Analysis Model Research Articles

Force Analysis Using Self-Expandable Valve Fluoroscopic Imaging: a way Through Artificial Intelligence.

This study aimed to develop a force analysis model correlating fluoroscopic images of self-expandable valves with stress distribution. For this purpose, a nonmetallic measuring device designed to apply diverse forces at specific positions on a valve stent while simultaneously measuring force magnitude was manufactured, obtaining 465 sets of fluorescent films under different force conditions, resulting in 5580 images and their corresponding force tables. Using the XrayGLM, a mechanical analysis model based on valve fluorescence images was trained. The accuracy of the image force analysis using this model was approximately 70% (50-88.3%), with a relative accuracy of 93.3% (75-100%). This confirms that fluoroscopic images of transcatheter aortic valve replacement (TAVR) valve stents contain a wealth of mechanical information, and machine learning can be used to train models to recognize the relationship between stent images and force distribution, enhancing the understanding of TAVR complications.

Strategi Pengembangan Usaha Pada UMKM Dimsum Bandung (Studi Kasus:Mami Dimsum Bandung)

This research is a call to action, aiming to urgently formulate a business development strategy for Mami Dimsum Bandung MSMEs using SWOT analysis and Business Model Canvas (BMC). Given the crucial role of MSMEs in the Indonesian economy, especially in West Java, this research underlines the pressing need to develop appropriate strategies to improve competitiveness. The method used is descriptive qualitative, with primary data collection through interviews and secondary data from related literature. The results show that Mami Dimsum's main strengths lie in product quality and service, while its main weaknesses are limited capital and production capacity. Significant opportunities are found in the increasing demand for fast food products, while threats come from intense competition and regulatory changes. The proposed strategies include improving operational efficiency, strengthening customer relationships, and diversifying distribution channels. Implementing Business Model Canvas provides a thorough strategic overview of the key elements of the business, assisting Mami Dimsum in mapping out measurable and sustainable development steps

Morphological quality on Day 3 affects the pregnancy outcomes of low-quality euploid blastocysts: a retrospective cohort study.

Does the morphological quality on Day 3 influence the pregnancy outcomes of euploid blastocysts? The morphological quality on Day 3 affects the clinical pregnancy rate (CPR) and live birth rate (LBR) of low-quality euploid blastocysts. The morphological grading of Day 3 embryos affects the pregnancy outcome of cleavage-stage embryos and is an excellent indicator to predict embryo development potential. However, it is still unclear whether morphological quality on Day 3 is associated with pregnancy outcomes of the euploid blastocyst. This retrospective cohort study comprised 1275 patients who received single euploid blastocyst transfer between January 2016 and August 2021 at a tertiary teaching hospital. Patients were grouped into two groups according to the morphological grading on Day 3 of transferred blastocysts: high-quality (HQ, including Grades I and II) Day 3 embryos and low-quality (LQ, Grade III) Day 3 embryos. The primary outcomes were CPR and LBR. Interactions of development days (Day 5 and Day 6) and morphological quality (high- and low-quality) of blastocysts with morphological quality of Day 3 embryos on pregnancy outcomes were tested in the stratified analysis and logistic regression models. The multivariate logistic regression analysis was conducted to investigate the independent effect of the morphological quality of Day 3 embryos on pregnancy outcomes after adjusting for potentially confounding factors. The CPR and LBR of the HQ Day 3 embryos group were statistically higher than those of the LQ Day 3 embryos group (CPR: 59.73% versus 49.70%, respectively, P = 0.015; LBR: 49.73% versus 41.21%, respectively, P = 0.041). The development days of blastocysts did not exhibit a multiplicative interaction with the morphological quality of Day 3 embryos on the CPR (P for interaction = 0.648) and LBR (P for interaction = 0.925). The morphological quality of blastocysts exhibits a multiplicative interaction with the morphological quality of Day 3 embryos on the CPR (P for interaction = 0.020) and LBR (P for interaction = 0.012). After adjusting for potential confounders, the HQ Day 3 embryo group was positively associated with the CPR (adjusted odds ratio (aOR): 2.10, 95% CI: 1.31-3.36, P = 0.002) and LBR (aOR: 1.97, 95% CI: 1.20-3.25, P = 0.008) of LQ blastocysts. However, the morphological quality on Day 3 was not significantly associated with the CPR (aOR: 0.95, 95% CI: 0.58-1.55, P = 0.835) and LBR (aOR: 0.86, 95% CI: 0.53-1.40, P = 0.550) of HQ blastocysts. Selection and confounding bias introduced by the retrospective design cannot be completely eliminated in this study, although multivariable logistic analysis was conducted to adjust for potential confounders. Also, some subgroups had small sample sizes, which may reduce statistical power. Moreover, participants in our study only received single euploid blastocyst transfer, and whether the results could apply to blastocysts with unknown ploidy status is unclear. This study found that the morphological quality on Day 3 was significantly associated with the CPR and LBR of LQ blastocysts; Therefore, when only LQ euploid blastocysts are available for transfer, blastocysts derived from HQ Day 3 embryos are recommended. No external funding was obtained. The authors have no conflicts of interest to declare. N/A.

