolitical climate impacted the daily lives and mental health of a sample of Black families with low income. Seventy-eight Black American preadolescents (Mage = 11.0; 43.6% girls) and their parents (79% mothers; 76% living below the U.S. federal poverty level [FPL]) from the southwestern U.S. reported their symptoms of depression and how they had been affected by racial stressors in the sociopolitical climate between Fall 2018 and Summer 2019. A nested sample of 10 parents (80% mothers; 80% living below the FPL) from the quantitative sample also participated in a semi-structured interview. Meta-inferences across methods were drawn pertaining to the influence of child gender on parents' interpretation of effects for children, the toll racism-related stress in the sociopolitical climate takes on Black families, and the transferal of effects on parents to children through parenting and parental depressive symptoms. Findings spotlight the need for policies and family-centered programming that address the racism-related stress faced by many Black youth and their families. Providing families with opportunities and tools that can potentially mitigate harmful effects and foster empowerment could promote positive and lasting change.

Numerical analysis of dump slope stability using material properties obtained by parallel gradation method

The stability of the dump slope depends on many parameters, mainly the shear strength of the dump material. Obtaining strength parameters of dump material is vital in dump stability analysis. Dump material consisting of particles of varying sizes, as large as 1000 mm, is typical. It is challenging to obtain the material strength properties of such large particles in the laboratory. Extrapolation of the material strength properties of the modelled samples prepared by parallel gradation technique is used to calculate the dump material strength properties in this study. A closer agreement is found between the predicted and calculated values of the angle of internal friction. Numerical modelling has been carried out with the obtained material properties using RS2 V9.0, a finite element package, to analyse the stability of a dump slope. It is observed from the results that FOS (factor of safety) increase with an increase in dmax (maximum particle size), keeping the shape of the PSD (particle size distribution) curve constant. The paper develops a slope stability analysis methodology considering particle size's effect on slope stability. The proposed method can be used for accuracy and reliable results for slope stability analysis.

Parallel cooperative multiobjective coevolutionary algorithm for constrained multiobjective optimization problems

The existing parallel multiobjective evolutionary computation does not perform well for constrained multiobjective optimization problems with discontinuous Pareto fronts or narrow feasible regions. This study parallelizes the state-of-the-art cooperative multiobjective coevolutionary algorithm and proposes an effective parallel evolutionary algorithm for constrained multiobjective optimization problems that are difficult to optimize. Two parallelization methods are compared: a global parallel model in which solution evaluations are performed in parallel, and a hybrid model that treats the cooperative populations in a distributed manner while performing each solution evaluation in parallel. The first model is a straightforward parallelization, while the second one capitalizes on the characteristics of the coevolutionary framework. To investigate the efficacy of the proposed models, experiments are conducted on constrained multiobjective optimization problems, including complex characteristics, while varying the number of parallel cores up to 64. The experiments compare the two proposed methods from the viewpoint of search performance and execution time. The experimental results reveal that the latter hybrid model shows better computational efficiency and scalability against an increasing number of cores without adversely affecting the search performance compared to the former straightforward parallelization.