Optimalisasi Pengembangan Bisnis pada Petani Ikan Ciparay

An essential strategic effort to improve community welfare and the productivity of the fishing industry is to optimize the development of fish farmer businesses in Ciparay, Bandung Regency, West Java, which has a lot of potential for cultivating freshwater fish. Fish farmers in this area, however, still face several problems. These include lack of access to advanced technology, knowledge about managing a business, and market limitations. This research aims to find and create the most effective strategy for building a fish farming business in Ciparay. This research uses SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) and Business Model Canvas to find internal and external elements influencing the fish farming business. Apart from field observations and in-depth interviews with fish farmers, data was collected through relevant literature studies. The results of the analysis show that traditional knowledge and extensive experience in fish cultivation are the main strengths of Ciparay fish farmers. In contrast, the main weakness is the difficulty of obtaining technology and capital. The ever-increasing market demand for fresh fish and government support through various aid programs are opportunities that can be exploited. However, there are threats such as climate change and competition with fish products from other countries. The results show that there are several methods that Ciparay fish farmers can use to maximize their business growth. First, increasing product diversification and innovation in fish processing can open up new market opportunities and increase product-added value. Second, increasing access to modern technology through training and technical assistance can help improve product quality and productivity. Third, forming farmer groups or cooperatives can strengthen farmers' bargaining positions and facilitate their access to capital and wider markets. Farmers can gain greater access to markets and capital by implementing stratification strategies

Assessing competences, needs, and satisfaction with health care in transition in rheumatology: Development and Validation of the Transition-KompAZ.

The aim of this study was to develop and psychometrically validate a self-report instrument to assess (1) competences, (2) needs, and (3) satisfaction among youth transitioning from pediatric to adult rheumatology. The Transition-KompAZ was developed in several steps with conceptual and psychometric analyses. The instrument was administered to adolescents and young adults (AYA, 16-25 years) with inflammatory rheumatic diseases before (group 1) or after (group 2) transfer to adult rheumatology to test its psychometric properties. A two-factor higher-order confirmatory factor analysis (CFA) model was applied to test the hypothesized factor structure. Internal consistency was estimated using the approach of Raykov with the factor loadings and error variances estimated in the CFA. Spearman rank correlation coefficients were used to assess construct validity. The Transition-KompAZ includes the following modules: (1) competences in transition [knowledge; self-management], (2) needs [health care services, information], and (3) satisfaction [general; transitional care]. A total of 173 AYA (group 1: n=86, group 2: n=87) from 12 rheumatology sites completed the Transition-KompAZ. It showed good model fit (Comparative Fit Index >0.9, Tucker-Lewis Index >0.9; Weighted Root Mean Square Residual <0.9) with good internal consistency. The instrument demonstrated moderate to good construct validity, and good test-retest reliability. The Transition-KompAZ appears to be a reliable tool for assessing important dimensions of transition. It may support a structured and individualized transition and the evaluation of transition services. However, further studies are required to assess its predictive value in terms of transfer readiness and successful transition.

A Study on the Steady and Dynamic Performance of Dry Gas Seals with Combined Superelliptical Grooves and Holes on the End Face

Background:: Enhancing the stability and leakage control of Dry Gas Seals (DGS) under high parameters has been a crucial research focus. The design of end-face groove structures and surface texture shapes has been an essential aspect of DGS studies aimed at improving performance. Objective:: The proposed end-face gas seal utilizes superelliptical grooves and holes to improve its performance, aiming to obtain a patent. The use of superelliptical curves allows for a more precise and efficient geometric representation, resulting in a better sealing effect. Methods:: A theoretical analysis model for the steady and dynamic behavior of the seal is established using the lubrication theory and perturbation methods. The study investigates the distribution patterns of gas film pressure on the sealing end-face and gas flow characteristics within the film. This approach provides a new perspective for understanding seal performance and offers a theoretical basis for optimizing seal design. Results:: The results indicate that the combined end-face structure with grooves and holes ensures good sealing stability and effectively enhances leakage control performance. By optimizing the design of the superelliptical groove and holes on the end face, the performance of DGS can be significantly improved. Conclusion:: Within the parameter range studied, better steady-state performance is achieved for θ=40~80°, v=1.3~1.4, u=1~2, β=0.6~0.7, and λ=1.0~1.5. In addition, better dynamic performance is observed for θ=80~120°, v=1.1~1.2, u=2~3, β=0.9~1.0, and λ=2.0~2.5.

Analisis Lingkungan dan Kemampuan Kerja Terhadap Kinerja Pegawai dan Dampaknya Pada Kepuasan Kerja Pegawai (Studi Pada UPTD Puskesmas Muara Komam)

This study aims to analyze the influence of work environment and workability on employee performance and its impact on employee job satisfaction at UPTD Muara Komam Health Center. The background of this study is based on the importance of employee performance in improving the quality of health services, as well as various problems related to suboptimal work environments and workability conditions. This study integrates the variables of work environment, workability, performance, and job satisfaction in one analysis model to provide a more comprehensive picture of the factors that influence the performance of Puskesmas employees. The research sample consisted of 63 UPTD Muara Komam Health Center employees who were selected randomly. Data collection was carried out through a questionnaire tested for validity and reliability. The data analysis method used was the Structural Equation Model (SEM) with SmartPLS software. The results showed that work environment and workability significantly affected employee performance. Job satisfaction was proven to mediate the relationship between work environment and workability on employee performance. A conducive work environment and good work ability directly improve employee performance and high job satisfaction, further strengthening this relationship. This study provides an important contribution to the development of human resource management policies in the health sector, especially in efforts to improve the performance and job satisfaction of Puskesmas employees. The managerial implications of this study include the need for good work environment management, such as providing adequate facilities and creating harmonious working relationships, as well as improving employee work skills through ongoing training and development programs

Application and Optimization of Decision Tree ID3 Algorithm in Innovation and Entrepreneurship Analysis Model

In recent years, with the rise of innovation and entrepreneurship, the decision tree ID3 algorithm is more and more widely used in innovation and entrepreneurship analysis models. The decision tree ID3 algorithm is an effective data mining technology, which can mine useful information from a large number of data and provide a scientific basis for innovation and entrepreneurship. The traditional ID3 algorithm has obvious convergence characteristics, which limits its application in innovation and entrepreneurship analysis models. Therefore, it is important to optimize the ID3 algorithm to improve its computational efficiency and accuracy. From the perspective of the innovation and entrepreneurship analysis model, the application of the decision tree ID3 algorithm in innovation and entrepreneurship is studied. The basic principle, advantages, and disadvantages of the ID3 algorithm are analyzed, and a method to optimize the ID3 algorithm is proposed. Through the experimental verification, the application effect of the optimized ID3 algorithm in the analysis model of innovation and entrepreneurship is proved. When the number of records is 5200, the error rate of the optimization algorithm is 70%, while the error probability of the classical algorithm is 95%. When the number of records is 7200, the error rate of the optimized calculation method is 130%, while the error rate of the classic ID3 calculation method is 170%. As the number of records increases, the error rates of both optimization algorithms and classical algorithms will increase. As the number of records increases, the error rate of the optimized ID3 algorithm increases less than that of the classical algorithm. The optimized ID3 algorithm can significantly improve computational efficiency and accuracy and provide an effective data mining technology for innovation and entrepreneurship analysis models.

A Study of Nonverbal Persuasion in TED Talks

This study investigates the realm of nonverbal communication and its persuasive impact in the context of TED talks. Nonverbal communication plays a significant role in effectively conveying persuasive messages, especially in TED talks, which are considered a useful database for the study. Surprisingly, little researches and studies have investigated the role of nonverbal behavior in persuading and influencing others. The study aims to find the most common body movements in TED talks, analyze the common movements kinetically to have a better understanding of these movements, and investigate the nonverbal persuasiveness of the selected movements. The study adopts a model consisting of Birdwhistell's (1952) kinesic analysis model and Andre et al.'s (2011) nonverbal persuasion analysis model. Final results reveal that (1) hand movement is the most common type among other types of movements, (2) the persuasiveness of nonverbal communication is achieved by basing on the following persuasive body behaviors: importance, emotion, certainty, the sender’s competence, evaluation, and the sender’s benevolence, (3) body language, such as hand gestures, head nods, and facial expressions, can be broken down into smaller parts, these smaller parts, known as the kinemorph, kineme, and kine, make the body movements understandable.

A Review of Traffic Flow Prediction Models in 5G Using Machine Learning Techniques

Abstract: Wireless technology has advanced significantly since its origin and is now an essential aspect of our daily life. The advancement of wireless communication from the first generation (1G) to the fifth generation (5G) of technology has been revolutionary. An extensive summary of the development of wireless communication from 1G to 5G has been given in this paper. Cellular networks are heading towards becoming more diverse, broadband, integrated, and intelligent networks with the introduction of 5G networks. The goals of 5G wireless technology are to provide more users with more consistent user experiences, ultralow latency, vast network capacity, faster multi-Gbps peak data speeds and increased reliability. While the resources needed for computation and communication are also growing with the maturity of 5G technology .At the same time, cellular traffic has increased dramatically due to the widespread use of smart devices. Cellular traffic prediction is a crucial component of the resource management system for cellular networks but it confronts many difficulties due to strict standards for accuracy and dependability. Among the most important issues is how to enhance the predictive performance of Mobile data traffic. This review describes the need of traffic forecasting in cellular network in 5G technology. A study of different models for network analysis and traffic prediction by different researchers is presented in this paper. The distinctiveness and guidelines of earlier research for traffic prediction in 5G are examined. To determine the distinctive qualities of each method used for traffic prediction in mobile network, a thorough analysis of the most popular techniques using Machine learning for predictive analysis are discussed.

Development of an IMU based 2-segment foot model for an applicable medical gait analysis

BackgroundThe two most commonly instrumented gait analysis tools used are Optical Motion Capture systems (OMC) and Inertial Measurement Units (IMU). To date, OMC based gait analysis is considered the gold-standard. Still, it is space-, cost-, and time-intense. On the other hand IMU systems are more cost- and time effective but simulate the whole foot as a single segment. To get a more detailed model of the foot and ankle, a new 2-segment foot model using IMU was developed, comparable to the multi-segment foot models assessed by OMC.Research questionCan an IMU based 2-segment foot model be developed to provide a more detailed representation of the foot and ankle kinematics?MethodsTo establish a 2-segment foot model, in addition to the previous 1-segment foot model an IMU sensor was added to the calcaneus. This allowed the differentiation between the hindfoot and forefoot kinematics. 30 healthy individuals (mean age 27 ± 7 years) were recruited to create a norm data set of a healthy cohort. Moreover, the kinematic data of the 2-segment foot model were compared to those of the traditional 1-segment foot model using statistical parametric mapping.ResultsThe 2-segment foot model proved to be applicable. Furthermore, it allowed for a more detailed representation of the foot and ankle joints, similar to other multi-segment foot model. The healthy cohort’s norm data set showed a homogeneous motion pattern for gait.ConclusionThe 2-segment foot model allows for an extension of IMU-based gait analysis. Futures studies must prove the reliability and validity of the 2-segment foot model in healthy and pathologic situations.Level of evidenceLevel II.

Reliability analysis of deep tunnels in spatially varying brittle rocks using interval and random field modelling

Rock properties are estimated using objective lab/in-situ testing and subjective judgements invoking different types of uncertainties, i.e., aleatory, and epistemic, along them, often indicated by varying information levels. This study presents a unified reliability method to integrate the spatial variability of inputs modelled via alternate uncertainty models (intervals and probability-boxes (p-boxes)) with those modelled as stochastic variables via probability distributions. The methodology employs advanced Karhunen–Loève decomposition to generate interval and random fields of inputs modelled via alternate and stochastic models, respectively. Input properties are allocated to the zones of the numerical model in Fast Lagrangian Analysis of Continua-2D (FLAC-2D) based on their spatial dependency and correlation functions through a developed MATLAB-FLAC coupled code. The methodology is demonstrated for a deep tunnel in Canada to be constructed along a massive rock prone to brittle failures. Intact rock properties are modelled as stochastic variables due to objective estimation, while Geological Strength Index (GSI) and deformation modulus are modelled using alternate models (interval and p-box, respectively) due to subjective and hybrid estimation (double uncertainty propagation algorithm). The results of the methodology are compared with those of traditional deterministic and random field methods. The methodology reduces the subjectivity invoked by including unavailable additional information (e.g., assuming probability distributions of inputs based on literature) and propagates the originally available information of inputs accurately. The final outputs are the p-boxes of response parameters (i.e., displacements and damage zone) instead of their fixed values (deterministic analysis) and probability distributions (traditional reliability analysis), indicating the propagation of both impreciseness and variability of inputs by the method. For this case study, the p-boxes of outputs were bounding their values/distributions estimated via traditional analyses, verifying the accuracy of the methodology. Further, the impreciseness in the outputs, highest in the damage zone extent, was due to imprecision in the estimated GSI.

Genome analysis through image processing with deep learning models.

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

Time Series Analysis for Air Pollution Prediction in High-Intensity Development Areas using Deep Learning

Objective: The purpose of this study is to design novel approach to predict air pollution indices accurately using hybrid approach in highly populated cities of the world. Methods: This study aimed to address the significant bottleneck of relying on a single model for time series analysis which we accomplished by developing a hybrid forecasting method. This blended method combines the advantages of CNN (Convolutional Neural Networks) and LSTM (Long Short Term Memory) hybrid model together, overcoming insufficiency of air pollution prediction. Findings: Due to the empirical operation that revealed in achieving proper predictions we have come to understand that relying on a single model for the estimation of time-series data results is inefficient and misleading, mostly due to the non-linear nature of raw data. On the other hand, by using this hybrid model forecasting, we can uncover more detailed patterns through longer period of time and predict them more accurate by using weather and air pollution related data.The hybrid CNN-LSTM Model achieved least values of 6.742 for MAE,12.921 for RMSE compared to other approaches, which indicatesmere perfection. Novelty: The novelty of our approach is to fuse LSTM and CNN models in order to perform exhaustive analysis on the dynamics of the air pollution throughout the entirety of the city. Through the combination of these two models, such a tool is made simpler to use, and there is an improved accuracy in predictions. This thus becomes a promising tool in the fight against air pollution in cities that have a very high population just like Delhi. Keywords: ­ Air Pollution, LSTM, CNN, Deep Learning, Time Series Analysis, Nonlinear Relationships

Comparison of the coexistence pattern of mangrove macrobenthos between natural and artificial reforestation.

The abandoned pond-to-mangrove restoration project provides greater advantages than tidal flats afforestation in restoring mangrove ecosystem services and will be the primary method for mangrove restoration in the future. The existing methods for abandoned pond-to-mangrove restoration include artificial restoration through 'dike-breaking, filling with imported soil and tree planting' and natural restoration through 'dike-breaking and natural succession'. However, little is known about which restoration strategy (natural or artificial restoration) provides more benefits to the biodiversity of mangrove macrobethos. Given a prevailing view suggested that artificial restoration should be the preferred approach for accelerating recovery of biodiversity and vegetation structure in tropical regions, we hypothesised higher macrobenthic biodiversity and more complex community structure in artificial restoration than in natural restoration. To test this hypothesis, macrobenthic biodiversity and ecological processes were monitored in a typical abandoned pond-to-mangrove area of Dongzhaigang Bay, China, where artificial and natural restoration methods were used concurrently. Differences in macrobenthic biodiversity, community structure and ecological processes were compared using diversity indices, complex network analysis and null models. Similar species composition and ecological niche overlap and width among macrobenthos were observed at artificial and natural restoration sites. The biotic heterogeneity and interaction among macrobenthos were higher at the natural restoration sites than at the artificial restoration sites. Macrobenthos community assembly at natural and artificial restoration sites was both determined by deterministic processes, with environmental filtering dominating, which explained 52% and 54% of the variations in macrobenthic community structures respectively. Although our findings did not validate the research hypothesis, higher biotic heterogeneity and species interaction among macrobenthos could support natural restoration as the primary method for abandoned pond-to-mangrove projects, because it is a nature-based solution for mangrove restoration.

Synergistic evaluation of energy security and environmental sustainability in BRICS geo-political entities: An integrated index framework

Research background: The increasing demand for energy, driven by economic growth and population expansion, is a critical driver of societal progress. However, the predominant reliance on fossil fuels to meet this demand presents significant challenges, particularly in the rapidly developing BRICS nations (Brazil, Russia, India, China, and South Africa). These countries are faced with a complex interplay of energy security and environmental sustainability issues, stemming from their substantial fossil fuel reserves and the associated environmental consequences. The challenges manifest as inequalities in access to clean energy, environmental degradation, and heightened vulnerability to the impacts of climate change. Addressing these multifaceted issues requires a comprehensive approach. Metrics-based strategies, which employ aggregated indices derived from a diverse set of energy and environmental indicators, have the potential to provide valuable insights into these complexities. However, the development of a universally applicable energy sustainability index is complicated by the heterogeneity of metrics, disparities between countries, and methodological challenges, emphasizing the need for an innovative and holistic analytical framework. Purpose of the article: This study aims to develop a tailored Energy Security and Environmental Sustainability Index for BRICS economies to evaluate the robustness of their energy systems and the viability of their ecological practices. The index serves as an instrument to assess the progress of these nations in the Energy and Environment domain and identify areas that require targeted interventions and improvements. Methods: The construction of the composite ESESI involves the selection of relevant parameters and the application of a Multi-Criteria Decision Analysis (MCDA) framework in conjunction with the Weighted Product Method (WPM). To ensure objectivity in the determination of optimal and least favorable weights for each indicator, the study employs the Multiplicative Data Envelopment Analysis (MDEA) model. Findings &amp; value added: The ESESI analysis reveals disparities in the progress made by BRICS nations in enhancing energy security, promoting renewable energy deployment, and mitigating environmental impacts. While some countries demonstrate substantial advancements, others face challenges in improving energy efficiency and reducing carbon emissions. The study underscores the necessity for tailored policies and targeted infrastructural enhancements that align with the unique challenges and strengths of each nation. Harnessing the abundant renewable energy potential through advanced energy trade mechanisms and fostering cross-border investments are identified as crucial strategies for ensuring environmental sustainability and long-term energy in the BRICS region. The ESESI provides a valuable tool for policymakers and researchers to evaluate the progress of BRICS nations in achieving sustainable energy goals and to inform evidence-based decision-making processes. By offering a comprehensive and scientifically rigorous assessment framework, this study contributes to the ongoing discourse on sustainable energy transitions and environmental stewardship in the context of rapidly developing economies.

Community education training to optimize the use of artemisinin-based combination therapy in Kamuli District, Uganda

BackgroundCommunity health education improves members health-seeking and utilization behaviours. To enhance the community knowledge and optimize the use of Artemisinin-based combination therapy (ACT), we carried out a community training in Kamuli District, Uganda.MethodsThe Analysis, Design, Development, Implementation and Evaluation (ADDIE) model was adopted. A total of 3420 community members were trained, 384 sampled to participate in pre-post-test assessment, with 76 healthcare workers (HCW). Community members were sampled by simple random sampling while the HCW were purposively selected. Community trainings occurred for two days at each of 42 public health facilities and one day at 27 parishes. A paired sample t-test and effect size was computed to establish effect with statistical significance tested at p < 0.05.ResultsOverall, a total of 3496 participants, majority 2705 (77.4%) females were trained. A total of 3420 community members, majority 2659 (77.7%) females trained, and 76 HCW, majority 46 (60.5%) females trained. The median age of community participants was 32 years, and interquartile range (IQR) = 17 years. The median age of HCW was 32 years, and IQR = 8 years. The training had a positive and significant effect on the community members knowledge: malaria transmission (T-test = 9.359; p < 0.0001) causes of malaria (T-test = 6.738; p < 0.0001), malaria symptoms (T-test = 5.403; p < 0.0001), dangerous malaria species (T-test = 12.088; p < 0.0001), Plasmodium vivax malaria cycle and occurrence every 48 h (T-test = 7.470; p < 0.0001), assessing whether a patient with malaria may suffer from jaundice (T-test = 7.228; p < 0.0001), organs affected by Plasmodium falciparum (T-test = 12.214; p < 0.0001), malaria diagnosis (T-test = 9.765; p < 0.0001), Plasmodium associated with malaria relapse (T-test = 10.250; p < 0.0001), and malaria prevention and control (T-test = 9.278; p < 0.0001). The intervention also had a significant and positive effect on HCW knowledge on all domains except on malaria transmission (T-test = 1.217; p = 0.228) where it didn’t have any statistically significant increase on their knowledge.ConclusionThe education intervention improved the knowledge of participants significantly. There is need to adopt and scale-up the current intervention at all levels of care to enhance proper use of medicines.

Association between oxidative balance score and diabetic kidney disease, low estimated glomerular filtration rate and albuminuria in type 2 diabetes mellitus patients: a cross-sectional study

ObjectiveThe oxidative balance score (OBS) is a comprehensive concept that includes 20 oxidative stressors and can be used to assess individual pro-oxidant versus antioxidant exposure, and the aim of the present study was to investigate the association between OBS and the risk of diabetic kidney disease (DKD), low estimated glomerular filtration rate (low-eGFR) and albuminuria in patients with diabetes mellitus (DM).MethodsThis cross-sectional study included nationally representative consecutive National Health and Nutrition Examination Survey DM patients aged 18 years and older from 2003-2018. The continuous variable OBS was converted into categorical variables by quartiles, and weighted multiple logistic regression analyses and restricted triple spline models were used to explore the relationships. We also performed subgroup analyses and interaction tests to verify the stability of the results.ResultsA total of 5389 participants were included, representing 23.6 million non-institutionalized US residents. The results from both multivariate logistic regression analysis and restricted cubic spline models indicated that OBS and dietary OBS levels were negatively associated with the risk of DKD, low-eGFR, and albuminuria, without finding a significant correlation between lifestyle OBS and these clinical outcomes. Compared to the lowest OBS quartile group, the prevalence risk of DKD (OR = 0.61, 95% CI: 0.46-0.80), low-eGFR (OR = 0.46, 95% CI: 0.33-0.64) and albuminuria (OR = 0.68, 95% CI: 0.51-0.92) decreased by 39%, 54% and 32%, respectively, in the highest OBS quartile group. The results remained stable in subgroup analyses and no interaction between subgroups was found.ConclusionHigher levels of OBS and dietary OBS were associated with a lower risk of DKD, low-eGFR, and albuminuria. These findings provided preliminary evidence for the importance of adhering to an antioxidant-rich diet and lifestyle among individuals with diabetes.

Psychological resilience, athletic experience, and competitive level of judokas. A transversal study

IntroductionWhile there is agreement on the positive link between psychological resilience and athletic performance, conclusive findings regarding the association between psychological resilience and other variables of interest (for example, age, gender, type of sport, or competitive level) remain elusive.ObjectiveThe study aimed to assess psychological resilience levels among judokas and explore potential associations with demographic factors, judo experience and competitive level.MethodsA total of 702 judokas (469 men and 233 women) participated in the study, of whom 194 (27.6%) were classified as TOP by their competitive level. Psychological resilience was evaluated using the Spanish version of the 10 item Connor-Davidson Resilience Scale (CD-RISC 10), with a score range from 0 to 40. Independent T-test and Pearson’s coefficient were used for bivariate analysis. A two-way non-parametric ANCOVA was carried out to analyse the impact of gender and competitive level on psychological resilience.ResultsThe judokas showed total mean scores in the CD-RISC 10 of 33.08 points (SD = 4.79), considered high. Levels of psychological resilience were significantly higher among men (33.36 ± 4.76) than women (32.53 ± 4.80) and were positively correlated with age and number of years practicing and competing in Judo (p = 0.019). Judokas with a higher competitive level (TOP judokas) showed significantly higher levels of resilience than the others (non-TOP judokas) (p &amp;lt; 0.001). These differences in resilience according to competitive level persisted, among both men and women, when adjusting the model of analysis (two-way ANCOVA) for all variables considered in the study, although with a small effect size.ConclusionThe results suggest that the practice of Judo, especially over long periods of time, is associated with high scores in psychological resilience. Furthermore, psychological resilience appears to be a differentiating variable among judokas at a high-competitive level, and its evaluation and development using different strategies based on age and gender should be considered by trainers and psychologists.

Health data management practice and associated factors among health professionals working in public health facilities in Oromia Special Zone, Amhara, Ethiopia: a cross-sectional study

ObjectiveData and information are vital to the decision-making process surrounding health sector reform. In spite of the vast amounts of capital invested in the development of health information systems, health...